loss aversion

Thinking, Fast and Slow
by Daniel Kahneman
Published 24 Oct 2011

The outside view and the risk policy are remedies against two distinct biases that affect many decisions: the exaggerated optimism of the planning fallacy and the exaggerated caution induced by loss aversion. The two biases oppose each other. Exaggerated optimism protects individuals and organizations from the paralyzing effects of loss aversion; loss aversion protects them from the follies of overconfident optimism. The upshot is rather comfortable for the d ecision maker. Optimists believe that the decisions they make are more prudent than they really are, and loss-averse decision makers correctly reject marginal propositions that they might otherwise accept. There is no guarantee, of course, that the biases cancel out in every situation.

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Every Nobel laureate receives an individual certificate with a personalized drawing, which is presumably chosen by the committee. My illustration was a stylized rendition of figure 10. “loss aversion ratio”: The loss aversion ratio is often found to be in the range of 1. 5 and 2.5: Nathan Novemsky and Daniel Kahneman, “The Boundaries of Loss Aversion,” Journal of Marketing Research 42 (2005): 119–28. emotional reaction to losses: Peter Sokol-Hessner et al., “Thinking Like a Trader Selectively Reduces Individuals’ Loss Aversion,” PNAS 106 (2009): 5035–40. Rabin’s theorem: For several consecutive years, I gave a guest lecture in the introductory finance class of my colleague Burton Malkiel.

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Novemsky and Kahneman, “The Boundaries of Loss Aversion.” Ian Bateman et al., “Testing Competing Models of Loss Aversion: An Adversarial Collaboration,” Journal of Public Economics 89 (2005): 1561–80. 28: Bad Events heartbeat accelerated: Paul J. Whalen et al., “Human Amygdala Responsivity to Masked Fearful Eye Whites,” Science 306 (2004): 2061. Individuals with focal lesions of the amygdala showed little or no loss aversion in their risky choices: Benedetto De Martino, Colin F. Camerer, and Ralph Adolphs, “Amygdala Damage Eliminates Monetary Loss Aversion,” PNAS 107 (2010): 3788–92. bypassing the visual cortex: Joseph LeDoux, The Emotional Brain: The Mysterious Underpinnings of Emotional Life (New York: Touchstone, 1996).

Irrationally Yours: On Missing Socks, Pickup Lines, and Other Existential Puzzles
by Dan Ariely and William Haefeli
Published 18 May 2015

The basic principle behind this emotional reaction to the elimination of these movies is loss aversion. Loss aversion is one of the most basic and well-understood principles in social science. The basic finding is that losing something has a stronger emotional impact than gaining something of the same value. Going back to Netflix, the implication is that having movies taken away from your account is perceived as a loss and because of that, it feels much more painful. The impact of loss aversion could be so strong that losing the not-so-great movies can still be more upsetting than the joy of getting movies that are objectively better. One other implication of loss aversion is that while old Netflix users, such as yourself, will view the new collection of movies on Netflix in a somewhat negative and loss-aversive way, new users who just see the new set of movies without the experience of having anything taken away from them will view the updated offering in a much more positive way.

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—YORAM When I was much younger I got to spend some time on a farm, where I heard farmers saying that they were going to hire a bull to “service” their cows. Maybe this is the answer to your question? Workplace, Language, Misery ON LOSS AVERSION AND SPORTS Dear Dan, You have mentioned many times the principle of loss aversion, where the pain of losing is much higher than the joy of winning. The recent World Cup was most likely the largest spectator event in the history of the world and fans from across the globe were clearly very involved. If indeed, as suggested by loss aversion, people suffer more from losing than they are elated by winning, why would anyone become a fan of a team? After all, as fans we have about an equal chance of losing (which you claim is very painful) and of winning (which you claim does not provide the same extreme emotional impact).

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After all, as fans we have about an equal chance of losing (which you claim is very painful) and of winning (which you claim does not provide the same extreme emotional impact). So in total, across many games, the outcome for fans is not a good deal. Am I missing something in my application of loss aversion? Is loss aversion not relevant to sports? —FERNANDO This description of “fan-ness” implies that people have a choice in the matter, and that they carefully consider the benefits versus the costs of becoming a fan of a particular team. Personally, I suspect that the choice of what team we root for is closer to religious convictions than to rational choice, which means that we don’t really make an active choice when choosing a team (at least not a deliberate, informed one) and that we are “given” our team affiliation by our surroundings, family, and friends.

Dollars and Sense: How We Misthink Money and How to Spend Smarter
by Dr. Dan Ariely and Jeff Kreisler
Published 7 Nov 2017

IT’S IN THE WAY THAT YOU LOSE IT The endowment effect is deeply connected to LOSS AVERSION. The principle of loss aversion, first proposed by Daniel Kahneman and Amos Tversky,6 holds that we value gains and losses differently. We feel the pain of losses more strongly than we do the same magnitude of pleasure. And it’s not just a small difference—it’s about twice as much. In other words, we feel the pain of losing $10 about twice as strongly as we do the pleasure of winning $10. Or, if we tried to make the emotional impact the same, it would take winning $20 to counteract the feeling of losing $10. Loss aversion works hand in hand with the endowment effect.

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Owners of an item, like the Bradleys with their home, value the potential loss of ownership much more than nonowners value the potential gain of the same item. This gap—fueled by loss aversion—gets us into all kinds of financial mistakes. We saw loss aversion at work when the Bradleys referenced the rising and falling real estate market. They thought about the price of their home in terms of its highest point, years ago, before the market slowed down. They thought about what they could have sold it for back then. They focused on the loss relative to the price during that previous historical moment. Retirement savings and investments are other areas where loss aversion and endowment effect can wreak havoc on our ability to see the world in an objective way.

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The statement might say, “We prefunded the account with $500, you contributed $100, and the company took back $400.” That made the loss very clear. It also triggered loss aversion in participants, who quickly began maximizing their 401(k) contributions. Once we understand loss aversion and that many things can be framed as either gains or losses—and that the loss framework is more motivating—maybe we can reframe choices, such as how much to contribute to retirement savings, in a way that will persuade us to act in ways that are more consistent with our long-term well-being. Speaking of long-term well-being, loss aversion also clouds our ability to gauge long-term risks. This problem specifically impacts investment planning.

More Than You Know: Finding Financial Wisdom in Unconventional Places (Updated and Expanded)
by Michael J. Mauboussin
Published 1 Jan 2006

And since humans have a hard time relating to all but the easiest probabilities, we often fail to see the significance of streaks. 8 Time Is on My Side Myopic Loss Aversion and Portfolio Turnover The attractiveness of the risky asset depends on the time horizon of the investor. An investor who is prepared to wait a long time before evaluating the outcome of the investment as a gain or a loss will find the risky asset more attractive than another investor who expects to evaluate the outcome soon. —Richard H. Thaler, Amos Tversky, Daniel Kahneman, and Alan Schwartz, “The Effect of Myopia and Loss Aversion on Risk Taking: An Experimental Test” Loss aversion . . . can be considered a fact of life. In contrast, the frequency of evaluations is a policy choice that presumably could be altered, at least in principle.

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This response prompted Samuelson to prove a theorem showing that “no sequence is acceptable if each of its single plays is not acceptable.” According to economic theory, his learned colleague’s answer was irrational.1 Even though the lunch bet has a positive expected value, Samuelson’s proof doesn’t feel quite right to most people. The concept of loss aversion explains why. One of prospect theory’s main findings, loss aversion says that given a choice between risky outcomes we are about two times as averse to losses than to comparable gains.2 So Samuelson’s theoretical proof notwithstanding, most people intuitively agree with his lunch partner: The prospective regret of losing $100 on a single toss exceeds the pleasure of winning $200.

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From 1900 through 2006, stocks in the United States have earned a 5.7 percent annual premium over treasury bills (geometric returns). Other developed countries around the world have seen similar results.4 In a trailblazing 1995 paper, Shlomo Benartzi and Richard Thaler suggested a solution to the equity risk premium puzzle based on what they called “myopic loss aversion.” Their argument rests on two conceptual pillars:5 1. Loss aversion. We regret losses two to two and a half times more than similar-sized gains. Since the stock price is generally the frame of reference, the probability of loss or gain is important. Naturally, the longer the holding period in a financial market the higher the probability of a positive return.

Misbehaving: The Making of Behavioral Economics
by Richard H. Thaler
Published 10 May 2015

I just have to get a few stragglers to finish their new papers and complete the introduction.” The book came out, shortly after the last paper arrived and the introduction was finished, in 2000, almost four years later. The “timid choices” part of the Kahneman and Lovallo story is based on loss aversion. Each manager is loss averse regarding any outcomes that will be attributed to him. In an organizational setting, the natural feeling of loss aversion can be exacerbated by the system of rewards and punishment. In many companies, creating a large gain will lead to modest rewards, while creating an equal-sized loss will get you fired. Under those terms, even a manager who starts out risk neutral, willing to take any bet that will make money on average, will become highly risk averse.

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Again we see that losses are roughly twice as painful as gains are pleasurable, a finding that has been replicated numerous times over the years. The endowment effect experiments show that people have a tendency to stick with what they have, at least in part because of loss aversion. Once I have that mug, I think of it as mine. Giving it up would be a loss. And the endowment effect can kick in very fast. In our experiments, the subjects had “owned” that mug for a few minutes before the trading started. Danny liked to call this the “instant endowment effect.” And while loss aversion is certainly part of the explanation for our findings, there is a related phenomenon: inertia. In physics, an object in a state of rest stays that way, unless something happens.

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After the new regulation, investors were shown returns for the past year instead. As predicted by myopic loss aversion, after the change investors shifted more of their assets into stocks. They also traded less often, and were less prone to shifting money into funds with high recent returns. Altogether this was a highly sensible regulation. These experiments demonstrate that looking at the returns on your portfolio more often can make you less willing to take risk. In our “myopic loss aversion” paper, Benartzi and I used prospect theory and mental accounting to try to explain the equity premium puzzle.

No Slack: The Financial Lives of Low-Income Americans
by Michael S. Barr
Published 20 Mar 2012

In addition, individuals may be loss averse to the point that they will overwithhold their taxes to avoid having to write a check to the IRS. We defer a detailed discussion of these competing explanations to the next two sections. 12864-10_CH10_3rdPgs.indd 223 3/23/12 11:57 AM 224 michael s. barr and jane k. dokko Similarly, for loss aversion to color the interpretation of the relationship between portfolio allocation and wanting to overwithhold, the groups that are most likely to want overwithholding must also be the most loss averse. While the study provides no direct measure of loss aversion, the tax filers who are more likely to owe tax liability are identifiable by the size of their refunds.

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While the study provides no direct measure of loss aversion, the tax filers who are more likely to owe tax liability are identifiable by the size of their refunds. If loss aversion influences tax filers to want to overwithhold, then loss aversion may be manifested more strongly among those with a higher probability of writing a check to the IRS. This group may then express a stronger preference for overwithholding. By studying the behavior of this subsample of tax filers, the importance of loss aversion may be inferred. If, in addition, the relationship between portfolio allocation and wanting to overwithhold remains the same among the tax filers most likely to owe tax liability (as among all filers), then heterogeneity in loss aversion most likely does not explain tax filers’ preference for overwithholding.

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That is, this finding suggests an inverse relationship between the likelihood of owing taxes at the time of filing and a preference for overwithholding, which is the opposite of what a model of loss aversion predicts. Furthermore, those with mainly illiquid assets and those with one liquid asset are shown to be the groups most likely to want excess withholding. They are 18 and 13 percentage points, respectively, more likely to want to overwithhold than the group with no assets. These point estimates are well within a standard error of what table 10-4 shows, so the likelihood of owing taxes at the time of filing, and thus loss aversion, does not affect the interpretation of tax filers’ preference for overwithholding.18 Loss aversion can be an important motivation for some households to prefer overwithholding.

The Journey of Humanity: The Origins of Wealth and Inequality
by Oded Galor
Published 22 Mar 2022

Regions of Planet Earth that resemble Volatilia, therefore, can be expected to contain a lower proportion of the loss-averse type, while regions that resemble Uniformia can be projected to have a greater proportion.[38] Experimental evidence, as well as polls conducted by the European Social Survey (2002–14), the World Values Survey (1981–2014) and the General Social Survey (1972–2018), provides estimates of the variation in the degree of loss aversion within and between countries. When combined with climate data from the past 1,500 years, and accounting for possible confounding factors from geography, culture and history, the evidence suggests that volatile climatic conditions have indeed contributed to the emergence of cultures with relatively low levels of loss aversion, while regions where climatic fluctuations are relatively uniform have contributed to more loss-averse cultures.[39] Again, of course, this association between climatic volatility and loss aversion may reflect the fact that loss-averse individuals and societies are more likely to settle in less volatile environments.

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The fact that these second-generation migrants are affected by their ancestral geographic environments suggests that these attitudes towards gender roles have been transmitted intergenerationally, and that this historical legacy endures even when families migrate to places with different institutions and education systems (although, as noted earlier, views on women entering the workforce tend to converge more rapidly with those of the dominant culture than other cultural traits).[36] Loss Aversion The Nobel Prize laureate Daniel Kahneman and cognitive psychologist Amos Tversky uncovered a common tendency among humans to attach greater weight to a loss than to a gain of equal or comparable size.[37] ‘Loss aversion’, as they term it, is an important determinant of the level of entrepreneurial activity in a population, which is in turn a significant factor in the driving of economic growth in the modern world.

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Thus, in Volatilia, inhabitants of certain areas escape weather-inflicted damage even in exceptionally difficult years, while in Uniformia, severe weather conditions affect the entire population and threaten mass extinction. Both continents are home to multiple societies. At first, some cultures on each of the continents are intensely loss-averse while others have more neutral attitudes towards losses. The loss-averse cultures adopt farming strategies that produce crop yields that are on average smaller but are also less vulnerable to climatic fluctuations. They can guarantee basic living conditions for their families regardless of weather conditions, so their population size remains stable over time.

Evil by Design: Interaction Design to Lead Us Into Temptation
by Chris Nodder
Published 4 Jun 2013

All of these situations work by framing the doubt in terms of what people may lose by choosing the undesired option. Although that’s a double negative, it has more power than reminding people of what they might gain by choosing the desired option. People feel loss more powerfully than gain, so it’s easier to manipulate them through doubt about negative outcomes. The magic of loss aversion will do all the rest. How to instill doubt Loss aversion is strongest when people have recently experienced the benefits of the product or service. If customers are canceling after a period of inactivity, find a way to convince them to use the product again (for instance by offering a free month of service, or access to a premium feature) so that they will feel the loss more keenly.

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Table of Contents Cover Credits About the Author About the Technical Editor Acknowledgments Foreword Introduction Evil designs and their virtuous counterparts Pride Misplaced pride causes cognitive dissonance Social proof: Using messages from friends to make it personal and emotional Closure: The appeal of completeness and desire for order Manipulating pride to change beliefs Sloth Sloth: Is it worth the effort? Gluttony Deserving our rewards Escalating commitment: foot-in-the-door, door-in-the-face Invoking gluttony with scarcity and loss aversion Anger Avoiding anger Embracing anger Using anger safely in your products Envy Manufacturing envy through desire and aspiration Status envy: demonstrating achievement and importance Manufacturing and maintaining envy in your products Lust Creating lust: Using emotion to shape behavior Controlling lust: Using desire to get a commitment Lustful behavior Greed Learning from casinos: Luck, probability, and partial reinforcement schedules Anchoring and arbitrary coherence Evil by Design Should you feel bad about deception?

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Companies encourage this overabundance by making us feel like we deserve to be rewarded and by escalating our level of commitment beyond what we first intended, drawing us in from early engagement through to full-on compliance. Sites also make us fearful of missing out—scarcity, exclusivity, and loss aversion play on the fears behind gluttony. Deserving our rewards We are easily fooled into gluttony. Just having healthy options available on menus or among the selections from a vending machine is sometimes enough to make our brains think we’ve satisfied our health and nutrition goals, and therefore have permission to choose less honorable options.

The Upside of Irrationality: The Unexpected Benefits of Defying Logic at Work and at Home
by Dan Ariely
Published 31 May 2010

* * * Supersizing the Incentive I should probably tell you now that we didn’t start out running our experiments in the way I just described. Initially, we set about to place some extra stress on our participants. Given our limited research budget, we wanted to create the strongest incentive we could with the fixed amount of money we had. We chose to do this by adding the force of loss aversion to the mix.* Loss aversion is the simple idea that the misery produced by losing something that we feel is ours—say, money—outweighs the happiness of gaining the same amount of money. For example, think about how happy you would be if one day you discovered that due to a very lucky investment, your portfolio had increased by 5 percent.

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Contrast that fortunate feeling to the misery that you would feel if, on another day, you discovered that due to a very unlucky investment, your portfolio had decreased by 5 percent. If your unhappiness with the loss would be higher than the happiness with the gain, you are susceptible to loss aversion. (Don’t worry; most of us are.) To introduce loss aversion into our experiment, we prepaid participants in the small-bonus condition 24 rupees (6 times 4). Participants in the medium-bonus condition received 240 rupees (6 times 40), and participants in the very-large-bonus condition were prepaid 2,400 rupees (6 times 400).

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First, it was difficult for me to accept the doctors’ recommendation because of two related psychological forces we call the endowment effect and loss aversion. Under the influence of these biases, we commonly overvalue what we have and we consider giving it up to be a loss. Losses are psychologically painful, and, accordingly, we need a lot of extra motivation to be willing to give something up. The endowment effect made me overvalue my arm, because it was mine and I was attached to it, while loss aversion made it difficult for me to give it up, even when doing so might have made sense. A second irrational influence is known as the status quo bias.

Why Nudge?: The Politics of Libertarian Paternalism
by Cass R. Sunstein
Published 25 Mar 2014

The purely semantic reframing has a major effect on people’s judgments. Similarly, people are “loss averse,” in the sense that they dislike losses more than they like corresponding gains. If people face a five-cent tax for using a plastic bag (a loss), they are much more likely to be affected than if they are given a five-cent bonus (a gain) for bringing their own bag.8 In response to questions, people persistently show both framing effects and loss aversion. (There is a nice lesson here for policymakers. If you want to have an impact, choose effective frames and enlist loss aversion. Is it paternalistic for policymakers to heed that lesson?

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—Consumers might lack information or a full appreciation of information even when it is presented. —Consumers might be especially averse to the short-term losses associated with the higher prices of energy-efficient products relative to the uncertain future fuel savings, even if the expected present value of those fuel savings exceeds the cost (the behavioral phenomenon of “loss aversion”). —Even if consumers have relevant knowledge, the benefits of energy-efficient vehicles might not be sufficiently salient to them at the time of purchase, and the lack of salience might lead consumers to neglect an attribute that it would be in their economic interest to consider. —U.S. Environmental Protection Agency, Final Rule on Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards Contents INTRODUCTION Behaviorally Informed Paternalism ONE Occasions for Paternalism TWO The Paternalist’s Toolbox THREE Paternalism and Welfare FOUR Paternalism and Autonomy FIVE Soft Paternalism and Its Discontents EPILOGUE The Lives We Save May Be Our Own Notes Acknowledgments Index Why Nudge?

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Before you answer “yes,” note that some kind of framing is inevitable.) Now assume that people are answering those same questions in a foreign language—that is, a language that they speak, but in which they are not entirely comfortable. Here is the key finding: It turns out that they do not show either framing effects or loss aversion.9 Asked to resolve problems in a language that is not their own, people are less likely to depart from standard accounts of rationality. In an unfamiliar language, they are more likely to get the right answer. How can this be? The answer is straightforward. When people are using their own language, they think quickly and effortlessly, so System 1 has the upper hand.

Priceless: The Myth of Fair Value (And How to Take Advantage of It)
by William Poundstone
Published 1 Jan 2010

Prospect Theory 97 “I would go batty”: Barbara Tversky interview, July 8, 2008. 97 “interesting choices”: Kahneman Nobel autobiography, nobelprize.org/nobel_prizes/economics/laureates/2002/kahneman=autobio.html. 97 Tversky’s idea to put a negative sign on amounts: Kahneman Nobel autobiography. 98 “We reasoned that”: Ibid. 98 “Our perceptual apparatus”: Kahneman and Tversky 1979, 277. 99 “extends to the domain of moral intuitions”: Kahneman Nobel autobiography. 101 Loss aversion in real estate: Ibid. 101 Loss aversion their greatest contribution: Ibid. 102 “The major points of prospect theory”: Lambert 2006. 102 the most cited article ever to appear in Econometrica: Laibson and Zeckhauser 1998, 8, which finds 1,703 citations. 102 Merckle suicide: Moulson 2009. 102 “Humans did not evolve to be happy”: Camerer, Loewenstein, and Prelec 2005, 27. 103 “Many of the losses people fear most”: Camerer n.d. (“Three cheers—psychological, theoretical, empirical—for loss-aversion”), 9–10. 17. Rules of Fairness 104 “spend a lot of money honestly”: Kahneman, Nobel autobiography, nobelprize.org/nobel_prizes/economics/laureates/2002/kahneman=autobio.html. 104 Russell Sage biography: Sarnoff 1965.

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This is much like the “adaptation level” of psychophysics. The reference point determines whether something is entered as a gain or a loss on the mental ledger. That can make a huge difference in behavior. A second key idea of prospect theory is loss aversion. Losing money (anything of value) hurts more than gaining that same thing delights. You can demonstrate loss aversion by offering a bet on a coin toss. Tails you lose $100, and heads you win X. How big does X have to be for you to take the bet? Surveys show that few want to accept a “fair” bet with X = $100. Few accept X = $110, which offers a nice expected profit.

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Replace “food” with “money” or any other gain, and you have prospect theory. We act as if losing $500 at poker is a life-or-death issue. Camerer suggests that loss aversion is a form of unreasoning fear, like that an acrophobic experiences looking out the window of a penthouse. “Many of the losses people fear most are not life-threatening, but there is no telling that to an emotional system overadapted to conveying fear signals,” Camerer wrote. “Thinking of loss-aversion as fear also implies the possibility that inducing emotions can push around buying and selling prices.” Seventeen Rules of Fairness Kahneman and Tversky spent the 1977–78 academic year at Stanford, polishing their prospect theory paper.

The Personal MBA: A World-Class Business Education in a Single Volume
by Josh Kaufman
Published 2 Feb 2011

As a result, people tend to become more conservative, avoiding risks that could make things worse. Unfortunately, some of those risks—like starting a new business—may actually present a major opportunity to make things better. The best way to overcome Loss Aversion is to Reinterpret the risk of loss as “no big deal.” Casinos are in the business of overcoming Loss Aversion every single day—in a sense, the ostentatious buildings on the Las Vegas Strip are enormous monuments to human stupidity. If Loss Aversion is such a big deal, how do casinos encourage people to play games in which they’re mathematically certain to lose money? Casinos win by abstracting the loss. Instead of having players gamble with currency, which is perceived as valuable, the casino converts currency into chips or debit cards, which don’t feel as valuable.

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SHARE THIS CONCEPT: http://book.personalmba.com/willpower-depletion/ Loss Aversion Our doubts are traitors, and make us lose the good we oft might win, by fearing to attempt. —WILLIAM SHAKESPEARE, MEASURE FOR MEASURE Recently, my wife, Kelsey, decided to withdraw some funds from an investment account. When the brokerage deposited the money into her bank account, they deposited an additional $10,000 by mistake. Rationally, it shouldn’t have been a big deal—it was a simple mistake that was easily corrected. Emotionally, however, Kelsey felt like she was “losing” the extra money, even though it wasn’t really hers at all. Loss Aversion is the idea that people hate to lose things more than they like to gain them.

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If you notice that your portfolio went down 100 percent, you’ll feel horrible. Loss Aversion explains why threats typically take precedence over opportunities when it comes to Motivation. The threat of loss used to require immediate attention, because losses were extremely costly—even life threatening. Dying or losing a loved one to a predator, sickness, exposure, or starvation is universally a horrible experience, so we’re built to do everything in our power to prevent that from happening. The potential losses we typically face now are rarely as serious, but our minds still give them automatic priority. Loss Aversion also explains why uncertainty appears risky.

The Behavioral Investor
by Daniel Crosby
Published 15 Feb 2018

One consequence of being wired to live is loss aversion; an asymmetric fear of bad stuff happening to you. Loss aversion is driven by the amygdalae, two tiny almond-shaped structures that are the seat of all of your emotional responses. Evolutionarily, loss aversion makes a lot of sense and many scientists believe it is why Homo sapiens outlasted other human species on the way to the top of the food chain. As McDermott, Fowler and Smirnov (2008) point out, running out of food was fatal and so a disposition toward avoiding loss is what prompted our ancient ancestors to pack up and forage in a new spot.19 While loss aversion is derided as being irrational in an investment context, those with a genetic predisposition against it didn’t live to see a time when their even-headedness could prevail.

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All paths to conservatism, it would seem, run through some form of loss aversion. It is perhaps the most widely disseminated finding of behavioral finance that our risk and reward preferences are asymmetrical and that we care far more about avoiding loss than we do about achieving gain. What is less understood is the brain science behind this phenomenon As reported in Scientific American, Dr. Russell Poldrack and his colleagues found that, “…the brain regions that process value and reward may be silenced more when we evaluate a potential loss than they are activated when we assess a similar sized gain.” Loss aversion is as much a physiological construct as it is a psychological one.

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Non-identification – Now that you have recognized, accepted and investigated your stress, you must realize that you are more than your emotions. You can feel something without being defined by it. What’s the big idea? Physical states can impact emotion just as surely as the reverse is true. Loss aversion kept our ancestors alive. It keeps you from being a successful investor. The body longs for homeostasis. Thinking about money disrupts homeostasis. Stress is as much a physical as it is a psychic phenomenon. Taking financial risk causes real bodily pain. Fear is impossible to extinguish since the body stores it for a rainy day.

Mindware: Tools for Smart Thinking
by Richard E. Nisbett
Published 17 Aug 2015

We don’t always behave in the fully rational way demanded by cost-benefit theory, but we can arrange the world so that we don’t have to in order to get the same benefits we would if we were professional economists. Loss Aversion We have a general tendency to avoid giving up what we already have, even in situations where the cost-benefit considerations say that we should relinquish what we have for the clear prospect of getting something better. The tendency is called loss aversion. Across a wide range of situations, it appears that gaining something only makes you about half as happy as losing the same thing makes you unhappy.1 We pay dearly for our aversion to loss.

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Unlucky students who did not get a mug are asked to examine one and say how much they would pay for a mug just like it. Mug owners are asked how much they would sell their mugs for. There is a heavy discrepancy between the two amounts. On average, owners are willing to sell only when the price is double what the average nonowner is willing to pay.2 Loss aversion lies behind this endowment effect. People don’t want to give up things they own, even for more than they originally considered a fair price. Imagine you bought a ticket to a football game for two hundred dollars but would have been willing to pay five hundred dollars. Then a couple of weeks later you discover on the Internet that there are lots of desperate people willing to pay up to two thousand dollars for a ticket.

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The previous point requires a huge qualification. Sentimental value is properly considered when thinking about a transaction. You couldn’t afford to buy my wedding band. But few people have an attachment to a bottle of Château de Something-or-Other that they would describe as sentimental. Changing the Status Quo Loss aversion produces inertia. Changing our behavior usually involves a cost of some kind. “Shall I change the channel? I have to get up to find the remote. I have to decide what would be a more interesting program to watch. Or maybe I would enjoy reading a book more. What book? Oh, well, I haven’t watched Jeopardy!

Nudge: Improving Decisions About Health, Wealth, and Happiness
by Richard H. Thaler and Cass R. Sunstein
Published 7 Apr 2008

When they have to give something up, they are hurt more than they are pleased if they acquire the very same thing. It is also possible to measure loss aversion with gambles. Suppose I ask you whether you want to make a bet. Heads you win $X, tails you lose $100. How much does X have to be for you to take the bet? For most people, the answer to this question is somewhere around $200. This implies that the prospect of winning $200 just offsets the prospect of losing $100. Loss aversion helps produce inertia, meaning a strong desire to stick with your current holdings. If you are reluctant to give up what you have because you do not want to incur losses, then you will turn down trades you might have otherwise made.

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The mugs and the chocolate cost about the same, and in pretests students were as likely to choose one as the other. Yet when offered the opportunity to switch from a mug to a candy bar or vice versa, only one in ten switched. As we will see, loss aversion operates as a kind of cognitive nudge, pressing us not to make changes, even when changes are very much in our interests. Status Quo Bias Loss aversion is not the only reason for inertia. For lots of reasons, people have a more general tendency to stick with their current situation. This phenomenon, which William Samuelson and Richard Zeckhauser (1988) have dubbed the “status quo bias,” has been demonstrated in numerous situations.

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Louis Germany, organ donations in Gilovich, Tom Give More Tomorrow Goldstein, Dan Goolsbee, Austan Gore, Al Gould, Stephen Jay government: distrust of, libertarian paternalism of, neutrality in, paternalism of, and RECAP, and retirement plans, and slippery slope, starting points provided by, transparency in government bonds greenhouse gas emissions Greenhouse Gas Inventory (GGI), proposed Green Lights, EPA program, Gross, David, group norms, gut feelings Hackman, Gene Halloween night experiment H&R Block, and FAFSA software Harvard School of Public Health Hazard Communication Standard (HCS) health care, birth control pills, choosing, costs of, defensive medicine, Destiny Health Plan, deterrent effect of tort liability in, drug compliance, framing in, freedom of contract in, incentive conflicts in, ineffective lawsuits in, libertarian paternalists on, medical malpractice liability, negligence defined in, “no-fault” system in some countries, organ donations, prescription drug plan, right to sue for negligence, social influences in, treatment options “heuristics and biases” approach Hoffman, Dustin home-building industry home equity loans Home Ownership and Equity Protection Act homo economicus (economic man) “hot-cold empathy gap,” hot-hand theory hot states Houston Natural Gas Howell, William Hoxby, Carolyn Humans: Automatic Systems used by, conformity of, difficult choices for, influenced by a nudge, loss aversion of, and money, social pressures on, use of term Hurricane Katrina Illinois First Person Consent registry imitation incentives, conflicts of, in free markets, in investments, and salience income tax: Automatic Tax Return, compliance in, refunds from index funds inertia: and default option, and loss aversion, and organ donations, power of, and status quo bias, “yeah, whatever,” information, spread of Informed Decisions inheritance INSEAD School of Business, France insurance: costs of, fraught choices in, health Internal Revenue Service (IRS) intuitive thinking, test of investment goods investments, asset allocation in, in company stock, default options, and ERISA, error expected in, feedback in, incentives in, index funds, “lifestyle” funds, mappings in, and market timing, mental accounting in, mutual funds, past performance of, portfolio management, portfolio theory, rates of return, risk in, rules of thumb for, stocks and bonds, structuring complex choices, “target maturity funds,” iPhone and iPod IRAs Johnson, Eric Johnson, Samuel Jones, Rev.

Decisive: How to Make Better Choices in Life and Work
by Chip Heath and Dan Heath
Published 26 Mar 2013

Compounding this preference for the status quo is another bias called loss aversion, which says that we find losses more painful than gains are pleasant. Imagine that we offer you the chance to play a game. We’ll flip a coin; if it turns up heads, you’ll win $100, and if it lands on tails, you owe us $50. Would you play? Most people wouldn’t, because they are loss averse: Losing $50 is so painful that even a potential gain twice as large doesn’t seem sufficient to compensate. Indeed, researchers have found again and again that people act as though losses are from two to four times more painful than gains are pleasurable. Loss aversion shows up in many different contexts.

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• A 10/10/10 analysis tipped Annie toward saying “I love you” first to Karl. 4. Our decisions are often altered by two subtle short-term emotions: (1) mere exposure: we like what’s familiar to us; and (2) loss aversion: losses are more painful than gains are pleasant. • How many of our organizational truths are ideas that we like merely because they’ve been repeated a lot? • Students given a mug won’t sell it for less than $7.12, even though five minutes earlier they wouldn’t have paid more than $2.87! 5. Loss aversion + mere exposure = status-quo bias. • PayPal: Ditching the PalmPilot product was a no-brainer—but it didn’t feel that way. 6. We can attain distance by looking at our situation from an observer’s perspective

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Mita, Marshall Dermer, and Jeffrey Knight (1977), “Reversed Facial Images and the Mere Exposure Hypothesis,” Journal of Personality and Social Psychology 35: 597–601. Repetition sparked trust: Alice Dechêne, et al. (2010), “The Truth About the Truth: A Meta-analytic Review of the Truth Effect,” Personality and Social Psychology Review 14: 238–57. 5 Loss aversion. The classic first discussion of loss aversion is Daniel Kahneman and Amos Tversky (1979), “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica 47: 263–92. This paper by two psychologists appeared in the journal that is the high temple of technical economics and became the most cited paper ever to appear in the journal.

Stocks for the Long Run, 4th Edition: The Definitive Guide to Financial Market Returns & Long Term Investment Strategies
by Jeremy J. Siegel
Published 18 Dec 2007

This tendency to base decisions on the short-term fluctuations in the market has been referred to as myopic loss aversion. Since over longer periods, the probability of stocks showing a loss is much smaller, investors influenced by loss aversion would be more likely to hold stocks if they monitored their performance less frequently. Dave: That’s so true. When I look at stocks in the very short run, they seem so risky that I wonder why anyone holds them. But over the long run, the superior performance of equities is so overwhelming, I wonder why anyone doesn’t hold stocks! IC: Exactly. Shlomo Bernartzi and Richard Thaler claim that myopic loss aversion is the key to solving the equity premium puzzle.27 For years, econ26 Shlomo Bernartzi and Richard Thaler, “Myopic Loss Aversion and the Equity Premium Puzzle,” Quarterly Journal of Economics, 1995, pp. 73–91. 27 See Chapter 8 for a further description of the equity premium puzzle. 

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318 Chapter 19 Behavioral Finance and the Psychology of Investing 319 The Technology Bubble, 1999 to 2001 320 Behavioral Finance 322 Fads, Social Dynamics, and Stock Bubbles 323 Excessive Trading, Overconfidence, and the Representative Bias 325 Prospect Theory, Loss Aversion, and Holding On to Losing Trades 328 Rules for Avoiding Behavioral Traps 331 Myopic Loss Aversion, Portfolio Monitoring, and the Equity Risk Premium 332 Contrarian Investing and Investor Sentiment: Strategies to Enhance Portfolio Returns 333 Out-of-Favor Stocks and the Dow 10 Strategy 335 PART 5 BUILDING WEALTH THROUGH STOCKS Chapter 20 Fund Performance, Indexing, and Beating the Market 341 The Performance of Equity Mutual Funds 342 Finding Skilled Money Managers 346 xiv Persistence of Superior Returns 348 Reasons for Underperformance of Managed Money 348 A Little Learning Is a Dangerous Thing 349 Profiting from Informed Trading 349 How Costs Affect Returns 350 The Increased Popularity of Passive Investing 351 The Pitfalls of Capitalization-Weighted Indexing 351 Fundamentally Weighted versus Capitalization-Weighted Indexation 353 The History of Fundamentally Weighted Indexation 356 Conclusion 357 Chapter 21 Structuring a Portfolio for Long-Term Growth 359 Practical Aspects of Investing 360 Guides to Successful Investing 360 Implementing the Plan and the Role of an Investment Advisor 363 Concluding Comment 364 Index 367 CONTENTS F O R E W O R D Some people find the process of assembling data to be a deadly bore.

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Prospect Theory, Loss Aversion, and Holding On to Losing Trades Dave: I see. Can we talk about individual stocks? Why do I end up holding so many losers in my portfolio? IC: Remember I said before that Kahneman and Tversky had kicked off behavioral finance with prospect theory? A key point in their theory was that individuals form a reference point from which they judge their performance. They found that from that reference point individuals are much more upset about losing a given amount of money than they are from gaining the same amount. They called this behavior loss aversion, and they suggested that the decision to hold or sell an investment will be dramatically influenced by whether your stock has gone up or down— in other words, whether you have had a gain or loss.

Stocks for the Long Run 5/E: the Definitive Guide to Financial Market Returns & Long-Term Investment Strategies
by Jeremy Siegel
Published 7 Jan 2014

This tendency to base decisions on the short-term fluctuations in the market has been referred to as myopic loss aversion. Since over longer periods, the probability of stocks showing a loss is much smaller, investors influenced by loss aversion would be more likely to hold stocks if they monitored their performance less frequently. Dave: That’s so true. When I look at stocks in the very short run, they seem so risky that I wonder why anyone holds them. But over the long run, the superior performance of equities is so overwhelming, I wonder why anyone doesn’t hold stocks! IC: Exactly. Bernartzi and Thaler claim that myopic loss aversion is the key to solving the equity premium puzzle29 For years, economists have been trying to figure out why stocks have returned so much more than fixed-income investments.

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Chapter 22 Behavioral Finance and the Psychology of Investing The Technology Bubble, 1999 to 2001 Behavioral Finance Fads, Social Dynamics, and Stock Bubbles Excessive Trading, Overconfidence, and the Representative Bias Prospect Theory, Loss Aversion, and the Decision to Hold on to Losing Trades Rules for Avoiding Behavioral Traps Myopic Loss Aversion, Portfolio Monitoring, and the Equity Risk Premium Contrarian Investing and Investor Sentiment: Strategies to Enhance Portfolio Returns Out-of-Favor Stocks and the Dow 10 Strategy PART V BUILDING WEALTH THROUGH STOCKS Chapter 23 Fund Performance, Indexing, and Beating the Market The Performance of Equity Mutual Funds Finding Skilled Money Managers Persistence of Superior Returns Reasons for Underperformance of Managed Money A Little Learning Is a Dangerous Thing Profiting from Informed Trading How Costs Affect Returns The Increased Popularity of Passive Investing The Pitfalls of Capitalization-Weighted Indexing Fundamentally Weighted Versus Capitalization-Weighted Indexation The History of Fundamentally Weighted Indexation Conclusion Chapter 24 Structuring a Portfolio for Long-Term Growth Practical Aspects of Investing Guides to Successful Investing Implementing the Plan and the Role of an Investment Advisor Concluding Comment Notes Index FOREWORD In July 1997 I called Peter Bernstein and said I was going to be in New York and would love to lunch with him.

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And according to news reports, there are some reformed traders who are establishing Traders’ Anonymous programs designed to help people who cannot resist the temptations of trading too frequently.27 Maybe you should look into those programs. Myopic Loss Aversion, Portfolio Monitoring, and the Equity Risk Premium Dave: Because of how badly I was doing in the market, I even considered giving up on stocks and sticking with bonds, although I know that in the long run that is a very bad idea. How often do you suggest that I monitor my stock portfolio? IC: Important question. If you buy stocks, it is very likely that the value will drop below the price you paid, if but for a short time after your purchase. We have already spoken about how loss aversion makes this decline very disturbing. However, since the long-term trend in stocks is upward, if you wait a period of time before checking your portfolio, the probability that you will see a loss decreases.

A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing (Eleventh Edition)
by Burton G. Malkiel
Published 5 Jan 2015

Expected value = ½($250) + ½(–$100) = $75. Kahneman and Tversky concluded that losses were 2½ times as undesirable as equivalent gains were desirable. In other words, a dollar loss is 2½ times as painful as a dollar gain is pleasurable. People exhibit extreme loss aversion, even though a change of $100 of wealth would hardly be noticed for most people with substantial assets. We’ll see later how loss aversion leads many investors to make costly mistakes. Interestingly, however, when individuals faced a situation where sure losses were involved, the psychologists found that they were overwhelmingly likely to take the gamble. Consider the following two alternatives: 1.

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REAPING REWARD BY INCREASING RISK Beta and Systematic Risk The Capital-Asset Pricing Model (CAPM) Let’s Look at the Record An Appraisal of the Evidence The Quant Quest for Better Measures of Risk: Arbitrage Pricing Theory The Fama-French Three-Factor Model A Summing Up 10. BEHAVIORAL FINANCE The Irrational Behavior of Individual Investors Overconfidence Biased Judgments Herding Loss Aversion Pride and Regret Behavioral Finance and Savings The Limits to Arbitrage What Are the Lessons for Investors from Behavioral Finance? 1. Avoid Herd Behavior 2. Avoid Overtrading 3. If You Do Trade: Sell Losers, Not Winners 4. Other Stupid Investor Tricks Does Behavioral Finance Teach Ways to Beat the Market?

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Moreover, people deviate in systematic ways from rationality, and the irrational trades of investors tend to be correlated. Behavioral finance then takes that statement further by asserting that it is possible to quantify or classify such irrational behavior. Basically, there are four factors that create irrational market behavior: overconfidence, biased judgments, herd mentality, and loss aversion. Well, yes, believers in efficient markets say. But—and we believers always have a but—the distortions caused by such factors are countered by the work of arbitrageurs. This last is the fancy word used to describe people who profit from any deviation of market prices from their rational value.

Fancy Bear Goes Phishing: The Dark History of the Information Age, in Five Extraordinary Hacks
by Scott J. Shapiro

As Kahneman and Tversky argued, happiness is not only a function of one’s “endowments” (the stuff one owns), but also changes in it. Jack and Jill have the same endowments, but Jack’s has gone up and Jill’s has gone down. Kahneman and Tversky argued that human beings are “loss averse”: we are far more sensitive to losses than to gains. Put bluntly, we really hate to lose. Kahneman and Tversky demonstrated the power of loss aversion by offering participants in studies the choice of several gambles. Here’s the first choice: Option 1: Gamble with an 80 percent chance of winning $4,000 and a 20 percent chance of winning $0. Option 2: Gamble with a 100 percent chance of winning $3,000.

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Johnson, “The Affect Heuristic in Judgments of Risks and Benefits,” Journal of Behavioral Decision Making 13 (2000): 5. Nigerian Astronaut: Katharine Trendacosta, “Here’s the Best Nigerian Prince Email Scam in the Galaxy,” Gizmodo, February 12, 2016, https://gizmodo.com/we-found-the-best-nigerian-prince-email-scam-in-the-gal-1758786973. “loss averse”: Amos Tversky and Daniel Kahneman, “Loss Aversion in Riskless Choice: A Reference-Dependent Model,” The Quarterly Journal of Economics, November 1991. Jack and Jill example from Kahneman, Thinking, Fast and Slow, 275. promise gains: Teodor Sommestad and Henrik Karlzén, “A Meta-Analysis of Field Experiments on Phishing Susceptibility” (2019 APWG Symposium on Electronic Crime Research [eCrime]).

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By ratcheting up anxiety, Fancy Bear hoped to persuade campaign staffers that clicking on the blue link would have high benefits and low risks. Fear, in other words, was designed to mask any contradictory evidence suggesting that the link was malicious. To magnify this effect, Fancy Bear added time pressure. The recipient must click the link immediately. Loss Aversion We’ve all gotten those Nigerian Prince phishing emails. But the Nigerian Astronaut version pushes this internet scam to eleven. It purports to be from Dr. Bakare Tunde, the cousin of Nigerian astronaut and air force major Abacha Tunde. Major Tunde, the doctor informs us, was the first African in space when he made a secret flight to the Salyut 6 space station in 1979.

Never Split the Difference: Negotiating as if Your Life Depended on It
by Chris Voss and Tahl Raz
Published 3 Oct 1989

But first let me leave you with a crucial lesson about loss aversion: In a tough negotiation, it’s not enough to show the other party that you can deliver the thing they want. To get real leverage, you have to persuade them that they have something concrete to lose if the deal falls through. 1. ANCHOR THEIR EMOTIONS To bend your counterpart’s reality, you have to start with the basics of empathy. So start out with an accusation audit acknowledging all of their fears. By anchoring their emotions in preparation for a loss, you inflame the other side’s loss aversion so that they’ll jump at the chance to avoid it.

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Martin Parish, Louisiana, 162–63, 171 “proof of life” and, 34, 147, 148–49, 165, 170 Schilling case, 96, 98–105 terrorists and, 232 “that’s right” and, 101–5 “knowing their religion,” 225, 228–29, 244 offering reasons that reference counterpart’s religion, 231 power of hopes and dreams and, 230–31 similarity principle and, 229–30 Koresh, David, 13 labeling, 19, 50, 54–73, 112 accusation audit, 64–68, 73, 254–55 Assertive (bargaining style) and, 196 avoiding “I,” 56 cranky grandfather example, 59 deescalating angry confrontations with, 58–59 to discover source of incongruence, 176 empathy as a mood enhancer, 62 empathy building and, 239 to extract information, 239, 257–58 of fears, 61–62 fill-in-the-blank examples, 255, 258 Girl Scout fundraiser and, 62–63 intentionally mislabeling an emotion, 91, 94 key lessons of, 71–73 labeling and calming fear, 61, 63, 64, 67, 73 lawyers and “taking the sting out” technique, 65 Lieberman brain imaging study, 55 negativity and, 57–61, 64–68, 70 phrasing the label, 56 Rule of Three and, 177 rules about form and delivery, 55 Schilling kidnapping case and, 103 silences and, 56–57, 71, 72 step one: detecting the other person’s emotional state, 55–56 step two: labeling it aloud, 56 as transformative, 63 Washington Redskins ticket holder script, 60–61 “words, music, and dance” and, 55 Lanceley, Fred, 14–15 Langer, Ellen, 231 late-night FM DJ voice, 19, 31–33, 47 contract discussion and, 34 downward-inflecting statement, 32, 33 general demeanor and delivery, 32 Harlem fugitive stand-off negotiation and, 51 hostage negotiation and, 33–34, 38 lawyer-negotiators, 192–93 Leonsis, Ted, 231 “Lessons of Waco: Proposed Changes in Federal Law Enforcement” (Heymann), 14 leverage, 220–24 Black Swans as leverage multipliers, 220–21, 224, 244 in a kidnapping, 221 loss aversion and, 128 negative, 222–23, 226, 227, 244 normative, 224, 226, 244 personal negotiation styles and, 192 positive, 221–22, 226, 244 what it is, 220 liars. See falsehoods and liars Lieberman, Matthew, 55 listening. See active listening loss aversion, 12, 127–28, 139, 223, 257 Macapagal-Arroyo, Gloria, 140 Malhotra, Deepak, 178, 179, 233 Mehrabian, Albert, 176 Memphis Bar Association, 132 Middle Eastern merchants, 33 Miller, George A., 28 Miller, Winnie, 227 mindset finding and acting on Black Swans and, 218, 219 as key to successful negotiation, 43 multiple hypotheses approach, 25 positive, 33, 47 ready-to-walk, 204–5 win-win, 115 mirroring (isopraxism), 19, 35–36, 44, 48, 70, 71, 183 active listening and, 103 body language and, 36 to elicit information, 185 four step process for workplace negotiation, 44–46 reaction to use of “fair” in negotiations, 125 silences and, 37, 44, 72 use with Assertive bargainers, 196 use with assertive people, 191–92 verbal, 36 Wiseman waiter study, 36 Misino, Dominick, 41–42 Mnookin, Robert, 2–4, 5 Moore, Don A., 120 Moore, Margaret, 214–15, 217 Mousavian, Seyed Hossein, 124 MSU (making shit up) approach, 30 Mueller, Robert, 143 negotiation.

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There’s the Framing Effect, which demonstrates that people respond differently to the same choice depending on how it is framed (people place greater value on moving from 90 percent to 100 percent—high probability to certainty—than from 45 percent to 55 percent, even though they’re both ten percentage points). Prospect Theory explains why we take unwarranted risks in the face of uncertain losses. And the most famous is Loss Aversion, which shows how people are statistically more likely to act to avert a loss than to achieve an equal gain. Kahneman later codified his research in the 2011 bestseller Thinking, Fast and Slow.3 Man, he wrote, has two systems of thought: System 1, our animal mind, is fast, instinctive, and emotional; System 2 is slow, deliberative, and logical.

Expected Returns: An Investor's Guide to Harvesting Market Rewards
by Antti Ilmanen
Published 4 Apr 2011

The two main behavioral explanations both require combining loss aversion with one other behavioral feature—a short time horizon (myopia) or the house money effect:• The myopic loss aversion model relies on a variant of mental accounting related to the investment time horizon (evaluation period). A given expected return advantage will attract investors more, the longer their investment horizon is. If investors evaluate their portfolios very frequently, the odds of risky assets outperforming riskless ones are close to 50/50 and loss aversion kicks in. Over longer horizons, the odds steadily improve. A typical degree of loss aversion applied to annual changes in financial wealth can justify an equity premium of 6.5%, suggesting that an annual portfolio evaluation period is plausible for the overall market

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• Yale’s Nicholas Barberis and co-authors develop an equilibrium model in which investors derive utility both from consumption and from annual changes in wealth. They too assume a typical degree of loss aversion (just above 2) but find that a model with constant loss aversion cannot fully explain the equity premium puzzle. However, they can resolve the puzzle if they include in their model the “house money effect”—the idea that the degree of loss aversion varies dynamically with prior gains and losses. The model thus implies that investors’ risk attitudes become more conservative in down-markets. The next section shows that estimates of the equity premium have edged lower since the 1990s.

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More risk preferences The house money effect is an important example of mental accounting. Gamblers tend to become less loss averse and more willing to take risks when they are ahead (“playing with house money)”. Greater willingness to gamble after recent gains suggests that losses are easier to take when they can be mentally added to earlier gains. At first blush, this may sound inconsistent with PT. However, PT as described above pertains to one-off gambles. Risk preferences in a sequence of gambles depend on how prior gains and losses influence loss aversion over time. An experimental study by Thaler and Johnson finds evidence in favor of the house money effect—more aggressive risk taking following successful trading, and cautiousness following losses.

The Great Economists Ten Economists whose thinking changed the way we live-FT Publishing International (2014)
by Phil Thornton
Published 7 May 2014

The problems occur when one party wants to avoid losses and another to make gains. Good examples in real life are renegotiations of existing contracts such as pay agreements between managers and unions or global trade agreements between large and small countries. Loss aversion creates an asymmetry that makes agreements hard to reach, Kahneman says. Loss aversion affects how people behave on the high street, in the workplace and at home. Kahneman and colleagues found that in this situation the existing sale price, wage or rent sets the reference point, which creates a position that must not be infringed. People see that companies are behaving unfairly if they try to impose losses (relative to the reference point) by hiking prices or rents or trimming wages – unless they can show that it is to defend their own position.

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Adam Smith, The Theory of Moral Sentiments (1759), Part VI, Sect. 1. 232 The Great Economists The significance is that assumptions of rational behaviour that simply look at the chances of a particular gamble coming off without any reference to the financial position of the gambler ignore the impact that the phrasing of the bet can have, and the way that people’s aversion to suffering losses can alter the way they make decisions. The danger of loss aversion is that it leads us to try to minimise these feelings of loss – even when it does not make financial sense to do so. Loss aversion has a noticeable effect in the housing market as evidence suggests that people are often unwilling to sell their home for less than they paid for it. Their reference point is what they paid for it in the past rather than its current value, which may be about to fall further.

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This involves both an assessment of the mathematical probabilities but also a subjective view of the outcomes, particularly set against the reference point. They found that people were more worried about suffering a loss then they were about making a gain, that they valued a sure gain over a probable gain (the certainty effect) and that they preferred a probable loss over a certain loss. Adam Smith had alluded to this idea of loss aversion when he said, ‘we suffer Chapter 10 • Daniel Kahneman231 more … when we fall from a better to a worse situation than we ever enjoy when we rise from a worse to a better’.4 Kahneman said that people were driven more strongly to avoid losses than to achieve gains. A reference point is sometimes the status quo, but it can also be a goal in the future: not achieving a goal is a loss; exceeding it is a gain.

Retirementology: Rethinking the American Dream in a New Economy
by Gregory Brandon Salsbury
Published 15 Mar 2010

Amos Tversky showed that investors are more sensitive to decreases in the value of their portfolio than to increases in value.41 Even in good times, many investors tend to suffer from what experts refer to as “myopic loss aversion”—a basic tenet from the field of behavioral finance, which holds that people psychologically weigh losses twice as heavily as gains. Here’s an example of myopic loss aversion. I flip a coin: Heads, you win $110. Tails, you lose $100. Will you take the bet? Behavioral finance shows us that there will be few takers of the gamble. How much would most people need to win before they would be willing to take the gamble?

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Then...pre-meltdown • In November 2007, the Consumer Confidence Survey checked in at a rather robust score of 87.3.5 • On October 8, 2007, the Dow was at 14,043.6 Now...post-meltdown • In October 2008, the Consumer Confidence Survey had dropped to 38%, the most pessimistic number in more than 25 years.7 In fact, 62% of American adults now believe that today’s children will not be better off than their parents.8 • On September 15, 2008, the stock market would begin a three-week slide that would see the Dow lose 2,937 points, or 26% of its value.9 The Retirement Brain Game Regret and pride—People avoid actions that create regret and seek actions that cause pride. Regret is emotional pain. Pride is emotional joy. Is this causing us to buy high and sell low? Research indicates that two of the most troublesome emotions that plague investors are pride and regret. Myopic loss aversion—One type of event in particular has overwhelming, disproportional impact on investors—loss. As we discussed in the Introduction, research shows that, on average, before people would be willing to risk loss, they would need to see their gains reach at least 2.25 times the potential loss. This is what led Dr.

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Our fear, our confidence, and our emotions convince us to take irrational chances with our investments. We often pull money out of the market when the market goes down and wait until it gets back to its highs to buy again. We repeat the same pattern of buying high and selling low. If you’re doing this, you’re not alone. Loss: A Cautionary Tale What myopic loss aversion means is that we have become so short-sighted, so fearful of loss, so concerned with losing our money, that we often make no decision, or make the wrong decision—either of which may prove costly. For example, suppose your child just entered college and the $10,000 bill for his tuition is due. You can either sell Stock A, for which you paid $20,000 and is now worth $10,000.

A Wealth of Common Sense: Why Simplicity Trumps Complexity in Any Investment Plan
by Ben Carlson
Published 14 May 2015

Historically, 53 percent of all days have been positive and 47 percent negative. With our understanding of loss aversion, the fact that losses hurt more than twice as much as gains make us feel good, if you check the value of your portfolio every single day you're likely to feel terrible about the stock market every single day. Every good feeling you get from gains will get completely wiped out by the terrible feelings on the down days. Loss aversion could also mean that most people are twice as likely to make bone-headed decisions when markets fall because of those feelings. But lengthen your time horizon and the effects of loss aversion slowly start to fade. On an annual basis stocks are up roughly three out of every four years.

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In fact, studies show that economic growth can even have an impact on people's happiness, which can affect their perception of risk in financial assets. Researchers looked at data on life satisfaction and compared it to economic growth in a number of countries. They found that happiness is around six times more sensitive to negative economic growth than it is to positive GDP. This is another form of loss aversion, whereby losses sting more than gains feel good. Difficult environments can leave lasting impressions on our psyches. So not only is it likely that your investments will be down, but your unhappiness will be rising, a terrible combination for making rational decisions with your money.7 While it makes sense to view your entire portfolio in aggregate for asset allocation and performance monitoring purposes, you can also think about it in terms of multiple time horizons.

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It turns into something of a game. However if the same clients lost money, they felt much more regret than if the decision was theirs alone. So following someone else's recommendations reduces the emotional impact of losses. A financial professional can act as something of a shock absorber to your loss aversion. This is why people pay for investment advice. It's a way to defer not only your decisions, but your emotional state in the case of losses.4 Education should be a huge aspect of any financial advisor–client relationship. This cannot be overstated. If an advisor is ever going to be able to save you from yourself, first they must educate you.

When to Rob a Bank: ...And 131 More Warped Suggestions and Well-Intended Rants
by Steven D. Levitt and Stephen J. Dubner
Published 4 May 2015

True to his word, Levitt hasn’t touched a bowling ball since. Loss Aversion in the NFL (SJD) Football coaches are known for being extraordinarily conservative when it comes to calling risky plays, since a single bad decision (or even a good decision that doesn’t work out) can get you fired. In the jargon of behavioral economics, coaches are “loss-averse”; this concept, pioneered by Amos Tversky and Daniel Kahneman, holds that we experience more pain with a loss of x than we experience pleasure with a gain of x. Who experiences loss aversion? Well, just about everyone: day traders, capuchin monkeys, and especially football coaches.

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This is bad news for those who argue that payoffs that come years or decades in the future are sufficient to motivate students. The very best results come when we give the students the money before the test, and then we take the money back if they don’t meet the standards. This result is consistent with what psychologists call “loss aversion.” With young kids, it is a lot cheaper to bribe them with trinkets like trophies and whoopee cushions, but cash is the only thing that works for the older students. It is remarkable how offended people get when you pay students for doing well—so many negative e-mails and comments. Roland Fryer endured the same onslaught as he has experimented with financial incentives in cities around the U.S.

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.”: “My friend Anders Ericsson popularized the magic number of 10,000 hours of practice”: See Dubner and Levitt, “A Star is Made,” The New York Times Magazine, May 7, 2006; K. Anders Ericsson, Neil Charness, Paul J. Feltovich, and Robert R. Hoffman, The Cambridge Handbook of Expertise and Expert Performance, Cambridge University Press, 2006. 206 “LOSS AVERSION IN THE NFL”: “Just about everyone . . . capuchin monkeys”: See Dubner and Levitt, “Monkey Business,” The New York Times Magazine, June 5, 2005. 208 “BILL BELICHICK IS GREAT”: “Teams seem to punt way too much”: See David Romer, “Do Firms Maximize? Evidence from Professional Football,” Journal of Political Economy 118, no. 2 (2006). / 209 “I’ve seen the same thing in my research on penalty kicks in soccer”: Pierre-André Chiappori, Steven D.

The Irrational Bundle
by Dan Ariely
Published 3 Apr 2013

* * * Supersizing the Incentive I should probably tell you now that we didn’t start out running our experiments in the way I just described. Initially, we set about to place some extra stress on our participants. Given our limited research budget, we wanted to create the strongest incentive we could with the fixed amount of money we had. We chose to do this by adding the force of loss aversion to the mix.* Loss aversion is the simple idea that the misery produced by losing something that we feel is ours—say, money—outweighs the happiness of gaining the same amount of money. For example, think about how happy you would be if one day you discovered that due to a very lucky investment, your portfolio had increased by 5 percent.

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Contrast that fortunate feeling to the misery that you would feel if, on another day, you discovered that due to a very unlucky investment, your portfolio had decreased by 5 percent. If your unhappiness with the loss would be higher than the happiness with the gain, you are susceptible to loss aversion. (Don’t worry; most of us are.) To introduce loss aversion into our experiment, we prepaid participants in the small-bonus condition 24 rupees (6 times 4). Participants in the medium-bonus condition received 240 rupees (6 times 40), and participants in the very-large-bonus condition were prepaid 2,400 rupees (6 times 400).

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First, it was difficult for me to accept the doctors’ recommendation because of two related psychological forces we call the endowment effect and loss aversion. Under the influence of these biases, we commonly overvalue what we have and we consider giving it up to be a loss. Losses are psychologically painful, and, accordingly, we need a lot of extra motivation to be willing to give something up. The endowment effect made me overvalue my arm, because it was mine and I was attached to it, while loss aversion made it difficult for me to give it up, even when doing so might have made sense. A second irrational influence is known as the status quo bias.

Adaptive Markets: Financial Evolution at the Speed of Thought
by Andrew W. Lo
Published 3 Apr 2017

It became obvious to them that, when people were faced with economic choices with uncertain outcomes, they had peculiar but systematic biases in their behavior. Kahneman and Tversky set out to test these systematic biases in an experimental setting. From a financial perspective, one of the most important of these biases is called loss aversion. When we make choices involving risky outcomes, most of us place greater weight on losses than on gains. We’re much more averse to losing in a risky situation than simple mathematics would predict. Loss aversion is so embedded in our behavior that it can be difficult for us to see. Kahneman and Tversky brought it to light by ruthlessly stripping the behavior down to its bare minimum in an experimental setting.

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Some economists claim that regulatory forbearance is partly responsible for the recent financial crisis,14 offering elaborate explanations for why regulatory forbearance might occur, such as global competition among regulatory agencies and the political economy of regulation.15 But a more mundane explanation is loss aversion: a sure loss to the regulator if she calculates that a bank’s assets have declined, and a riskier but less psychologically painful alternative if she maintains the older, higher estimate. Although we still have much to learn about the behavior of bank supervisors and other financial regulators in the years leading up to the financial crisis,16 we shouldn’t dismiss the possibility that they didn’t react sooner simply because they were too human. PROBABILITY MATCHING AND MARCH MADNESS Loss aversion is only one of many behavioral biases discovered by psychologists like Tversky and Kahneman.

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When it comes to losses, most people are willing to take much greater risks to avoid losses, even if those risks aren’t compensated by higher expected payoffs. Apparently, a thorn in the hand is worth much less than the possibility of many thorns in the bush if that possibility also includes a chance of avoiding thorns altogether. Why should loss aversion be interesting to anyone other than academics? It’s because this behavior is especially counterproductive in a financial context. To see why, take the combination of Alfa and Delta, the two choices most people pick. This pair is equivalent to a single investment that pays $240,000 with 25 percent probability and loses $760,000 with 75 percent probability (Alfa yields $240,000 for sure, and with 25 percent probability Delta loses nothing in which case the $240,000 is yours to keep, but with 75 percent probability Delta loses $1 million in which case your total earnings are $240,000 minus $1 million for a net loss of $760,000).

The Bogleheads' Guide to Investing
by Taylor Larimore , Michael Leboeuf and Mel Lindauer
Published 1 Jan 2006

But when it comes to investing your hard-earned dollars, please keep this thought in mind: the stock market is a very expensive place to learn that neither you nor anyone else is endowed with the gift of investment prophecy. Loss Aversion Do you check your portfolio every day? Do you sell a stock or mutual fund when it earns a healthy return to lock in the profit? Do you sell stocks/mutual funds whenever you see them going down? Are you a young person who keeps most of your savings in bonds or safe, ultraconservative investments? If so, you may be hurting your potential returns through loss aversion. Loss aversion is the flip side of overconfidence. Although overconfidence tends to make us overly bold, loss aversion makes us overly timid about investing. Experiments have determined that at the emotional level, we feel the pain of a $100 loss twice as much as we enjoy the benefit of a $100 gain.

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Perhaps you know people who lost a fortune in the stock market crash of 1929, the 1973 to 1974 bear market, or the tech wreck of 2000 who now keep all their money in bank certificates of deposit. They may think their investments are risk free. However, if you factor in the taxes due on the interest earned and inflation, many of them are actually losing purchasing power. What's perceived as safe isn't always as safe as those who are loss averse believe it to be. Paralysis by Analysis This investment trap is the first cousin to loss aversion. When it comes time to invest, we have literally thousands of funds to choose from and an abundance of noise telling us why we should invest in a certain way. The more choices people are given, the harder it becomes to choose one. As a result, some people don't make a choice and don't invest.

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HOW TO ESCAPE THE EMOTIONAL TRAPS Finally, for the common emotional traps mentioned earlier, we offer the following tools for escape: • Recency bias. Never assume today's results predict tomorrow's. It's a changing world. • Overconfidence. No one can consistently predict short-term movements in the market. This means you and/or the person investing your money. • Loss aversion. Be a risk manager instead of a risk avoider. Believing you are avoiding risk can be a costly illusion. • Paralysis by analysis. Every day you don't invest is a day less you'll have the power of compounding working for you. Put together an intelligent investment plan and get started. If you need help, seek out a good financial planner to assist you

Smart Money: How High-Stakes Financial Innovation Is Reshaping Our WorldÑFor the Better
by Andrew Palmer
Published 13 Apr 2015

So the behavioralists have had to fight back with another clever technique known as “auto escalation,” an opt-in feature that automatically increases employees’ savings rates whenever they get a pay rise until they hit a maximum level of contributions.17 By synchronizing contribution increases and salary hikes, this “Save More Tomorrow” option also gets around another human foible known as “loss aversion,” which means that people weigh losses more heavily than gains. Loss aversion appears to have very deep neurological roots. In a 2005 study, a trio of academics from Yale introduced a colony of capuchin monkeys to the concept of money. The monkeys were first trained to understand that they could exchange a token for food. Having grasped its purpose, some familiar patterns of behavior emerged.

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People who are paid to think about the industry’s future wonder about the potential for an “old people’s bank” for the baby boomers, whose services would be explicitly based around budgeting, managed drawdowns of savings, and the like. In the meantime, the annuity is the obvious answer to the problem of not knowing when you are going to die, but it has its flaws. If you have a small pension pot, an annuity may deliver only a meager stream of income; fixed annuities offer no protection against the effect of inflation; and loss aversion means that people hate the idea of “losing out” to the annuity provider if they die early. Another option is to squeeze more juice out of the assets that older people do own. The biggest of those is likely to be their houses. In 2009 half of home owners aged sixty-two and older in the United States had at least 55 percent of their net worth tied up in home equity.

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Flowers, 69, 81 Japan, banking crisis in, 75 Japan, financial innovation in, 27, 29, 39–40 Jha, Saumitra, 27 Jiménez-Martín, Sergi, 73 Job creation, young small firms and, 147–148 Joint-stock firms, 23 JPMorgan, 77, 169 Jump-to-default risk, 238 Käärmann, Kristo, 190 Kabbage, 218 Kahneman, Daniel, 47, 137 Kanjorski, Paul, 145 Kauffman Foundation, 158 Kennedy, John F., 32 Keys, Benjamin, 48 Kharroubi, Enisse, 79 Kickstarter, 172 King, Stephen, 99 Klein, David, 182 Krugman, Paul, xv Lahoud, Sal, 166 Lang, Luke, 153, 161–162 Laplanche, Renaud, 179, 184, 188, 190, 193–194, 196–197 Latency, 53 Law of large numbers, 17 Layering, 57 Left-digit bias, 46 Lehman Brothers, x, 44, 65 Lending direct, 84 marketplace, 184 payday, 200 relationship-based, 11, 151, 206–208 secured, xiv, 76 unsecured, 206 See also Loans; Peer-to-peer lending Lending Club, 172, 179–180, 182–184, 187, 189, 194–195, 197 Leonardo of Pisa (Fibonacci), 19 Lerner, Josh, 59 Lethal pandemic, risk-modeling for demographic profile, 230 exceedance-probability curve, 231–232, 232 figure 3 historical data, 228–229 infectiousness and virulence, 229–230 location of outbreak, 230–231 Leverage, 51, 70–71, 80, 186, 188 Leverage ratio, 76–77 Lewis, Michael, 57 Liber Abaci or Book of Calculation (Fibonacci), 19 LIBOR (London Interbank Offered Rate), 41 Liebman, Jeffrey, 98 Life expectancy government reaction to, 128–129 projections of, 124–127, 126 figure 2 ratio of young to older people, 127–128 Life-insurance policies, 142 Life-settlements industry, 142–143 Life table, 20 Limited liability, 212 Liquidity, 12–14, 39, 185–186 List, John, 109 The Little Book of Behavioral Investing (Montier), 156 Lo, Andrew, 113–115, 117–123 Loans low-documentation, 48–49 secured, 76 small business, 181, 216 student, 164, 166–167, 169–171, 182 syndicated, 41 Victory Loans, 28 See also Lending; Peer-to-Peer lending Logistic regression, 201 London, early fire insurance in, 16–17 London, Great Fire of, 16 London Interbank Offered Rate (LIBOR), 41 Long-Term Capital Management, 123 Longevity, betting on, 143–144 Loss aversion, 136 Lotteries, 212, 213 Low-documentation loans, 48–49 Lumni, 165, 168, 175 Lustgarten, Anders, 111 Lynn, Jeff, 160–161 Mack, John, 180 Mahwah, New Jersey, 52, 53 Marginal borrowers assessment of, 216–217 behavioral finance and, 208–214 industrialization of credit, 206 microfinance and, 203 savings schemes, 209–214 small businesses, 215–219 unsecured lending to, 206 Wonga, 203, 205, 208 Marginal borrowers (continued) ZestFinance, 199, 202, 205–206 Maritime piracy, solutions to, 151–152 Maritime trade, role of in history of finance, 3, 7–8, 14, 17, 23 Market makers, 15–16, 55 MarketInvoice, 195, 207, 217–218 Marketplace lending, 184 Markowitz, Harry, 118 Massachusetts, use of inflation-protected bonds in, 26 Massachusetts, use of social-impact bonds in, 98 Matching engine, 52 Maturity transformation, 12–13, 187–188, 193 McKinsey & Company, ix, 42 Mercator Advisory Group, 203 Merrill, Charles, 28 Merrill, Douglas, 199, 201 Merrill Lynch, 28 Merton, Robert, 31, 113–114, 123–124, 129–132, 142, 145 Mian, Atif, 204 Michigan, University of, financial survey by, 134–135 Microfinance, 203 Micropayment model, 217 Microwave technology, 53 The Million Adventure, 213–214 Minsky, Hyman, 42 Minsky moment, 42 Mississippi scheme, 36 Mitchell, Justin, 166–167 Momentum Ignition, 57 Monaco, modeling risk of earthquake in, 227 Money, history of, 4–5 Money illusion, 73–74 Money laundering, 192 Money-market funds, 43, 44 Monkeys, Yale University study of loss aversion with, 136 Montier, James, 156–157 Moody, John, 24 Moody’s, 24, 235 Moore’s law, 114 Morgan Stanley, 188 Mortgage-backed securities, 49, 233 Mortgage credit by ZIP code, study of, 204 Mortgage debt, role of in 2007–2008 crisis, 69–70 Mortgage products, unsound, 36–37 Mortgage securitization, 47 Multisystemic therapy, 96 Munnell, Alicia, 129 Naked credit-default swaps, 143 Nature Biotechnology, on drug-development megafunds, 118 “Neglected Risks, Financial Innovation and Financial Fragility” (Gennaioli, Shleifer, and Vishny), 42 Network effects, 181 New York, skyscraper craze in, 74–75 New York City, prisoner-rehabilitation program in, 108 New York Stock Exchange (NYSE), 31, 52, 53, 61, 64 New York Times, Merrill Lynch ad in, 28 Noncorrelated assets, 122 Nonprofits, growth of in United States, 105–106 Northern Rock, x NYMEX, 60 NYSE Euronext, 52 NYSE (New York Stock Exchange), 31, 52, 53, 61, 64 OECD (Organization for Economic Co-operation and Development), 128, 147 Oldfield, Sean, 67–68, 80–84 OnDeck, 216–218 One Service, 94–95, 105, 112 Operating expense ratio, 188–189 Options, 15, 124 Order-to-trade ratios, 63 Oregon, interest in income-share agreements, 172, 176 Organization for Economic Co-operation and Development (OECD), 128, 147 Overtrading, 24 Packard, Norman, 60 Pandit, Vikram, 184 Park, Sun Young, 233 Partnership mortgage, 81 Pasion, 11 Pave, 166–168, 173, 175, 182 Payday lending Consumer Financial Protection Bureau, survey on, 200 information on applicants, acquisition of, 202 underwriting of, 201 PayPal, 219 Peak child, 127 Peak risk, 228 Peer-to-peer lending advantages of, 187–189 auction system, 195 big investors in, 183 borrowers, assessment of, 197 in Britain, 181 commercial mortgages, 181 CommonBond, 182, 184, 197 consumer credit, 181 diversification, 196 explained, 180 Funding Circle, 181–182, 189, 197 investors in, 195 Lending Club, 179–180, 182–184, 187, 189, 194–195, 197 network effects, 181 ordinary savers and, 184 Prosper, 181, 187, 195 RateSetter, 181, 187, 196 Relendex, 181 risk management, 195–197 securitization, 183–184, 196 Peer-to-peer lending (continued) small business loans, 181 SoFi, 184 student loans, 182 Zopa, 181, 187, 188, 195 Pensions, cost of, 125–126 Perry, Rick, 142–143 Peterborough, England, social-impact bond pilot in, 90–92, 94–95, 104–105, 112 Petri, Tom, 172 Pharmaceuticals, decline of investment in, 114–115 Piracy Reporting Centre, International Maritime Bureau, 151 Polese, Kim, 210 Poor, Henry Varnum, 24 “Portfolio Selection” (Markowitz), 118 Prediction Company, 60–61 Preferred shares, 25 Prepaid cards, 203 Present value of cash flows, 19 Prime borrowers, 197 Prince, Chuck, 50–51, 62 Principal-agent problem, 8 Prisoner rehabilitation programs, 90–91, 94–95, 98, 108, 112 Private-equity firms, 69, 85, 91, 105, 107 Projection bias, 72–73 Property banking crises and, xiv, 69 banking mistakes involving, 75–80 behavioral biases and, 72–75 dangerous characteristics of, 70–72 fresh thinking, need for, xvii, 80 investors’ systematic errors in, 74–75 perception of as safe investment, 76, 80 Prosper, 181, 187, 195 Provisioning funds, 187 Put options, 9, 82 Quants, 19, 63, 113 QuickBooks, 218 Quote stuffing, 57 Raffray, André-François, 144 Railways, affect of on finance, 23–25 Randomized control trials (RCTs), 101 Raphoen, Christoffel, 15–16 Raphoen, Jan, 15–16 RateSetter, 181, 187, 196 RCTs (randomized control trials), 101 Ready for Zero, 210–211 Rectangularization, 125, 126 figure 2 Regulation NMS, 61 Reinhart, Carmen, 35 Reinsurance, 224 Relendex, 181 Rentes viagères, 20 Repurchase “repo” transactions, 15, 185 Research-backed obligations, 119 Reserve Primary Fund, 44 Retirement, funding for anchoring effect, 137–138 annuities, 139 auto-enrollment in pension schemes, 135 auto-escalation, 135–136 conventional funding, 127–128 decumulation, 138–139 government reaction to increased longevity, 128–129 home equity, 139–140 life expectancy, projections of, 124–127, 126 figure 2 life insurance policies, cash-surrender value of, 142 personal retirement savings, 128–129, 132–133 replacement rate, 125 reverse mortgage, 140–142 savings cues, experiment with, 137 SmartNest, 129–131 Reverse mortgages, 140–142 Risk-adjusted returns, 118 Risk appetite, 116 Risk assessment, 24, 45, 77–78, 208 Risk aversion, 116, 215 Risk-based capital, 77 Risk-based pricing model, 176 Risk management, 55, 117–118, 123, 195–197 Risk Management Solutions, 222 Risk sharing, 8, 82 Risk-transfer instrument, 226 Risk weights, 77–78 Rogoff, Kenneth, 35 “The Role of Government in Education” (Friedman), 165 Roman Empire business corporation in, 7 financial crisis in, 36 forerunners of banks in, 11 maritime insurance in, 8 Rotating Savings and Credit Associations (ROSCAs), 209–210 Roulette wheel, use of in experiment on anchoring, 138 Royal Bank of Scotland, 186 Rubio, Marco, 172 Russia, mortgage market in, 67 S-curve, in diffusion of innovations, 45 Salmon, Felix, 155 Samurai bonds, 27 Satsuma Rebellion (1877), 27 Sauter, George, 58 Save to Win, 214 Savings-and-loan crisis in US (1990s), 30 Savings cues, experiment with, 137 Scared Straight social program, 101 Scholes, Myron, 31, 123–124 Science, Technology, and Industry Scoreboard of OECD, 147 Securities and Exchange Commission (SEC), 54, 56, 57, 58, 64 Securities markets, 14 Securitization, xi, 20, 37–38, 117–122, 183–184, 196, 236 Seedrs, 160–161 Sellaband, 159 Shared equity, 80–84 Shared-equity mortgage, 84 Shepard, Chris, xii–xiii Shiller, Robert, xv–xvi, 242 Shleifer, Andrei, 42, 44 Short termism, 58 SIBs.

Investing Amid Low Expected Returns: Making the Most When Markets Offer the Least
by Antti Ilmanen
Published 24 Feb 2022

Consequences of Impatience There are three main ways impatience can hurt an investor's bottom-line: missing out on long-run expected returns; costs and frictions from excessive strategy or manager turnover; and any mean reversion in strategy or manager performance. Loss averse investors may miss out on long-term rewarded exposures if they disinvest after a bad experience, or never gain exposure for fear of such experiences. By forgoing rewarded risks, loss averse investors actually increase the risk of not earning enough to achieve long-term goals. But realized losses tend to be felt more keenly than opportunity losses. The costs of excessive turnover include transaction costs, operational costs, and redemption fees.

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Turning to behavioral premia, the behavioral finance literature has been criticized for offering too many distinct “just-so” stories instead of one comprehensive theory (e.g. Fama (1998)). The closest to meeting this challenge is the prospect theory by Kahneman-Tversky (1979), which encompasses many features: preferences on gains and losses – thus implying narrow framing; loss aversion (moderated by diminishing sensitivity to gains or losses); and overweighting low-probability events. These features have been studied separately in many papers, while the broadest empirical study of its investment implications is in Barberis-Jin-Wang (2020). Several excellent surveys have given structure to the behavioral finance literature.8 I briefly state three key areas: Biased beliefs (related to extrapolation, overconfidence, availability heuristic, anchoring, conservatism, confirmation bias, hindsight, etc.)

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Several excellent surveys have given structure to the behavioral finance literature.8 I briefly state three key areas: Biased beliefs (related to extrapolation, overconfidence, availability heuristic, anchoring, conservatism, confirmation bias, hindsight, etc.) Non-standard preferences (within the prospect theory: loss aversion (part of narrow framing) and simultaneous insurance and lottery preferences (part of probability weighting); elsewhere leverage aversion, regret aversion, ambiguity aversion, home bias, impact of moods/sentiment, etc.) Cognitive limits (bounded rationality) In addition, there are the limits of arbitrage sustaining all anomalies caused by these forces.

A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing
by Burton G. Malkiel
Published 10 Jan 2011

Expected value ½($250) + ½ (–$100) $75. Kahneman and Tversky concluded that losses were 2½ times as undesirable as equivalent gains were desirable. In other words, a dollar loss is 2½ times as painful as a dollar gain is pleasurable. People exhibit extreme loss aversion, even though a change of $100 of wealth would hardly be noticed for most people with substantial assets. We’ll see later how loss aversion leads many investors to make costly mistakes. Interestingly, however, when individuals faced a situation where sure losses were involved, the psychologists found that they were overwhelmingly likely to take the gamble. Consider the following two alternatives: 1.

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REAPING REWARD BY INCREASING RISK Beta and Systematic Risk The Capital-Asset Pricing Model (CAPM) Let’s Look at the Record An Appraisal of the Evidence The Quant Quest for Better Measures of Risk: Arbitrage Pricing Theory The Fama-French Three-Factor Model A Summing Up 10. BEHAVIORAL FINANCE The Irrational Behavior of Individual Investors Overconfidence Biased Judgments Herding Loss Aversion Pride and Regret Behavioral Finance and Savings The Limits to Arbitrage What Are the Lessons for Investors from Behavioral Finance? 1. Avoid Herd Behavior 2. Avoid Overtrading 3. If You Do Trade: Sell Losers, Not Winners 4. Other Stupid Investor Tricks Does Behavioral Finance Teach Ways to Beat the Market?

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Moreover, people deviate in systematic ways from rationality, and the irrational trades of investors tend to be correlated. Behavioral finance then takes that statement further by asserting that it is possible to quantify or classify such irrational behavior. Basically, there are four factors that create irrational market behavior: overconfidence, biased judgments, herd mentality, and loss aversion. Well, yes, believers in efficient markets say. But—and we believers always have a but—the distortions caused by such factors are countered by the work of arbitrageurs. This last is the fancy word used to describe people who profit from any deviation of market prices from their rational value.

The Moral Landscape: How Science Can Determine Human Values
by Sam Harris
Published 5 Oct 2010

It is also an important impediment to conflict resolution through negotiation: for if each party values his opponent’s concessions as gains and his own as losses, each is bound to perceive his sacrifice as being greater.34 Loss aversion has been studied with functional magnetic resonance imaging (fMRI). If this bias were the result of negative feelings associated with potential loss, we would expect brain regions known to govern negative emotion to be involved. However, researchers have not found increased activity in any areas of the brain as losses increase. Instead, those regions that represent gains show decreasing activity as the size of the potential losses increases. In fact, these brain structures themselves exhibit a pattern of “neural loss aversion”: their activity decreases at a steeper rate in the face of potential losses than they increase for potential gains.35 There are clearly cases in which such biases seem to produce moral illusions—where a person’s view of right and wrong will depend on whether an outcome is described in terms of gains or losses.

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The Monist, 59 (2), 204–217. Tiihonen, J., Rossi, R., Laakso, M. P., Hodgins, S., Testa, C., Perez, J., et al. (2008). Brain anatomy of persistent violent offenders: More rather than less. Psychiatry Res, 163 (3), 201–212. Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315 (5811), 515–518. Tomasello, M. (2007, January 13). For human eyes only. New York Times. Tomlin, D., Kayali, M. A., King-Casas, B., Anen, C., Camerer, C. F., Quartz, S. R., et al. (2006). Agent-specific responses in the cingulate cortex during economic exchanges.

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See also New Atheists; secular liberals Atran, Scott, 155–56, 205n28 “autonomous hand,” 216n104 aversive sounds, 77 Babiak, P., 214n87 Bad Life versus Good Life, 15–21, 38–42 Ball, Philip, 137–39 Barrett, Justin, 151 Bechara, A., 228n61 beehive approach to morality, 89 belief: adoption of, for feeling better, 137–39 bias and, 122–26, 137, 226n36 brain science on, 11, 14, 116–22, 197n22 definitions of, 117 different categories of, 139–40 ethical beliefs, 14 extraneous information and/or context as influence on, 140–42 factual beliefs, 14 freedom of, 136–44 inseparability of reasoning and emotion, 126–31 internet’s influence on, 123 as intrinsically epistemic, 138 knowledge as, 115, 196–97n22 lie detection and, 133–36 meaning of, 115–18, 219–20n15 memory and, 116 mental properties of, 136–40 motivation for, 126 reasoning and, 122, 131–33 religious belief, 137–38, 148–54 science and, 144 wrong beliefs, 21 Benedict, Pope, 200n14 Benedict, Ruth, 20, 60–62 Bentham, Jeremy, 207n12 bias: adaptive fitness versus, 226–27n38 belief and, 122–26, 137, 226n36 decisional conflict, 231n75 definition of, 132 endowment effect and, 75 factors causing, 226n36 internet’s influence on, 123 knowledge and, 123–24 of liberals, 125–26 loss aversion, 75–77, 209n35 medical decisions and, 143, 231n75 of parents, 73 of political conservatives, 124–25 in reasoning, 132, 142–43 of science, 47 sins of commission versus sins of omission, 77 truth bias, 120, 223n26 unconscious and, 122–23 Bible, 3, 34, 38, 150, 166, 236–37n82 Biblical Creationism, 34, 37, 151, 202n19 Bin Laden, Osama, 5 Bingham, Roger, 5–6, 23 BioLogos Foundation, 169 birth control.

Little Bets: How Breakthrough Ideas Emerge From Small Discoveries
by Peter Sims
Published 18 Apr 2011

Jerry Seinfeld: Drawn from The Comedian (DVD), Directed by Christian Charles, with Jerry Seinfeld (2002). John Legend and Kevin Brereton: Interviews with Legend and Brereton. Status quo bias and loss aversion: Origin of status quo bias terminology and research: “Status Quo Bias in Decision Making,” by William Samuelson and Richard Zeckhauser, Journal of Risk and Uncertainty, vol. 1, 1988, 7–59. Addition of loss aversion and endowment effect: “The Endowment Effect, Loss Aversion, and Status Quo Bias,” by Daniel Kahneman, Jack L. Knetsch, Richard H. Thaler, Journal of Economic Perspectives, vol. 5, 193–206. “Timid Choices and Bold Forecasts,” by Daniel Kahneman and Dan Lavallo, Management Science, 39, 17–31.

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and “why not?” The authors wrote that the innovators steer “entirely clear” of what’s called the status quo bias. This research demonstrates that people do not like to change unless there is a compelling reason to do so, such as an attractive incentive. Related research shows that people exhibit strong “loss aversion,” in that they are twice as likely to seek to avoid losses as they are to acquire gains. The researchers who discovered this phenomenon found that people wanted to be able to gain at least forty dollars on a coin toss before they risked losing twenty dollars, a roughly two to one (fear-of-loss to pleasure-of-gain) ratio.

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I., 99, 101 Lead users, 133–40 Legend, John, 109, 115, 134 Lehrer, Jonah, 66 Leibovitz, Annie, 126 Letterman, David, 3 Liker, Jeffrey, The Toyota Way, 167 Limb, Charles, 65–67 Lincoln Center, New York, 79, 80 Listening, 97–116 Little bets, 1, 8, 154 active users, 133–40 big bets vs., 19–33 failing quickly to learn fast, 51–64 growth mind-set and, 35–49 learning a lot from a little, 131–40 openness to experience and, 117–30 play and, 65–76 prototyping and, 52–64 questions and, 97–116 smallifying problems and, 77–95 small wins and, 141–52 London, 109 Los Angeles, 78–83, 111, 112, 161 Los Angeles Philharmonic, 80, 83 Loss aversion, 110 Lucas, George, 30, 31, 143 Lucasfilm, 30 Luck, 121–24, 129 making your own, 124–29 network of, 124 Luxo Jr. (film), 143–44 MacFarland, Sean, 94–95 Macintosh, 108 Maeda, John, The Laws of Simplicity, 170 Malaria, 9 Management by walking around, 120–21 Manufacturing, 15 Marketing, 4–5, 6, 20, 21 Market research, 21–22, 111, 135 Martin, Roger, 63 The Design of Business, 172 Mayer, Marissa, 78 McCain, John, 125 McEnroe, John, 37, 38 McGrath (Rita Gunther) and MacMillan (Ian C.), Discovery-Driven Growth, 174 McMaster, H.

The Gig Economy: The Complete Guide to Getting Better Work, Taking More Time Off, and Financing the Life You Want
by Diane Mulcahy
Published 8 Nov 2016

Why does it hold us back and keep us small even when the opportunity for something bigger and better lies right in front of us? One reason is that we let our fears fester and grow, unchallenged, in our heads. We don’t examine them in the cold light of day and see what they’re made of. Another reason is that we are loss averse. We feel the pain of loss much more than the pleasure of gain. We pay greater attention to potential losses than gains, and we’re inclined to avoid possible setbacks more than we are to seek potential wins. Facing Fear It’s easiest to deconstruct our fears and identify our risks if we see them in front of us.

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Just having better information can help Beth overcome some of her fear. We’re not very good at assessing our fears and risks accurately. Our assessment of risk is distorted by a series of cognitive biases like overconfidence (which researchers implicate in gambling), anchoring (we assess gains and losses depending on how they are framed), and loss aversion (we hate losses more than we love equivalent gains). These cognitive biases can cause us to either overestimate or underestimate risk and make (sometimes big) decisions based on our inaccurate perception. We tend to hold unfounded fears about events and outcomes that are unlikely yet seem oblivious to the very real risks in our everyday lives.

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We’re much more likely to waste our time than our money because we have a higher level of pain over losing money than over “losing” or wasting time. For example, tossing a fifty-dollar bill into the fireplace to burn causes us pain over the loss of the fifty dollars, but if we waste an hour in front of that same fire watching cat videos on Facebook, we don’t feel the same level of pain. It doesn’t make sense that we feel less loss aversion to wasting time than money because, unlike money, our time here on Earth is so limited. Except for a few tweaks around the edges, like wearing our seatbelts, not smoking, and taking other life-prolonging steps, there’s not much we can do to create more of it. We can’t bank our time and save it for use later like we can our money.

Skin in the Game: Hidden Asymmetries in Daily Life
by Nassim Nicholas Taleb
Published 20 Feb 2018

Cognitive dissonance (a psychological theory by Leon Festinger about sour grapes, by which people, in order to avoid inconsistent beliefs, rationalize that, say, the grapes they can’t reach got to be sour). It is seen first in Aesop, of course, repackaged by La Fontaine. But its roots look even more ancient, with the Assyrian Ahiqar of Nineveh. Loss aversion (a psychological theory by which a loss is more painful than a gain is pleasant): in Livy’s Annals (XXX, 21) Men feel the good less intensely than the bad.fn6 Nearly all the letters of Seneca have some element of loss aversion. Negative advice (via negativa): We know the wrong better than what’s right; recall the superiority of the Silver over the Golden Rule. The good is not as good as the absence of bad,fn7 Ennius, repeated by Cicero.

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The flaw in psychology papers is to believe that the subject doesn’t take any other tail risks anywhere outside the experiment and, crucially, will never again take any risk at all. The idea in social science of “loss aversion” has not been thought through properly—it is not measurable the way it has been measured (if it is at all measurable). Say you ask a subject how much he would pay to insure a 1 percent probability of losing $100. You are trying to figure out how much he is “overpaying” for “risk aversion” or something even more foolish, “loss aversion.” But you cannot possibly ignore all the other financial risks he is taking: if he has a car parked outside that can be scratched, if he has a financial portfolio that can lose money, if he has a bakery that may risk a fine, if he has a child in college who may cost unexpectedly more, if he can be laid off, if he may be unexpectedly ill in the future.

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Languages The One-Way Street of Religions Decentralize, Again Imposing Virtue on Others Stability of the Minority Rule, a Probabilistic Argument Popper-Goedel’s Paradox Irreverence of Markets and Science Unus sed Leo: Only One but a Lion Summary and Next Appendix to Book 3: A Few More Counterintuitive Things About the Collective Zero-Intelligence Markets BOOK 4: WOLVES AMONG DOGS Chapter 3. How to Legally Own Another Person To Own a Pilot From the Company Man to the Companies Person Coase’s Theory of the Firm Complexity A Curious Form of Slave Ownership Freedom is Never Free Wolves Among the Dogs Loss Aversion Waiting for Constantinople Do Not Rock Bureaucristan Next Chapter 4. The Skin of Others in Your Game A Mortgage and Two Cats Finding Hidden Vulnerabilities How to Put Skin in the Game of Suicide Bombers Next BOOK 5: BEING ALIVE MEANS TAKING CERTAIN RISKS Chapter 5. Life in the Simulation Machine Jesus Was a Risk Taker Pascal’s Wager The Matrix The Donald Next Chapter 6.

Investment: A History
by Norton Reamer and Jesse Downing
Published 19 Feb 2016

There were some who suggested that it was merely survivorship bias that explained this phenomenon; that is, there were stocks that went bankrupt or otherwise delisted and so this high premium was not real after all.48 Others suggested that there were frictions unaccounted for, such as transaction costs. The behavioral economists mounted a different set of explanations. One of the most cited and well-regarded explanations, put forth by Shlomo Benartzi and Richard Thaler in 1995, is “myopic loss aversion,” a notion that borrows heavily from the concepts developed in prospect theory, including the fact that individuals tend to exhibit loss aversion and that they care about changes in wealth more keenly than about absolute levels of wealth. Investors who frequently look at the value of their equity portfolio—say, on a daily or weekly basis when the market behaves randomly over these short time frames, moving up and down—will thus experience more disutility on average, given that they derive greater pain from losses than pleasure from the same magnitude of gains.

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Investors who frequently look at the value of their equity portfolio—say, on a daily or weekly basis when the market behaves randomly over these short time frames, moving up and down—will thus experience more disutility on average, given that they derive greater pain from losses than pleasure from the same magnitude of gains. Over long evaluation periods, The Emergence of Investment Theory 253 however, where market movements have a general upward trend, this feeling of loss aversion is reduced because equities tend to appreciate over time, so it is more palatable to hold on to equities. The size of the equity premium, then, is really due to loss aversion experienced by investors whose frequency of evaluations is too great; if investors looked at their equities portfolios over longer time frames, they would demand lower premiums and this puzzle would be resolved.49 Other explanations that have been offered by behavioral economists focus on earnings uncertainty and how that influences investors’ willingness to bear risk, and yet others develop a dynamic loss aversion model where investors react differently to stocks that fall after a run-up compared to those that fall directly after purchase.

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Their pioneering paper noted many 252 Investment: A History of the known behaviors that represent aberrations from expected utility theory, including lottery problems (in which individuals tend to elect a lump-sum payment up front even if that is smaller than the expected value of receiving a larger amount or zero when a coin flip is involved) and probabilistic insurance (in which individuals have a more disproportionate dislike for a form of insurance that would cover losses based on a coin flip more than the math suggests they should). Prospect theory contends that individuals’ choices are more centered on changes in utility or wealth rather than end values; it also suggests that most people exhibit loss aversion in which losses cause more harm to one’s welfare than the benefit from happiness one receives from gaining the same amount of reward.46 This theory may seem intellectually interesting, but how does it relate precisely to finance and investing? Since Kahneman and Tversky’s seminal paper, subsequent work has made many connections to markets, one of which is the “equity premium puzzle.”

The Choice Factory: 25 Behavioural Biases That Influence What We Buy
by Richard Shotton
Published 12 Feb 2018

Simply promoting the end date would boost the attendees in the last few weeks. One explanation for the appeal of scarce items is loss aversion – that people feel losses more powerfully than the same level of gains. Stressing the end date taps into this by emphasising that consumers are in danger of missing the opportunity. Brands can apply this bias by tweaking their copy: rather than promoting their benefits, focus on what is being missed out by not switching. Working with Gabrielle Hobday, I surveyed 834 respondents to see how loss aversion could influence advertising claims. Half were told they could save £100 by switching to a new energy provider, while the remainder were informed they stood to lose £100 if they didn’t switch.

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Index 118 118 ad campaigns ad design ad messaging advertising advertising expenditure Alter, Adam anchoring Anderson, Eric Apple iPod Ariely, Dan Aristotle Arkes, Hal Aronson, Elliot Asch, Solomon auctions Audi Avis Banks, Iain banner ads Batey, Ifan Batson, Daniel behaviour contexts Behavioural Insight Team behavioural science Bernbach, Bill biases Binet, Les Black Sheep Vodka Borges, Jorge Louis brand flaws brand ideals brand measurement brand purpose brand reviews brand tracking brands Brink, Lois British Airways ads broken escalator phenomenon Bronner, Fred Brooks, Roy Brown, Millward brownies example browser choice Bruner, Jerome budget airlines budget setting see advertising expenditure Bullmore, Jeremy business growth business success Busse, Meghan bystander effect camera experiment Camerer, Colin Campbell’s soup experiment canned laughter Cantril, Hadley car deals examples card payments category norms CBS Outdoor and TNS cell methodology charity campaigns charm pricing Cherry, Colin Christiaensen, Luc Cialdini, Robert Cimbalo, Richard cinema ads claimed data Clay, Richard cocktail party effect cognitive illusions coin exaggeration Comparethemarket comparison sets confirmation bias Confused consumer overconfidence consumers habits contactless cards contexts behaviour media target cookies example Copernicus Consulting copywriting Corrigan, Spencer Costa coffee credit cards customer reviews Darley, John De Beers diamonds de Crane, Anton Deaux, Kay Deppe, Michael descriptive norms diamonds see De Beers diamonds diary-based experiment digital ads digital brand tracking direct mail discounts distinctiveness distraction dress sales experiment Dunning, David Dzamic, Lazar Ebbinghaus, Herman Eno, Brian estate agents ethics evaluation expectancy theory explicit messages fake beer experiment fake brands Fanelli, Daniele Ferguson, Alex Festinger, Leon Field, Peter first impressions football sponsorships Ford found data fundamental attribution error general election, UK Genovese, Kitty Genovese syndrome see bystander effect Gilbert, Dan Give Blood campaign, NHS gluten-free products Gocompare Goldstein, Noah good mood Goodhart’s Law Goodman, Cecile Gossett, William Sealy green goods Griffiths, Dylan Griskevicus, Vladas groups Guinness Gumroad habits Haidt, Jonathan Halifax Halpern, David Harford, Tim Hastorf, Albert heavy buyers Hegarty, John Hershfield, Hal Hobday, Gabrielle Hoffman, Bob Housman, Michael injunctive norm IPA Effectiveness Databank isolation effect JC Decaux Jenkins, Jeff Jones, John Joyner, Cynthia Jung, Carl Kahneman, Daniel Kandasamy, Anna Kay, John King Cobra beer knowledge Kruger, Justin labelling lager choice example Larrey, Dominique Jean Latané, Bibb leading brands Leahy, Terry Levenson, Bob lies life events Linford, Claire localisation loss aversion Lumen Research Maccoby, Nathan Maclean, Laura Magners cider male incontinence brand Manchester United Many Labs Replication Project market leaders marketing marketing managers marketing triage Martin, Steve maximisers McDonald’s media contexts medical practice method planning mimicry Mirenberg, Matthew Moneysupermarket mood Moseley, Winston motivation music musicians National Survey of Sexual Attitudes and Lifestyle (NATSAL) Neale, Margaret negative opinions negative social proof Nespresso new energy provider example New Look campaign newspaper ads Newton, Elizabeth NHS Give Blood campaign nine-enders Nokia nominative determinism Northcraft, Gregory Nosek, Brian nudges Nurse Family Partnership observed behaviour observed data Ocado O’Callaghan, Aidan online advertising overconfidence painkillers payment methods contactless cards credit cards Pelham, Brett perfume experiment Perry, Katy personal appeal personalisation persuasion petrified wood experiment placebo effect Polkinghorne, Vic popularity pratfall effect Prelec, Drazen pre-paid gift cards presentation Prezzo, Mark price price illusions price relativity primacy effect principal-agent problem product flaws programmatic advertising promotions Quinn, Jeffrey Raghubir, Priya recall Red Bull rejecters replicability Restorff, Hedwig von Riddell, Jenny Rosenzweig, Phil Ross, Lee Ross, Stephen Rosser Reeves fallacy Rudder, Christian Sainsbury’s satisficers scarcity serial position effect sexism sexual partners Shapiro, Matthew Sharp, Byron Sherif, Muzafer Shiv, Baba Shotton, Richard ad design Black Sheep Vodka brand purpose charity campaigns contactless cards fake beer experiment general election good mood green goods lager choice leading brands life events loss aversion male incontinence brand New Look campaign nine-enders overconfidence perfume experiment product flaws scarcity spending patterns sponsorships thought experiment value perception shower gel example Shteynberg, Garriy Simester, Duncan Simon, Herbert Simonsohn, Uri Simonson, Itamar Slovic, Paul Snickers chocolate bars social media social proof negative spending patterns sponsorships Srivastava, Joydeep Stella Artois Stengel, Jim Stengel Stephens-Davidowitz, Seth Street Bump app, Boston Strong, Rebecca supermarkets surveys Sutherland, Rory Svenson, Ola switched brands tailored approach target audiences target contexts target setting targeting ads tax payments Tesco Thaler, Richard The Wasp Factory (Banks, 1984) thought experiment time frames Total Recall Touchpoints towel experiment tracking data transparency Trott, Dave TV ads Tversky, Amos unintended consequences Vallance, Charles value perception video-conferencing Vietnam visual illusions vitamins example Von Restorff effect VW ad campaign Wansink, Brian waste websites Weston, Laura wheel of fortune example Whiskas Wikipedia Wilcox, Keith winner’s curse wishful seeing Wood, Wendy Worchel, Stephen Yahoo younger age groups Zettelmayer, Florian Zhang, Yong Zinkan, George

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Index 118 118 ad campaigns ad design ad messaging advertising advertising expenditure Alter, Adam anchoring Anderson, Eric Apple iPod Ariely, Dan Aristotle Arkes, Hal Aronson, Elliot Asch, Solomon auctions Audi Avis Banks, Iain banner ads Batey, Ifan Batson, Daniel behaviour contexts Behavioural Insight Team behavioural science Bernbach, Bill biases Binet, Les Black Sheep Vodka Borges, Jorge Louis brand flaws brand ideals brand measurement brand purpose brand reviews brand tracking brands Brink, Lois British Airways ads broken escalator phenomenon Bronner, Fred Brooks, Roy Brown, Millward brownies example browser choice Bruner, Jerome budget airlines budget setting see advertising expenditure Bullmore, Jeremy business growth business success Busse, Meghan bystander effect camera experiment Camerer, Colin Campbell’s soup experiment canned laughter Cantril, Hadley car deals examples card payments category norms CBS Outdoor and TNS cell methodology charity campaigns charm pricing Cherry, Colin Christiaensen, Luc Cialdini, Robert Cimbalo, Richard cinema ads claimed data Clay, Richard cocktail party effect cognitive illusions coin exaggeration Comparethemarket comparison sets confirmation bias Confused consumer overconfidence consumers habits contactless cards contexts behaviour media target cookies example Copernicus Consulting copywriting Corrigan, Spencer Costa coffee credit cards customer reviews Darley, John De Beers diamonds de Crane, Anton Deaux, Kay Deppe, Michael descriptive norms diamonds see De Beers diamonds diary-based experiment digital ads digital brand tracking direct mail discounts distinctiveness distraction dress sales experiment Dunning, David Dzamic, Lazar Ebbinghaus, Herman Eno, Brian estate agents ethics evaluation expectancy theory explicit messages fake beer experiment fake brands Fanelli, Daniele Ferguson, Alex Festinger, Leon Field, Peter first impressions football sponsorships Ford found data fundamental attribution error general election, UK Genovese, Kitty Genovese syndrome see bystander effect Gilbert, Dan Give Blood campaign, NHS gluten-free products Gocompare Goldstein, Noah good mood Goodhart’s Law Goodman, Cecile Gossett, William Sealy green goods Griffiths, Dylan Griskevicus, Vladas groups Guinness Gumroad habits Haidt, Jonathan Halifax Halpern, David Harford, Tim Hastorf, Albert heavy buyers Hegarty, John Hershfield, Hal Hobday, Gabrielle Hoffman, Bob Housman, Michael injunctive norm IPA Effectiveness Databank isolation effect JC Decaux Jenkins, Jeff Jones, John Joyner, Cynthia Jung, Carl Kahneman, Daniel Kandasamy, Anna Kay, John King Cobra beer knowledge Kruger, Justin labelling lager choice example Larrey, Dominique Jean Latané, Bibb leading brands Leahy, Terry Levenson, Bob lies life events Linford, Claire localisation loss aversion Lumen Research Maccoby, Nathan Maclean, Laura Magners cider male incontinence brand Manchester United Many Labs Replication Project market leaders marketing marketing managers marketing triage Martin, Steve maximisers McDonald’s media contexts medical practice method planning mimicry Mirenberg, Matthew Moneysupermarket mood Moseley, Winston motivation music musicians National Survey of Sexual Attitudes and Lifestyle (NATSAL) Neale, Margaret negative opinions negative social proof Nespresso new energy provider example New Look campaign newspaper ads Newton, Elizabeth NHS Give Blood campaign nine-enders Nokia nominative determinism Northcraft, Gregory Nosek, Brian nudges Nurse Family Partnership observed behaviour observed data Ocado O’Callaghan, Aidan online advertising overconfidence painkillers payment methods contactless cards credit cards Pelham, Brett perfume experiment Perry, Katy personal appeal personalisation persuasion petrified wood experiment placebo effect Polkinghorne, Vic popularity pratfall effect Prelec, Drazen pre-paid gift cards presentation Prezzo, Mark price price illusions price relativity primacy effect principal-agent problem product flaws programmatic advertising promotions Quinn, Jeffrey Raghubir, Priya recall Red Bull rejecters replicability Restorff, Hedwig von Riddell, Jenny Rosenzweig, Phil Ross, Lee Ross, Stephen Rosser Reeves fallacy Rudder, Christian Sainsbury’s satisficers scarcity serial position effect sexism sexual partners Shapiro, Matthew Sharp, Byron Sherif, Muzafer Shiv, Baba Shotton, Richard ad design Black Sheep Vodka brand purpose charity campaigns contactless cards fake beer experiment general election good mood green goods lager choice leading brands life events loss aversion male incontinence brand New Look campaign nine-enders overconfidence perfume experiment product flaws scarcity spending patterns sponsorships thought experiment value perception shower gel example Shteynberg, Garriy Simester, Duncan Simon, Herbert Simonsohn, Uri Simonson, Itamar Slovic, Paul Snickers chocolate bars social media social proof negative spending patterns sponsorships Srivastava, Joydeep Stella Artois Stengel, Jim Stengel Stephens-Davidowitz, Seth Street Bump app, Boston Strong, Rebecca supermarkets surveys Sutherland, Rory Svenson, Ola switched brands tailored approach target audiences target contexts target setting targeting ads tax payments Tesco Thaler, Richard The Wasp Factory (Banks, 1984) thought experiment time frames Total Recall Touchpoints towel experiment tracking data transparency Trott, Dave TV ads Tversky, Amos unintended consequences Vallance, Charles value perception video-conferencing Vietnam visual illusions vitamins example Von Restorff effect VW ad campaign Wansink, Brian waste websites Weston, Laura wheel of fortune example Whiskas Wikipedia Wilcox, Keith winner’s curse wishful seeing Wood, Wendy Worchel, Stephen Yahoo younger age groups Zettelmayer, Florian Zhang, Yong Zinkan, George

Walk Away
by Douglas E. French
Published 1 Mar 2011

Most all attempt to negotiate a modification with their lender and are turned away at the door because they are current on their payments or if they are invited to pursue a modification, the “process turns out, however, to be immensely frustrating and ultimately unsuccessful for many homeowners.” Research has shown that investment decisions are driven by biases locked in the human brain and humans are especially loss-averse and tend to rationalize bad investment decisions. David Genesove and Christopher Mayer write in a chapter entitled “Loss-Aversion and Seller Behavior: Evidence from the Housing Market” from Advances In Behavioral Economics, “housing professionals are not surprised that many sellers are reluctant to realize a loss on their house.” These authors found that during the boom and bust in the Boston downtown real estate market of 1990–97, sellers subject to losses set higher asking prices of 25–35% of the difference between the expected selling price of a property and their original purchase price.

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“One especially successful broker even noted that she tried to avoid taking on clients who were facing ‘too large’ a potential loss on their property because such clients often had unrealistic target selling prices,” write Genesove and Mayer. And the cold, hard realities of the market are slow to change sellers’ minds according to Genesove and Mayer. According to their data, lower prices and increased time on the market do not significantly influence loss-aversion. Dražen Prelec and George Lowenstein believe that people do an accounting in their heads that affects their behavior. The linkages tying together specific acts of consumption with specific payments “generates pleasure or pain depending on whether the accounts are in the red or in the black.” In an article entitled “The Red and the Black: Mental Accounting of Savings and Debt” which appeared as a chapter in Exotic Preferences: Behavioral Economics and Human Motivation, the authors’ modeling predicts that most people are debt averse and show “that people generally like sequences of events that improve over time and dislike sequences that deteriorate.”

Luxury Fever: Why Money Fails to Satisfy in an Era of Excess
by Robert H. Frank
Published 15 Jan 1999

If the value people assign to winning such a gamble were on a par with the value they assign to losing, they might nonetheless be inclined to roll the dice. For most people, however, these values are highly discrepant. As a burgeoning literature in psychology and economics has amply demonstrated, people assign much greater weight to losses than to gains of the same magnitude.11 The economist Richard Thaler coined the term loss aversion to describe this tendency. Loss aversion means not just that the pain of losing, say, $1,000, is larger, for most of us, than the pleasure from winning that same amount. It means that it is much larger. Thaler illustrates the asymmetry in our reactions to gains and losses by asking his students to consider the following hypothetical questions: 1.

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Thaler reports that the median responses from his students are approximately $800 for the first question and $100,000 for the second.13 “In general,” Thaler explains, “people seem willing to pay more to keep something already in their possession than to acquire the same item had they not already owned it [in these examples, their good health].”14 Loss aversion also helps us understand why workers expend vastly more effort to avoid a 10 percent cut in pay than to win a 10 percent increase. One important consequence of loss aversion, Friedman argues, is that the overriding goal for a majority of citizens in a stagnant economy becomes the protection of their current positions. In such environments, proposals to reduce employment barriers almost invariably fall on deaf ears.

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Over a much broader range of the existing income distribution, therefore, in a growing economy people will be willing to accept enhanced mobility, and they will support measures like anti-discrimination laws, or special education programs for disadvantaged children, designed to make actual mobility greater.15 The concept of loss aversion also enriches our understanding of the relationship between wealth and the amounts people are willing to spend for environmental cleanup and other public goods. In particular, it suggests that the rate of change of wealth is likely to be important. In a rapidly growing economy, the cost of having a cleaner environment is a slight reduction in the rate of growth of private consumption.

The Joys of Compounding: The Passionate Pursuit of Lifelong Learning, Revised and Updated
by Gautam Baid
Published 1 Jun 2020

Richard Thaler and Cass Sunstein take the idea of aversion to loss one step further.8 They explain that investors also suffer from myopic loss aversion; the more often we evaluate our portfolios, the more likely we are to see losses. And the more often we see losses, the more often we experience loss aversion, which then becomes a vicious cycle. (Financial losses are processed in the same area of the brain that responds to mortal danger.) Investors should learn the big lesson from Buffett’s insurance underwriting practices: always think in terms of expected value. Be risk averse but do not be loss averse, that is, do not be afraid to take calculated risks. Investing is not a business in which every investment is profitable.

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The answer lies in an attribute intrinsic to all humans and the overarching theory that forms the bedrock for most biases in behavioral finance. Loss aversion. Human beings are more motivated by the thought of losing something than by the thought of gaining something of equal value. This is especially true under conditions of uncertainty. The threat of potential loss plays a significant role in our decision-making, and we have a natural tendency to be loss averse. Or, in Munger’s words, “The quantity of a man’s pleasure from a ten-dollar gain does not exactly match the quantity of his displeasure from a ten-dollar loss.”7 This is the foundational principle of Daniel Kahneman and Amos Tversky’s prospect theory (figure 31.3).

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(Buffett often has talked about his love for toll roads in a figurative manner, such as newspapers in one-newspaper towns.) Likewise, licenses and regulatory approvals confer legal oligopoly status through regulatory fiat (as is the case with ratings agencies). Switching Costs Switching costs come in many forms and may be explicit (in the form of money and time) or psychological (resulting from deep-rooted loss aversion or status quo bias). These costs tend to be associated with critical products (such as Oracle’s SAP software) that are so tightly integrated with the customer’s business processes that it would be too disruptive and costly to switch vendors, or with products that have high benefit-to-cost ratios (such as Moody’s).

Bulletproof Problem Solving
by Charles Conn and Robert McLean
Published 6 Mar 2019

This has been underscored by recent work on forecasting.8  Executives rank reducing decision bias as their number one aspiration for improving performance.9  For example, a food products company Rob was serving was trying to exit a loss‐making business. They could have drawn a line under the losses if they took an offer to exit when they had lost $125 million. But they would only accept offers to recover accounting book value (a measure of the original cost). Their loss aversion, a form of sunk‐cost bias, meant that several years later they finally exited with losses in excess of $500 million! Groupthink amongst a team of managers with similar backgrounds and traditional hierarchy made it hard for them see the real alternatives clearly; this is a common problem in business.

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This is what the seven steps process is all about. A Practical Way to Deal with Biases Lets talk about the main sources of bias and error in problem solving. In conversations with our former colleague and Kahneman collaborator, Professor Dan Lovallo, he suggests that the most important to address are confirmation bias, anchoring bias, and loss aversion.4 We will add in availability bias and overoptimism as additional issues to address in your team processes. Indeed, as Exhibit 4.6 shows, many of the other biases described in the literature are forms of these five underlying issues. EXHIBIT 4.6 Confirmation bias is falling in love with your one‐day answer.

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EXHIBIT 4.6 Confirmation bias is falling in love with your one‐day answer. It is the failure to seriously consider the antithesis to your thesis, ignoring dissenting views—essentially picking low hanging mental fruit. Anchoring bias is the mistaken mental attachment to an initial data range or data pattern that colors your subsequent understanding of the problem. Loss aversion, and its relatives, the sunk cost fallacy, book loss fear, and the endowment effect, are a failure to ignore costs already spent (sunk) or any asymmetric valuing of losses and gains. Availability bias is use of an existing mental map because it is readily at hand, rather than developing a new model for a new problem, or just being influenced by more recent facts or events.

Model Thinker: What You Need to Know to Make Data Work for You
by Scott E. Page
Published 27 Nov 2018

More realistic models may require more sophisticated mathematics.13 None of these concerns is so persuasive to suggest abandoning models with psychologically realistic behaviors, but collectively they imply that we proceed with caution and emphasize well-documented behavioral regularities. Two deviations that have been replicated many times are loss aversion and hyperbolic discounting. Loss aversion states that people are risk-averse over gains and risk-loving over losses. Kahneman and Tversky refer to this general theory of behavior as prospect theory.14 Loss aversion does not at first appear irrational, but it implies that people choose different actions when an identical scenario is presented as a potential loss as opposed to a potential gain. For example, people prefer winning $400 for certain rather than entering a lottery with an even chance of winning $1,000.

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The rational actor will be less successful at predicting human behavior than as a tool for communicating, evaluating actions, and designing policies. We then show how we can add psychological biases and altruistic preferences onto the standard rational-actor model. The choice of whether to include a bias or a concern for others rests again on what we are studying. Some human biases such as loss aversion and presentist bias—caring more about delays today than in the future—may be necessary to include in some instances. For example, those assumptions may be important for models of retirement savings or riots. The assumptions may be less important for models of driving behavior or disease transmission.

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Psychological Biases The rational-actor model has been challenged by psychologists, economists, and neuroscientists, who note that it does not match up with how humans behave. Empirical findings from laboratory and natural experiments show that people suffer a variety of biases, including a status quo bias. We ignore base rates when making probability calculations, we attach too much significance to sure things, and we are loss-averse. As researchers begin to link behavior and beliefs to processes within the brain, evidence of hardwired biases becomes more compelling. For example, neuroeconomics uses brain imaging studies to study economically relevant behaviors such as attitudes toward risk, levels of confidence, and responses to information.9 Kahneman argues that what we know so far supports making a distinction between two types of thinking: quick, intuitive rules (fast thinking) and deliberate contemplation (slow thinking).

Against the Gods: The Remarkable Story of Risk
by Peter L. Bernstein
Published 23 Aug 1996

The answer to a question should be the same regardless of the setting in which it is posed. Kahneman and Tversky interpret the evidence produced by these experiments as a demonstration that people are not risk-averse: they are perfectly willing to choose a gamble when they consider it appropriate. But if they are not risk-averse, what are they? "The major driving force is loss aversion," writes Tversky (italics added). "It is not so much that people hate uncertainty-but rather, they hate losing."6 Losses will always loom larger than gains. Indeed, losses that go unresolved-such as the loss of a child or a large insurance claim that never gets settled-are likely to provoke intense, irrational, and abiding risk-aversion.?

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One of the most familiar manifestations of the failure of invariance is in the old Wall Street saw, "You never get poor by taking a profit." It would follow that cutting your losses is also a good idea, but investors hate to take losses, because, tax considerations aside, a loss taken is an acknowledgment of error. Loss-aversion combined with ego leads investors to gamble by clinging to their mistakes in the fond hope that some day the market will vindicate their judgment and make them whole. Von Neumann would not approve. The failure of invariance frequently takes the form of what is known as "mental accounting," a process in which we separate the components of the total picture.

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Worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally."4 Prospect Theory confirms Keynes's conclusion by predicting which decision you will make. First, the absolute performance of the stock you select is relatively unimportant. The start-up company's performance as compared with Johnson & Johnson's performance taken as a reference point is what matters. Second, loss aversion and anxiety will make the joy of winning on the start-up company less than the pain if you lose on it. Johnson & Johnson is an acceptable "long-term" holding even if it often underperforms. The stocks of good companies are not necessarily good stocks, but you can make life easier by agreeing with your clients that they are.

The Missing Billionaires: A Guide to Better Financial Decisions
by Victor Haghani and James White
Published 27 Aug 2023

Prospect theory proposes that people act as if their “happiness” function looks quite different from the classic utility function suggested by Bernoulli and other economists. Exhibit 7.1 Prospect Theory Versus Classical Utility Preferences Prospect theory describes as “loss aversion” the behavior consistent with the “kinked” utility function displayed in Exhibit 7.1, as distinct from the “risk‐aversion” behavior implied by classical Expected Utility. Both theories agree that individuals are averse to losses relative to gains, and the choice of the term “loss aversion” for the specific observed preferences described by Kahneman and Tversky is unfortunate and leads to much confusion. The implication of the kink, and of the convexity in the region of losses, is that people behave as if they have negative risk‐aversion over lower levels of wealth.

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Not All Dynamic Asset Allocation Is “Market Timing” Connecting the Dots Notes 6 The Mechanics of Choice Desire 101 A Silly Game Gives Birth to a Sensible Idea The Happiness Curve Expected Utility and Choice Theory The One‐shape‐fits‐all Suite of Utility Functions Utility User's Guide Demystifying Utility with Certainty‐equivalence Risk‐adjusted Return and the Price of Risk A Penny Saved Is Two Pennies Earned A Sharper Lens Than the Sharpe Ratio Some Clarity on Risk Parity When an Economist Calls You Irrational Baby Needs a New Pair of Shoes, or Investing to Reach a Goal Connecting the Dots Notes 7 Criticisms of Expected Utility Decision‐making This Isn't How Ordinary People Make Decisions What Happens in Vegas… Individuals Are Incapable of Specifying Their Personal Utility Functions Prospect Theory and “Loss Aversion” Criticisms of the Axioms of Rational Choice Wicked Games Probabilities of Future Outcomes Are Unknowable The Kelly Criterion Is Good; Expected Utility Is Bad Expected Utility for Life and Death Choices So Why Isn't Everyone Using Expected Utility Already? Connecting the Dots Notes 8 Reminiscences of a Hedge Fund Operator Background The Big Decision Would've, Should've, Could've (Un)common Sense Notes II: Lifetime Spending and Investing 9 Spending and Investing in Retirement What Do Good Solutions Look Like?

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Swensen is likely correct that few market participants today know how to calibrate their own utility function, but this is an issue of education rather than possibility. Reasonable calibration procedures are both possible and straightforward, and we explore them in detail in Chapter 12. Prospect Theory and “Loss Aversion” Prospect theory was developed by psychologists Daniel Kahneman and Amos Tversky to explain how people behave when facing risky decisions. They observed that behavior systematically disagrees with the predictions of classical economics driven by maximizing Expected Utility. In particular, they observed, over and over again, three noteworthy patterns of behavior: People weigh the pain of small losses a lot more than the pleasure of small gains.

The Paradox of Choice: Why More Is Less
by Barry Schwartz
Published 1 Jan 2004

Losing $100 produces a feeling of negativity that is more intense than the feelings of elation produced by a gain. Some studies have estimated that losses have more than twice the psychological impact as equivalent gains. The fact is, we all hate to lose, which Kahneman and Tversky refer to as loss aversion. The last and crucial element to the graph is the location of the neutral point. This is the dividing line between what counts as a gain and what counts as a loss, and here, too, subjectivity rules. When there is a difference in price between cash and credit at the gas station, is it a discount for cash or a surcharge for credit?

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You think about it for a while and decide. Now imagine trying to decide whether to buy a mountain bike or a digital camera. Each option represents a gain (positive features it has that the other doesn’t) and a loss (positive features it doesn’t have that the other does). We saw in Chapter 3 that people tend to display loss aversion. The loss of $100 is more painful than the gain of $100 is pleasurable. What that means is that when the mountain bike and the digital camera are compared, each will suffer from the comparison. If you choose the camera, you’ll gain the quality and convenience of digital photography but lose the exercise in lovely surroundings.

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Or suppose you are Many examples of phenomena discussed in this section can be found in articles collected in D. Kahneman and A. Tversky (eds.), Choices, Values, and Frames (New York: Cambridge University Press, 2000). On the endowment effect, see D. Kahneman, J. Knetsch, and R. Thaler, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias.” On decisions to sell stock, see T. Odean, “Are Investors Reluctant to Realize Their Losses?” On sunk costs, see R. Thaler, “Mental Accounting Matters,” and R. Thaler, “Toward a Positive Theory of Consumer Choice.” On health insurance decisions, see E. Johnson, J. Hershey, J.

Thinking in Bets
by Annie Duke
Published 6 Feb 2018

Don’t fall in love or even date anybody if you want only positive results. The world is structured to give us lots of opportunities to feel bad about being wrong if we want to measure ourselves by outcomes. Don’t fall for it! Second, being wrong hurts us more than being right feels good. We know from Daniel Kahneman and Amos Tversky’s work on loss aversion, part of prospect theory (which won Kahneman the Nobel Prize in Economics in 2002), that losses in general feel about two times as bad as wins feel good. So winning $100 at blackjack feels as good to us as losing $50 feels bad to us. Because being right feels like winning and being wrong feels like losing, that means we need two favorable results for every one unfavorable result just to break even emotionally.

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Edward Russo, and Nancy Pennington. “Back to the Future: Temporal Perspective in the Explanation of Events.” Journal of Behavioral Decision Making 2, no. 1 (January 1989): 25–38. Morewedge, Carey, Lisa Shu, Daniel Gilbert, and Timothy Wilson. “Bad Riddance or Good Rubbish? Ownership and Not Loss Aversion Causes the Endowment Effect.” Journal of Experimental Social Psychology 45, no. 4 (July 2009): 947–51. Mullally, Sinead, and Eleanor Maguire. “Memory, Imagination, and Predicting the Future: A Common Brain Mechanism?” The Neuroscientist 20, no. 3 (June 2014): 220–34. Munnell, Alice, Wenliang Hou, and Anthony Webb.

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Baltimore: Johns Hopkins University Press, 2002. Tomlin, Damon, David Rand, Elliot Ludvig, and Jonathan Cohen. “The Evolution and Devolution of Cognitive Control: The Costs of Deliberation in a Competitive World.” Scientific Reports 5 (June 16, 2015). Tversky, Amos, and Daniel Kahneman. “Loss Aversion in Riskless Choice: A Reference-Dependent Model.” Quarterly Journal of Economics 106, no. 4 (November 1991): 1039–61. Von Neumann, John, and Oskar Morgenstern. Theory of Games and Economic Behavior. 60th anniv. ed. Princeton, NJ: Princeton University Press, 1944, 2004. Wachowski, Lana, and Wachowski, Lilly, dirs.

Super Thinking: The Big Book of Mental Models
by Gabriel Weinberg and Lauren McCann
Published 17 Jun 2019

Other times you may find that what it would take to get where you originally wanted to go is just not worth the effort anymore. Unfortunately, psychologically, your mind is working hard against you here, and loss aversion is the model that explains why. You are more inclined to avoid losses, to be averse to them, than you are to want to make similar gains. Quite simply, you get more displeasure from losing fifty dollars than pleasure from gaining fifty dollars. Since you hate losing, loss aversion can cause you harm under many circumstances. You may hold losing stocks way too long, hoping they will recover back to the value they had when you bought them.

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Daniel Kahneman and Amos Tversky’s work on this topic, detailed in the October 1992 issue of the Journal of Risk and Uncertainty, demonstrated that across many risky situations, such as winning or losing money based on a coin toss, people tend to want the potential payoff to be around double the potential loss before they are willing to take the gamble. That is, people want to have a fifty-fifty chance of winning one hundred dollars if they have to put fifty dollars on the line. Loss aversion can be better understood using the frame of reference model (see Chapter 1). When you already have a win on your hands, you tend to want to lock in your gains. From this frame of reference, you tend to act more conservatively and are more likely to pass up a chance at a bigger gain if it means risking your current winnings.

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Use the Pareto principle to find the 80/20 in any activity and increase your leverage at every turn. Recognize when you’ve hit diminishing returns and avoid negative returns. Use commitment and the default effect to avoid present bias, and periodic evaluations to avoid loss aversion and the sunk-cost fallacy. Look for shortcuts via existing design patterns, tools, or clever algorithms. Consider whether you can reframe the problem. 4 Becoming One with Nature BEFORE THE INDUSTRIAL REVOLUTION, most peppered moths in Manchester, England, were light-colored, using trees covered with pale bark and lichens as camouflage to avoid becoming prey for birds.

Success and Luck: Good Fortune and the Myth of Meritocracy
by Robert H. Frank
Published 31 Mar 2016

The labour of his body, and the work of his hands, we may say, are properly his.”8 With these words, Locke became the patron saint of tax resisters around the world. This sense of entitlement to the fruits of one’s labors may owe much to the phenomenon known as loss aversion. One of the most reliable findings in behavioral economics, loss aversion refers to the fact that people will fight much harder to avoid a loss than they would to achieve a gain of the same amount.9 Since most successful people work extremely hard for the money they earn, it feels like they own it, and that makes taxation feel like theft.

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Chunliang Feng, Yi Luo, Ruolei Gu, Lucas S Broster, Xueyi Shen, Tengxiang Tian, Yue-Jia Luo, Frank Krueger, “The Flexible Fairness: Equality, Earned Entitlement, and Self-Interest,” PLOS ONE 8.9 (September 2013), http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0073106. 7. Mechanical Turk, https://www.mturk.com/mturk/welcome. 8. John Locke, Second Treatise on Civil Government, 1689, chap. 5, section 27, http://www.constitution.org/jl/2ndtr05.htm. 9. Daniel Kahneman, Jack L. Knetsch, and Richard H. Thaler, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias,” Journal of Economic Perspectives 5.1 (1991): 193–206. 10. Liam Murphy and Thomas Nagel, The Myth of Ownership, New York: Oxford University Press, 2001. 11. David DeSteno, Monica Y. Bartlett, Jolie Baumann, Lisa A. Williams, and Leah Dickens, “Gratitude as Moral Sentiment: Emotion-Guided Cooperation in Economic Exchange,” Emotion 10.2 (2010): 289–93. 12.

Big Mistakes: The Best Investors and Their Worst Investments
by Michael Batnick
Published 21 May 2018

You go back and forth several times, but finally decide to pull the trigger on the team with the less talented quarterback but a stronger defense. After you've walked to the counter and placed your bet, you'll immediately feel much better about your decision than before you parted with your dollars. Kahneman, Knetsch, and Thaler documented this in an experiment in their 1991 paper, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias.”1 In an advanced undergraduate economics class at Cornell, 22 students in alternating seats were given coffee mugs that sell for $6 at the bookstore. When sellers were given the option to sell, and buyers given the option to buy, the study found that the median owner was unwilling to sell for less than $5.25, while the median buyer was unwilling to pay more than $2.25.

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Making decisions ahead of time, especially decisions that involve admitting defeat, can help conquer one of the biggest hurdles investors face; looking in the mirror and seeing an ability that we just do not possess. Notes 1. Daniel Kahneman, Jack L. Knetsch, and Richard H. Thaler, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias,” Journal of Economic Perspectives 5, no. 1 (Winter 1991): 193–206. 2. Robert Shiller, Irrational Exuberance (Princeton, NJ: Princeton University Press, 2000), 60. 3. David Dreman, Contrarian Investment Strategies: The Psychological Edge (New York: Free Press, 2012), 176. 4.

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Index 13D registration, 90 101 Years on Wall Street (Brown), 50 Abbot Labs, 91 ABX Index, 134 Ackman, Bill, 3, 85, 88 CNN interview, 92 confidence, 88–89 persistence, 89 Adams, Evelyn, 131 Airbnb, 151 Alcoa, trading, 157 Alfond, Harold, 81 Amazon, 139–140 earnings, 7 Animal spirits, 126 “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias” (Kahneman/Knetsch/Thaler), 75 AOL/Time Warner, merger, 49 Apple earnings, certainty (example), 120 shareholder wealth, 109 Arthur Lipper, tracking, 70 Art of Contrary Thinking, The, (Neill), 67 Assets under management (AUM), reduction, 61 Automatic, Sacca investment, 149 Bacon, Louis, 103 Balanced fund, transformation, 50 Bank of England, currency defense, 103 Bank of Taiwan, investments, 40 Baruch, Bernard, 7 Batnick, Michael, 155 Behavior gap, 99 Bell, Alexander Graham, 29 Benchmarks, 77 Benjamin Graham Joint Account, 7 Berkowitz, David, 88 Berkshire Hathaway Buffett control, 76 drawdowns, 143 market cap, 79 recovery, 114 shares, decline, 142 stock, Buffett purchase, 76 value loss, 57 Bernstein, Peter, 121, 164 Bernstein, William, 37 Betting on Zero (Silvan), 94 Betty Crocker, comparison, 91 Black Monday, 102 Black‐Scholes option pricing model, 39–40 Blood money, 91 Blue Chip Stamps, 141–142 Bogle, Jack, 45, 159 firing (Wellington Management), 51 impact, 47 performance, problems, 51 Boston Security Analysis Society, Samuelson remarks, 51 Brokerage house, offer, 20 Brokers, long‐term relationship, 61 Brooks, John, 68 Brown, John Dennis, 50 Brown, Josh, 162–163 Bucket shops closure, 18 usage, 16 Buffalo Evening News (purchase), 142 Buffett, Warren, 4, 10, 73, 140 annual forecasts, 77 circle of competence, 80 comparison, 100 gross returns, 76 investment philosophy, 76–77 limited partnership, closure, 111 Oracle of Omaha, 76, 78 Pearson, contrast, 114 Bull market, margin for error, 67 Cabot, Walter, 50 Capital, usage, 17 Carr, Fred, 69 Cayne, James, 40 Charlie Munger: The Complete Investor (Griffin), 81 Charmin, comparison, 91 Chartered Financial Analyst (CFA) exam, 158–159 Chasing the Last Laugh (Zacks), 27 Chesapeake & Atlantic, 20–21 Chicago, Burlington and Quincy Railroad, 16 Chicago Herald (problems), 30 Church and Dwight, value, 91 Churchill, Winston, 91 Circle of competence (Buffett), 80 Cisco, gains, 57 Citron Research, 113–114 Clemens, Samuel.

Willful: How We Choose What We Do
by Richard Robb
Published 12 Nov 2019

Since the 1960s, detractors of the efficient market hypothesis have identified potential anomalies in the data that a savvy trader could exploit, and defenders have counterattacked with one of three claims: (1) The detractors looked at hundreds of possible anomalies and only published one—even if markets are perfectly unpredictable, some patterns will appear by chance, (2) There’s a flaw in the detractors’ analysis (for instance, transaction costs would chew up apparent profit or the securities were not really available at the published price), or (3) Any above-market returns can be attributed to risk, since the payoff is positively correlated with other financial assets or human capital. In return, detractors point to alleged cognitive biases, such as loss aversion, as the reason that money-making opportunities persist. Defenders then respond that some people may be biased some of the time, but even a small number of arbitrageurs can force the market to its proper level.1 I am neither in favor of the efficient market hypothesis, nor against it. I am against the way it frames the debate: it mischaracterizes markets by ignoring different ways of possessing information.

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This field investigates the systematic mistakes that we could correct by recognizing our biases and mental shortcuts. It shouldn’t be hard to persuade anyone who falls for the gambler’s fallacy that the ball is no more likely to land on red in the next spin of the roulette wheel because it was red the last five times or the opposite, that black is not due to catch up. A person exhibiting loss aversion might reject a bet with a 50 percent chance of winning two dollars and a 50 percent chance of losing one dollar. She pays over the odds to avoid small losses because losing is accompanied by psychological discomfort. (This is different from risk aversion, which deals with losses on a large scale.)

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See also mercy ambiguity effect, 24 American Work-Sports (Zarnowski), 191 Anaximander, 190 anchoring, 168 angel investors, 212–213n1 “animal spirits,” 169 Antipater of Tarsus, 134–135, 137 “anxious vigilance,” 73, 82 arbitrage, 70, 78 Aristotle, 200, 220n24 Asian financial crisis (1997–1998), 13 asset-backed securities, 93–95 asset classes, 75 astrology, 67 asymmetric information, 96, 210n2 authenticity, 32–37, 114 of challenges, 176–179 autism, 58, 59 auto safety, 139 Bank of New York Mellon, 61 Battle of Waterloo, 71, 205 Bear Stearns, 85 Becker, Gary, 33, 108–109 behavioral economics, 4, 10, 198–199 assumptions underlying, 24 insights of, 24–25 rational choice complemented by, 6 Belgium, 191 beliefs: attachment to, 51 defined, 50 evidence inconsistent with, 54, 57–58 formation of, 53, 92 persistence of, 26–28, 54 transmissibility of, 92–93, 95–96 Bentham, Jeremy, 127, 197–198 “black swans,” 62–64 blame aversion, 57, 72 brain hemispheres, 161 Brexit, 181–185 “bull markets,” 78 capital asset pricing model, 64 care altruism, 38, 104, 108–114, 115, 120, 135, 201 Casablanca (film), 120, 125 The Cask of Amontillado (Poe), 126–127 challenges, 202–203 authenticity of, 176–179 staying in the game linked to, 179–181 changes of mind, 147–164 charity, 40, 45–46, 119, 128 choice: abundance of, 172–174 intertemporal, 149–158, 166 purposeful vs. rational, 22–23 Christofferson, Johan, 83, 86, 87, 88 Cicero, 133–134 Clark, John Bates, 167 cognitive bias, 6, 23, 51, 147–148, 167, 198–199 confirmation bias, 200 experimental evidence of, 10–11, 24 for-itself behavior disguised as, 200–201 gain-loss asymmetry, 10–11 hostile attribution bias, 59 hyperbolic discounting as, 158 lawn-mowing paradox and, 33–34 obstinacy linked to, 57 omission bias, 200 rational choice disguised as, 10–11, 33–34, 199–200 salience and, 29, 147 survivor bias, 180 zero risk bias, 24 Colbert, Claudette, 7 Columbia University, 17 commitment devices, 149–151 commodities, 80, 86, 89 commuting, 26, 38–39 competitiveness, 11, 31, 41, 149, 189 complementary skills, 71–72 compound interest, 79 confirmation bias, 57, 200 conspicuous consumption, 31 consumption planning, 151–159 contrarian strategy, 78 cooperation, 104, 105 coordination, 216n15 corner solutions, 214n8 cost-benefit analysis: disregard of, in military campaigns, 117 of human life, 138–143 credit risk, 11 crime, 208 Dai-Ichi Kangyo Bank (DKB), 12–14, 15, 17, 87, 192–193 Darwin, Charles, 62–63 depression, psychological, 62 de Waal, Frans, 118 Diogenes of Seleucia, 134–135, 137 discounting of the future, 10, 162–164 hyperbolic, 158, 201 disjunction effect, 174–176 diversification, 64–65 divestment, 65–66 Dostoevsky, Fyodor, 18 drowning husband problem, 6–7, 110, 116, 123–125 effective altruism, 110–112, 126, 130, 135–136 efficient market hypothesis, 69–74, 81–82, 96 Empire State Building, 211–212n12 endowment effect, 4 endowments, of universities, 74 entrepreneurism, 27, 90, 91–92 Eratosthenes, 190 ethics, 6, 104, 106–108, 116, 125 European Union, 181–182 experiential knowledge, 59–61 expert opinion, 27–28, 53, 54, 56–57 extreme unexpected events, 61–64 fairness, 108, 179 family offices, 94 Fear and Trembling (Kierkegaard), 53–54 “felicific calculus,” 197–198 financial crisis of 2007–2009, 61, 76, 85, 93–94, 95 firemen’s muster, 191 flow, and well-being, 201–202 Foot, Philippa, 133–134, 135 for-itself behavior, 6–7, 19, 21, 27, 36, 116, 133–134, 204–205, 207–208 acting in character as, 51–53, 55–56, 94–95, 203 acting out of character as, 69, 72 analyzing, 20 authenticity and, 33–35 charity as, 39–40, 45–46 comparison and ranking lacking from, 19, 24, 181 consequences of, 55–64 constituents of, 26–31 defined, 23–24 difficulty of modeling, 204 expert opinion and, 57 extreme unexpected events and, 63–64 flow of time and, 30 free choice linked to, 169–172 in groups, 91–100 incommensurability of, 140–143 in individual investing, 77–78 in institutional investing, 76 intertemporal choice and, 168, 175, 176 job satisfaction as, 189 mercy as, 114 misclassification of, 42, 44, 200–201 out-of-character trading as, 68–69 purposeful choice commingled with, 40–43, 129, 171 rationalizations for, 194–195 in trolley problem, 137 unemployment and, 186 France, 191 Fuji Bank, 14 futures, 80–81 gain-loss asymmetry, 10–11 Galperti, Simone, 217n1 gambler’s fallacy, 199 gamifying, 177 Garber, Peter, 212n1 Germany, 191 global equity, 75 Good Samaritan (biblical figure), 103, 129–130, 206 governance, of institutional investors, 74 Great Britain, 191 Great Depression, 94 Greek antiquity, 190 guilt, 127 habituation, 201 happiness research (positive psychology), 25–26, 201–202 Hayek, Friedrich, 61, 70 hedge funds, 15–17, 65, 75, 78–79, 93, 95 herd mentality, 96 heroism, 6–7, 19–20 hindsight effect, 199 holding, of investments, 79–80 home country bias, 64–65 Homer, 149 Homo ludens, 167–168 hostile attribution bias, 59 housing market, 94 Huizinga, Johan, 167–168 human life, valuation of, 138–143 Hume, David, 62, 209n5 hyperbolic discounting, 158, 201 illiquid markets, 74, 94 index funds, 75 individual investing, 76–82 Industrial Bank of Japan, 14 information asymmetry, 96, 210n2 innovation, 190 institutional investing, 74–76, 82, 93–95, 205 intergenerational transfers, 217n1, 218n4 interlocking utility, 108 intertemporal choice, 149–159, 166 investing: personal beliefs and, 52–53 in start-ups, 27 Joseph (biblical figure), 97–99 Kahneman, Daniel, 168 Kantianism, 135–136 Keynes, John Maynard, 12, 58, 167, 169, 188–189 Kierkegaard, Søren, 30, 53, 65, 88 Knight, Frank, 145, 187 Kranton, Rachel E., 210–211n2 labor supply, 185–189 Lake Wobegon effect, 4 lawn-mowing paradox, 33–34, 206 Lehman Brothers, 61, 86, 89, 184 leisure, 14, 17, 41, 154, 187 Libet, Benjamin, 161 life, valuation of, 138–143 Life of Alexander (Plutarch), 180–181 Locher, Roger, 117, 124 long-term vs. short-term planning, 148–149 loss aversion, 70, 199 lottery: as rational choice, 199–200 Winner’s Curse, 34–36 love altruism, 104, 116, 123–125, 126, 203 lying, vs. omitting, 134 Macbeth (Shakespeare), 63 MacFarquhar, Larissa, 214n6 Madoff, Bernard, 170 malevolence, 125–127 Malthus, Thomas, 212n2 manners, in social interactions, 104, 106, 107, 116, 125 market equilibrium, 33 Markowitz, Harry, 65 Marshall, Alfred, 41, 167 Mass Flourishing (Phelps), 189–191 materialism, 5 merchant’s choice, 133–134, 137–138 mercy, 104, 114–116, 203 examples of, 116–120 inexplicable, 45–46, 120–122 uniqueness of, 119, 129 mergers and acquisitions, 192 “money pump,” 159 monks’ parable, 114, 124 Montaigne, Michel de, 114, 118 mortgage-backed securities, 93 Nagel, Thomas, 161 Napoleon I, emperor of the French, 71 neoclassical economics, 8, 10, 11, 22, 33 Nietzsche, Friedrich, 21, 43, 209n5 norms, 104, 106–108, 123 Norway, 66 Nozick, Robert, 162 observed care altruism, 108–112 Odyssey (Homer), 149–150 omission bias, 200 On the Fourfold Root of the Principle of Sufficient Reason (Schopenhauer), 209n5 “on the spot” knowledge, 61, 70, 80, 94, 205 Orico, 13 overconfidence, 57, 200 “overearning,” 44–45 The Palm Beach Story (film), 7 The Paradox of Choice (Schwartz), 172 parenting, 108, 141, 170–171 Pareto efficiency, 132–133, 136, 139–140 Peirce, Charles Sanders, 53–54, 67, 94 pension funds, 66, 74–75, 93, 95 permanent income hypothesis, 179 Pharaoh (biblical figure), 97–99 Phelps, Edmund, 17, 189–191 Philip II, king of Macedonia, 181 planning, 149–151 for consumption, 154–157 long-term vs. short-term, 148–149 rational choice applied to, 152–158, 162 play, 44–45, 167, 202 pleasure-pain principle, 18 Plutarch, 180–181 Poe, Edgar Allan, 126 pollution, 132–133 Popeye the Sailor Man, 19 portfolio theory, 64–65 positive psychology (happiness research), 25–26, 201–202 preferences, 18–19, 198 aggregating, 38–39, 132, 164 altruism and, 28, 38, 45, 104, 110, 111, 116 in behavioral economics, 24, 168 beliefs’ feedback into, 51, 55 defined, 23 intransitive, 158–159 in purposeful behavior, 25, 36 risk aversion and, 51 stability of, 33, 115, 147, 207, 208 “time-inconsistent,” 158, 159, 166, 203 present value, 7, 139 principal-agent problem, 72 Principles of Economics (Marshall), 41 prisoner’s dilemma, 105 private equity, 75 procrastination, 3, 4, 19, 177–178 prospect theory, 168 protectionism, 185–187 Prussia, 191 public equities, 75 punishment, 109 purposeful choice, 22–26, 27, 34, 36, 56, 133–134, 204–205 altruism compatible with, 104, 113–114, 115–116 commensurability and, 153–154 as default rule, 43–46 expert opinion and, 57 extreme unexpected events and, 62–63 flow of time and, 30 for-itself behavior commingled with, 40–43, 129, 171 mechanistic quality of, 68 in merchant’s choice, 135, 137–138 Pareto efficiency linked to, 132 rational choice distinguished from, 22–23 regret linked to, 128 social relations linked to, 28 stable preferences linked to, 33 in trolley problem, 135–136 vaccination and, 58–59 wage increases and, 187.

The Impulse Society: America in the Age of Instant Gratification
by Paul Roberts
Published 1 Sep 2014

Or, to quote the acronym that traders and executives repeated whenever anyone raised concerns about the deals being done, “IBG YBG”—as in “I’ll Be Gone, You’ll Be Gone.”42 Gamblers, when their luck turns sour, often exhibit a behavioral tic known as loss aversion. It’s a survival thing—because we were adapted for scarcity, we’re predisposed to hate losing any sort of asset. In studies involving gambling, subjects perceive losses to be twice as large as wins even though the losses and wins involve the same amount of money.43 Loss aversion is why blackjack players will double-down repeatedly after a bad hand, and why stock traders will ride a losing stock into the ground. It’s also why homeowners often refuse to lower their selling price even when the market is collapsing, which is what began to happen in 2006.

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We couldn’t sell the houses.”44 Adding to the misery, however, realtors now had to get clients to understand that the massive wealth they’d possessed only months before was now gone. “You had to counsel people. I had one client come to me. He had twelve houses. He had been buying them and flipping them, and he got stuck with twelve houses. I said to him, ‘The market has stopped.’ ” Loss aversion is also an apt description of how the entire market, and especially the financial market, reacted to the collapse—with increasingly desperate moves that made the final damage so much worse. As the economy stalled and corporate earnings flattened, panicked CEOs initiated massive share buybacks.

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“IBG YBG,” review of Jonathan Knee, The Accidental Investment Banker (Oxford University Press, 2006), in Words, Words, Words, http://wordsthrice.blogspot.com/2006/12/ibg-ybg.html. 43. Yexin Jessica Li, Douglas Kenrick, Vladas Griskevicius, and Stephen L. Neuberg, “Economic Decision Biases in Evolutionary Perspectives: How Mating and Self-Protection Motives Alter Loss Aversion,” Journal of Personality and Social Psychology 102, no. 3 (2012), http://www.csom.umn.edu/marketinginstitute/research/documents/HowMatingandSelf-ProtectionMotivesAlterLossAversion.pdf. 44. Interview with author. 45. William Lazonick, “The Innovative Enterprise and the Developmental State: Toward an Economics of ‘Organizational Success.’”

When the Money Runs Out: The End of Western Affluence
by Stephen D. King
Published 17 Jun 2013

This feature is not uniquely Argentine or Japanese. It is a deeply embedded psychological characteristic. It's called loss aversion. Standard economic theory suggests that individuals treat gains and losses in similar fashion. In reality, however, people dislike losses far more than they enjoy gains. An economic system that seems to offer individuals the equal possibility of gains and losses – a world of stagnation rather than growth – is one that's likely to be dominated by loss aversion. Entrepreneurial activity falls by the wayside. What we already think we have – or we are entitled to – we'll not give up easily, even if we are much better off than previous generations.

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The Sellers started off with mugs but no money. In their experiments, the authors found that Sellers valued their mugs far more highly than the Choosers even though both groups were, in effect, faced with the same choice: both would go home either with a mug or some cash. The difference relates to loss aversion. We don't like to lose things we already own. The Sellers had an ‘emotional’ attachment to their coffee mugs. This psychological insight is incredibly important in a world of economic stagnation or contraction, Smith's dull and melancholy states. Melancholia sets in not just because there is an absence of economic progress but, worse, because there is a fight over the spoils of economic endeavour.

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(i) Knickerbocker Trust Company (i) Korea (i), (ii), (iii), (iv), (v) Krugman, Paul (i), (ii), (iii) labour market (i), (ii) productivity (i) Landes, David (i) Latin American debt crisis (i) Layard, Richard (i), (ii) Lehman Brothers (i), (ii) Leveson inquiry (i) Libor (i) life expectancy (i) liquidity (i), (ii) liquidity trap (i) Liquidity Coverage Ratio (LCR) (i) Little Dorrit (Dickens) (i) living standards (i), (ii), (iii), (iv), (v) belief in ever rising (i), (ii) China (i) Indonesia (i) Japan (i) Korea (i) late 19th century (i), (ii) Malaysia (i) post-Second World War (i) US (i), (ii) loan-to-value ratios, mortgage (i) Long Depression (i) loss aversion (i) lotteries (i) Macroeconomic Imbalance Procedure (MIP) (i) macroeconomic policies (i), (ii), (iii), (iv), (v) Japan (i) macroprudential rules (i) Madoff, Bernie (i) Mahathir Mohamad (i), (ii) Malaysia (i), (ii), (iii) Malthus, Thomas (i) Manchester United (i) Marr, Wilhelm (i) Marx, Karl (i), (ii) Mary Poppins (i) May Report (i) Megawati Sukarnoputri (i) Mellon, Andrew (i), (ii) Mexico (i) Mieno, Yasushi (i) miners (i) Mississippi (i) mistrust creditors and debtors (i) cross-border (i) endemic (i) governments (i), (ii) of money (i) and political extremism (i) monetarism (i) monetary policy (i), (ii), (iii), (iv), (v), (vi) a new monetary framework (i) see also Gold Standard; interest rates; quantitative easing (QE) Monetary Policy Committee (i) monetary unions (i) see also eurozone moral hazard (i) mortgage-backed securities (i), (ii), (iii) mortgages (i), (ii) Napoleon Bonaparte (i) Napoleon III (i) National Bank of North America (i) national incomes (i), (ii), (iii), (iv) Germany (i) Japan (i) UK (i), (ii), (iii) US (i), (ii), (iii), (iv), (v) National Lottery (i) nationalism (i) the Netherlands (i) New Deal (i) ‘new economy’ of the 1990s (i) New Order (Indonesia) (i) New Zealand (i) Nicholson, Viv (i) Nigeria (i) Northern Rock (i), (ii), (iii), (iv) Norway (i) Occupy movement (i), (ii) Office for Budget Responsibility (i) Oliver Twist (Dickens) (i) Osborne, George (i) Overend, Gurney and Co.

No Rules Rules: Netflix and the Culture of Reinvention
by Reed Hastings and Erin Meyer
Published 7 Sep 2020

According to a survey conducted by Glassdoor in 2017, American workers took only about 54 percent of their entitled vacation days. Employees are likely to take even less time off if you remove the vacation allotment altogether because of a well-documented human behavior, which psychologists refer to as “loss aversion.” We humans hate to lose what we already have, even more than we like getting something new. Faced with losing something, we will do everything we can to avoid losing it. We take that vacation. If you’re not allotted vacation, you don’t fear losing it, and are less likely to take any at all.

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A Academy Awards, xvii, 165, 233 “accept or discard” feedback guideline, 31, 33 accidents and safety issues, management style and, 213–14, 269–71 “actionable” feedback guideline, 30, 31, 33, 36, 193, 257 “adapt” feedback guideline, 264 “aim to assist” feedback guideline, 30, 31, 33, 36 Airbnb, 136 Alexa and Katie, 145 alignment, 217–18, 231 on a North Star, 218–21 as tree, 221–31 Allmovie.com, 87 Amazon, 3, 81, 97, 136, 208, 232 Prime, 146, 148 amygdala, 21 Anitta, 97 annual performance reviews, 191 Antioco, John, xi–xii AOL, xviii, 236 Apple, xvii, 77, 97 “appreciate” feedback guideline, 31, 33 Arc de Triomphe, 268–69 Ariely, Dan, 83 Armstrong, Lance, 207, 232–33 Aronson, Elliot, 124 Aspen Institute, 107–8 autonomy, 133 see also decision-making; decision-making approvals, eliminating Avalos, Diego, 151 B Ballad of Buster Scruggs, The, xviii Ballmer, Steve, 122–23 Baptiste, Nigel, 64–66, 68 Bazay, Dominique, 223, 224, 227–31 Bde Maka Ska, 267, 268 Becker, Justin, 35–36 belonging cues, 24–25 bet-taking analogy, 138–40, 153–57, 225–27 Bird Box, 165 Blacklist, The, 26 Black Mirror, 157–59 Blitstein, Ryan, 52 Blockbuster, 3, 171, 236 bankruptcy of, xii, xviii late fees of, 3 Netflix’s offer to, xi–xii size of, xi, xii bonuses, 80–84 Booz Allen Hamilton, 81 brain: feedback and, 20, 21 secrets and, 103 Branson, Richard, xxiv, 50 Brazil, 137, 150, 224–26, 243, 247, 249–51, 257, 264 Brier, David, xxiv brilliant jerks, 34–36, 200 Brown, Brené, 123 Bruk, Anna, 123–24 Bull Durham, 169 Bullock, Sandra, 165 bungee jumping, 194–95 C Canada, 241 candor, 18–21, 141, 175 cultural differences around the world, 250-55, 260, 263–64 culture of, 22–23 dentist visits compared to, 190–91 as disliked but needed, 20–22 failure to speak up, 18, 27, 141 increasing, xx, xxi, 1, 12–37, 72, 100–127, 188–205 jerks and, 34–36 misuse of, 29, 30, 36 “only say about someone what you will say to their face,” 15, 189–90 performance and, 17–20 and readiness to release decision-making controls, 133–35 saying what you really think with positive intent, 13–37 see also feedback; transparency Carey, Chris, 181 Caro, Manolo, 137 Caruso, Rob, 113–14 Casa De Papel, La, xviii celebrating wins, 140, 152 Chapman, Jack, 86 Chase, Chevy, 222 cheating, 62–64 Chelsea, 115–16 children’s programming, 144–45, 226–31 Choy, Josephine, 252–54, 257 Christensen, Nathan, 51 circle of feedback (360-degree assessments), 26–27, 189–205 benefits of, 202–3 discussion facilitated by, 194 in Japan, 256 live, 197–203 stepping out of line during, 200–201 tips for, 199–200 written, names used in, 191–97 Cobb, Melissa, 221–27, 231 Coen, Joel and Ethan, xii Coherent Software, 101, 104 collaboration, 170, 178 Colombia, 251 Comparably, xvii competitiveness, internal, 177–78 compliments and praise, 21, 23 computer software, 77–78, 216 conformity, 141–42 connecting the dots, xxiv first dot, 10–11 second dot, 36 third dot, 69 fourth dot, 98 fifth dot, 125 sixth dot, 160 seventh dot, 185 eighth dot, 203–4 ninth dot, 233 last dot, 264–65 consensus building, 149 contagious behavior, 8–10 context, see leading with context, not control contract signing, 149–51 control, leadership by, 209 ExxonMobil example of, 213–14 leading with context versus, 209–12 see also leading with context, not control controls, removing, xx, xxi, 1, 38–72, 128–61, 206–36 decision-making approvals, 129–61 bet-taking analogy in, 138–40, 153–57, 225–27 Informed Captain model in, 140, 149–52, 216, 223, 224, 231, 248 and picking the best people, 165–66 readiness for, 133–35 signing contracts, 149–51 travel and expense approvals, 55–72 cheating and, 62–64 company’s best interest and, 58, 59, 61, 66, 68–69 context and, 59–62 Freedom and Responsibility ethos and, 60–62 frugality and, 64–69 vacation policy, xv, 39–53, 56, 69–70 freedom and responsibility and, 52–53 Hastings’ nightmares about, 40–41, 42, 44 Hastings’ vacations, 44, 45, 47 Japanese workers and, 46–47 leaders’ modeling and, 42–47 loss aversion and, xv–xvi and setting and reinforcing context to guide employee behavior, 48–49 value added by, 50–52 see also leading with context, not control corporate culture, xiii of Netflix, xiii, xxii, xxiii, 45 Netflix Culture Deck, xiii–xvi, 172–73 Costa, Omarson, 150–51 coupling: alignment and, 218 loose versus tight, 215–17 Coyle, Daniel, 24 creative positions, 78–79, 83–84 criticism (negative feedback), 19–21, 23 belonging cues and, 24 brain and, 20, 21 cultural differences around the world, 251, 261 as disliked but needed, 20–22 language used in, 251–52 responding to, 24, 31 upgraders and downgraders in, 251–52 see also feedback Crook-Davies, Danielle, 19–20 Crown, The, xvii Cryan, John, 82–83 Cuarón, Alfonso, xii, 165 cultural differences around the world, see global expansion and cultural differences Culture Code, The (Coyle), 24 culture map, 242–50 Culture Map, The (Meyer), xxii, 19, 242–50 culture of freedom and responsibility, see Freedom and Responsibility D Daring Greatly: How the Courage to Be Vulnerable Transforms the Way We Live, Love, Parent, and Lead (Brown), 123 Dark, xvii days off, 39–40 see also vacation policy, removing decision-making: dispersed, 216–17 innovation and, 130, 131, 135, 136 and leading with context, 210, 216, 217 to please the boss, 129–30, 133, 152–53 pyramid structure for, 129, 221–23 spreadsheet system and, 143–44 talent density and, 131 transparency and, 131 decision-making approvals, eliminating, 129–61 bet-taking analogy in, 138–40, 153–57, 225–27 Informed Captain model in, 140, 149–52, 216, 223, 224, 231, 248 and picking the best people, 165–66 readiness for, 133–35 signing contracts, 149–51 Del Castillo, Kate, 138 Del Deo, Adam, 207–9, 232–33 Disney, 144, 221, 222, 226, 227 dissent, farming for, 140–44, 158 diversity, 241 Dora the Explorer, 145 Dormen, Yasemin, 157–59 dot-com bubble, 4 dots, see connecting the dots downloading, 146–48 dream teams, 76 DreamWorks, 145, 221, 226 driver feedback, 22 Dutch, Netherlands, 242, 243, 246, 248, 251, 261–63 DVDs, 3–4, 5, 129 Qwikster and, 140–42 shift to streaming from, xii, xvii, 140–41, 236 E Edmondson, Amy, xv Eichenwald, Kurt, 176 Eisner, Michael, 195 elephants, penguins versus, 174 Elite, xvii Emmy Awards, xvii, 145 “Emperor’s New Clothes” syndrome, 23–29 empowerment, 109, 133, 134 see also decision-making; decision-making approvals, eliminating; Freedom and Responsibility Engadget, 158 Enron, xiii entrepreneurship, 138 error prevention, and management style, 213–14, 220, 269–71 Escobar, Pablo, 132 Estaff meetings, 218–19, 243 Evening Standard, 25 Eventbrite, 50 expenses, see travel and expenses; travel and expense approvals, removing experimentation, 138 Explorer project, 154–55, 157 Express, 158 ExxonMobil, 213–14 F Facebook, xiii, 77, 97, 130, 137, 195 failures, 140, 152–59 asking what learning came from the project, 153, 155 not making a big deal about, 153–55 sunshining of, 153, 155–59 family business metaphor, 166–68 moving to sports team metaphor from, 168–70, 173–74 farming for dissent, 140–44, 158 Fast Company, xxiv, 213 fear of losing one’s job, xv, 178–80, 183–84 Fearless Organization, The (Edmondson), xv FedEx, 139 feedback, 14–17, 139, 175, 190, 240 annual performance reviews and, 191 belonging cues and, 24 brain’s response to, 20, 21 circle of (360-degree assessments), 26–27, 189–205 benefits of, 202–3 discussion facilitated by, 194 in Japan, 256 live, 197–203 stepping out of line during, 200–201 tips for, 199–200 written, names used in, 191–97 cultural differences and, 250-57, 260, 261–64 for drivers, 22 “Emperor’s New Clothes” syndrome and, 23–29 failure to speak up with, 18, 27, 141 4A guidelines for, 29–36, 255, 264 accept or discard, 31, 33 actionable, 30, 31, 33, 36, 193, 257 adding 5th A to (adapt), 264 aim to assist, 30, 31, 33, 36 appreciate, 31, 33 cultural differences and, 260 for giving feedback, 30 for receiving feedback, 31 frequency of, 18 Hastings and, 26–29 honesty in, 18; see also candor Japanese culture and, 251–57 loop of, 22–23 Meyer and, 19, 32 negative (criticism), 19–21, 23 belonging cues and, 24 brain and, 20, 21 cultural differences around the world, 251, 261 as disliked but needed, 20–22 language used in, 251–52 responding to, 24, 31 upgraders and downgraders in, 251–52 positive, brain and, 21 responding to, 24, 31 and speaking and reading between the lines, 253 spreadsheet system for gathering, 143–44 survey on, 21–22 teaching employees how to give and receive, 29–32 from teammates, 199 when and where to give, 31–34 see also candor Felps, Will, 8–9 firing, see letting people go Fisher Phillips, 50 five-year plans, 219–20 Flint, Joe, 178 flexibility, and leading with context or control, 220, 221 Fogel, Bryan, 207–8, 233 4K ultra high definition televisions, 65–66 Fowler, Geoffrey, 65–66 Fox, 221 France, 240, 251 Paris, 268–69 Freedom and Responsibility (F&R), xx–xxi, 191, 236, 267, 268 expenses and, 60–62 first steps to, 1–72 Informed Captain model in, 140, 149–52, 216, 223, 224, 231, 248 next steps to, 73–161 techniques to reinforce, 163–236 vacations and, 52–53 weight of responsibility in, 150–52 Friedland, Jonathan, 196 Fuller House, 145 G Game of Thrones, 131–32 Garden Grove, Calif., 22 Gates, Bill, 78 General Electric (GE), 177–78 Germany, 147–48, 250–51 Gizmodo, 178 Gladwell, Malcolm, 142 Glassdoor, xv, 50 global expansion and cultural differences, 237–65, 239–65 adjusting your style for, 257–61 Brazil, 137, 150, 224–26, 243, 247, 249–51, 257, 264 candor and, 250–55, 260, 263–64 culture map, 242–50 feedback and, 250–57, 260, 261–64 Google and, 240–41 Japan, 46–47, 183, 224, 225, 257, 261 in culture map, 243, 247, 248 feedback and criticism in, 251–57 Japanese language, 252–53 360 process and, 256 Netherlands, 242, 243, 246, 248, 251, 261–63 Schlumberger and, 240–41 Singapore, 243, 246, 248, 251, 257–59, 261, 264 trust and, 248, 249 Golden Globe Awards, xvii, 76 Goldman Sachs, 177 Golin, 50 Google, xvii, 77, 94–96, 98, 136 global expansion of, 240–41 gossip, 189 Guillermo, Rob, 207 H Handler, Chelsea, 115–16 happiness, xvii Harvard Business Review, xxii Hastings, Mike, 87 Hastings, Reed: childhood of, 10, 13 at Coherent Software, 101, 104 downloading issue and, 146–48 feedback and, 26–27 interview with, 173–80 in leadership tree, 224–25 marriage of, 13–15 Meyer contacted by, xxii–xxiii Netflix cofounded by, xi, 3–4 in Netflix’s offer to Blockbuster, xi–xii in Peace Corps, xxii, xxiii, 14, 101, 239–40 Pure Software company of, xviii–xix, xxiv, 3, 4, 6, 7, 13–14, 55, 64, 71, 101, 122, 123, 236 Qwikster and, 140–42 HBO, 113–14, 208 Hewlett-Packard (HP), 66–67 hierarchy of picking, 165–66 Hired, xvii hiring: hierarchy of picking and, 165–66 talent density and, see talent density honesty, xvi, xxiii, 178 and spending company money, 58–59 see also candor; transparency hours worked, 39 House of Cards, xvii, 65, 75, 171, 236 HubSpot, xvii, 50 Huffington Post, xxii Hulu, 208, 232 humility, 123 Hunger Games, The, 176 Hunt, Neil, 41, 45, 94, 98, 154, 196 downloads and, 146, 148 and Netflix as team, not family, 173–74 360s and, 197, 198 vacations of, 41 I Icarus, 207–8, 232–33 India, 83, 84, 147–48, 224–26 Mighty Little Bheem in, 228–31 industrial era, 269, 271 industry shifts, xvii–xviii, xix Informed Captain model, 140, 149–52, 216, 223, 224, 231, 248 innovation, xv, xix, xxi, 84, 135–36, 155, 271–72 decision-making and, 130, 131, 135, 136 and leading with context or control, 214–15, 217 Innovation Cycle, 139–40 asking what learning came from the project, 153, 155 celebrating wins, 140, 152 failures and, 140, 152–59 farming for dissent, 140–44, 158 not making a big deal about failures, 153–55 placing your bet as an informed captain, 140, 149–52 socializing the idea, 140, 144–45, 158, 159 spreadsheet system and, 143–44 sunshining failures, 153, 155–59 testing out big ideas, 140, 146–48 International Olympic Committee, 232 internet, 146–48, 154 internet bubble, 4 iPhone, 130 Italy, 131–32 J Jacobson, Daniel, 166–68 Jaffe, Chris, 153–57 Japan, 46–47, 183, 224, 225, 257, 261 in culture map, 243, 247, 248 feedback and criticism in, 251–57 Japanese language, 252–53 360 process and, 256 jerks, 34–36, 200 Jobs, Steve, xxiv, 130 Jones, Rhett, 178 K karoshi, 46 kayaking, 180 Keeper Test, xiv, 165–87, 240, 242 Keeper Test Prompt, 180–83 Key Performance Indicators (KPIs), 81, 191, 209 Kilgore, Leslie, 14–15, 81, 94, 171 expense reports and, 61–62 on hiring and recruiters, 95–96 “lead with context, not control” coined by, 48, 208–9 new customers and, 81–82 signing contracts and, 149–50 360s and, 192, 193, 197, 198 King, Rochelle, 27–29 Kodak, xviii, 236 Korea, 224, 225 Kung Fu Panda, 221 L Lanusse, Adrien, 148 Latin America, 136, 241, 249 Brazil, 137, 150, 224–26, 243, 247, 249–51, 257, 264 Lawrence, Jennifer, 176 lawsuits, 175 layoffs at Netflix, 4–7, 10, 77, 168 leading with context, not control, 48, 207–36 alignment in, 217–18, 231 on a North Star, 218–21 as tree, 221–31 control versus context, 209–12 decision-making in, 210, 216, 217 Downton Abbey-type cook example, 211–12, 218 error prevention and, 213–14, 220, 269–71 ExxonMobil example, 213–14 Icarus example, 207–8, 232–33 innovation and, 214–15, 217 Kilgore’s coining of phrase, 48, 208–9 and loose versus tight coupling, 215–17 Mighty Little Bheem example, 228–31 parenting example, 210–11 spending and, 59–62 talent density and, 212, 213 Target example, 213–15 lean workforce, 79 letting people go, 173–76 “adequate performance gets a generous severance,” xv, xxii, 171, 175–76, 242 employee fears about, xv, 178–80, 183–84 employee turnover, 184–85 in Japan, 183 Keeper Test, xiv, 165–87, 240, 242 Keeper Test Prompt, 180–83 lawsuits and, 175 at Netflix, 185 Netflix layoffs in 2001, 4–7, 10, 77, 168 post-exit communications, 117–20, 183–84 quotas for, 178 LinkedIn, 50, 51, 137 Little Prince, The (Saint-Exupéry), 215 loose versus tight coupling, 215–17 Lorenzoni, Paolo, 131–33, 135, 138 loss aversion, xv–xvi Low, Christopher, 258–60 M Mammoth, 51 Management by Objectives, 209 Man of the House, 222 Massachusetts Institute of Technology, 83 McCarthy, Barry, 14–15, 56 McCord, Patty, 4–7, 9, 10, 15, 27–28, 41, 53, 71, 173 all-hands meetings and, 108 departure from Netflix, 171 expense policy and, 55, 60–61 financial data and, 110 salary policy and, 78, 81, 94, 96 team metaphor and, 169 360s and, 197–99 vacation policy and, 40, 43, 45, 52–53 Memento project, 156, 157 Mexico, 136–38 Meyer, Erin, xxii The Culture Map, xxii, 19, 242–50 Hastings’ message to, xxii–xxiii keynote address of, 19, 32 Netflix employees interviewed by, xxiii, 19–20 in Peace Corps, xxii micromanaging, 130, 133, 134 Microsoft, 78, 122, 176–78 Mighty Little Bheem, 228–31 Mirer, Scott, 200–201 mistakes, 121–25, 271–72 distancing yourself from, 157 management style and, 213–14, 220, 270 sunshining of, 157 see also failures Morgan Stanley, 123 Moss, Trenton, 50–51 Mr.

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A Academy Awards, xvii, 165, 233 “accept or discard” feedback guideline, 31, 33 accidents and safety issues, management style and, 213–14, 269–71 “actionable” feedback guideline, 30, 31, 33, 36, 193, 257 “adapt” feedback guideline, 264 “aim to assist” feedback guideline, 30, 31, 33, 36 Airbnb, 136 Alexa and Katie, 145 alignment, 217–18, 231 on a North Star, 218–21 as tree, 221–31 Allmovie.com, 87 Amazon, 3, 81, 97, 136, 208, 232 Prime, 146, 148 amygdala, 21 Anitta, 97 annual performance reviews, 191 Antioco, John, xi–xii AOL, xviii, 236 Apple, xvii, 77, 97 “appreciate” feedback guideline, 31, 33 Arc de Triomphe, 268–69 Ariely, Dan, 83 Armstrong, Lance, 207, 232–33 Aronson, Elliot, 124 Aspen Institute, 107–8 autonomy, 133 see also decision-making; decision-making approvals, eliminating Avalos, Diego, 151 B Ballad of Buster Scruggs, The, xviii Ballmer, Steve, 122–23 Baptiste, Nigel, 64–66, 68 Bazay, Dominique, 223, 224, 227–31 Bde Maka Ska, 267, 268 Becker, Justin, 35–36 belonging cues, 24–25 bet-taking analogy, 138–40, 153–57, 225–27 Bird Box, 165 Blacklist, The, 26 Black Mirror, 157–59 Blitstein, Ryan, 52 Blockbuster, 3, 171, 236 bankruptcy of, xii, xviii late fees of, 3 Netflix’s offer to, xi–xii size of, xi, xii bonuses, 80–84 Booz Allen Hamilton, 81 brain: feedback and, 20, 21 secrets and, 103 Branson, Richard, xxiv, 50 Brazil, 137, 150, 224–26, 243, 247, 249–51, 257, 264 Brier, David, xxiv brilliant jerks, 34–36, 200 Brown, Brené, 123 Bruk, Anna, 123–24 Bull Durham, 169 Bullock, Sandra, 165 bungee jumping, 194–95 C Canada, 241 candor, 18–21, 141, 175 cultural differences around the world, 250-55, 260, 263–64 culture of, 22–23 dentist visits compared to, 190–91 as disliked but needed, 20–22 failure to speak up, 18, 27, 141 increasing, xx, xxi, 1, 12–37, 72, 100–127, 188–205 jerks and, 34–36 misuse of, 29, 30, 36 “only say about someone what you will say to their face,” 15, 189–90 performance and, 17–20 and readiness to release decision-making controls, 133–35 saying what you really think with positive intent, 13–37 see also feedback; transparency Carey, Chris, 181 Caro, Manolo, 137 Caruso, Rob, 113–14 Casa De Papel, La, xviii celebrating wins, 140, 152 Chapman, Jack, 86 Chase, Chevy, 222 cheating, 62–64 Chelsea, 115–16 children’s programming, 144–45, 226–31 Choy, Josephine, 252–54, 257 Christensen, Nathan, 51 circle of feedback (360-degree assessments), 26–27, 189–205 benefits of, 202–3 discussion facilitated by, 194 in Japan, 256 live, 197–203 stepping out of line during, 200–201 tips for, 199–200 written, names used in, 191–97 Cobb, Melissa, 221–27, 231 Coen, Joel and Ethan, xii Coherent Software, 101, 104 collaboration, 170, 178 Colombia, 251 Comparably, xvii competitiveness, internal, 177–78 compliments and praise, 21, 23 computer software, 77–78, 216 conformity, 141–42 connecting the dots, xxiv first dot, 10–11 second dot, 36 third dot, 69 fourth dot, 98 fifth dot, 125 sixth dot, 160 seventh dot, 185 eighth dot, 203–4 ninth dot, 233 last dot, 264–65 consensus building, 149 contagious behavior, 8–10 context, see leading with context, not control contract signing, 149–51 control, leadership by, 209 ExxonMobil example of, 213–14 leading with context versus, 209–12 see also leading with context, not control controls, removing, xx, xxi, 1, 38–72, 128–61, 206–36 decision-making approvals, 129–61 bet-taking analogy in, 138–40, 153–57, 225–27 Informed Captain model in, 140, 149–52, 216, 223, 224, 231, 248 and picking the best people, 165–66 readiness for, 133–35 signing contracts, 149–51 travel and expense approvals, 55–72 cheating and, 62–64 company’s best interest and, 58, 59, 61, 66, 68–69 context and, 59–62 Freedom and Responsibility ethos and, 60–62 frugality and, 64–69 vacation policy, xv, 39–53, 56, 69–70 freedom and responsibility and, 52–53 Hastings’ nightmares about, 40–41, 42, 44 Hastings’ vacations, 44, 45, 47 Japanese workers and, 46–47 leaders’ modeling and, 42–47 loss aversion and, xv–xvi and setting and reinforcing context to guide employee behavior, 48–49 value added by, 50–52 see also leading with context, not control corporate culture, xiii of Netflix, xiii, xxii, xxiii, 45 Netflix Culture Deck, xiii–xvi, 172–73 Costa, Omarson, 150–51 coupling: alignment and, 218 loose versus tight, 215–17 Coyle, Daniel, 24 creative positions, 78–79, 83–84 criticism (negative feedback), 19–21, 23 belonging cues and, 24 brain and, 20, 21 cultural differences around the world, 251, 261 as disliked but needed, 20–22 language used in, 251–52 responding to, 24, 31 upgraders and downgraders in, 251–52 see also feedback Crook-Davies, Danielle, 19–20 Crown, The, xvii Cryan, John, 82–83 Cuarón, Alfonso, xii, 165 cultural differences around the world, see global expansion and cultural differences Culture Code, The (Coyle), 24 culture map, 242–50 Culture Map, The (Meyer), xxii, 19, 242–50 culture of freedom and responsibility, see Freedom and Responsibility D Daring Greatly: How the Courage to Be Vulnerable Transforms the Way We Live, Love, Parent, and Lead (Brown), 123 Dark, xvii days off, 39–40 see also vacation policy, removing decision-making: dispersed, 216–17 innovation and, 130, 131, 135, 136 and leading with context, 210, 216, 217 to please the boss, 129–30, 133, 152–53 pyramid structure for, 129, 221–23 spreadsheet system and, 143–44 talent density and, 131 transparency and, 131 decision-making approvals, eliminating, 129–61 bet-taking analogy in, 138–40, 153–57, 225–27 Informed Captain model in, 140, 149–52, 216, 223, 224, 231, 248 and picking the best people, 165–66 readiness for, 133–35 signing contracts, 149–51 Del Castillo, Kate, 138 Del Deo, Adam, 207–9, 232–33 Disney, 144, 221, 222, 226, 227 dissent, farming for, 140–44, 158 diversity, 241 Dora the Explorer, 145 Dormen, Yasemin, 157–59 dot-com bubble, 4 dots, see connecting the dots downloading, 146–48 dream teams, 76 DreamWorks, 145, 221, 226 driver feedback, 22 Dutch, Netherlands, 242, 243, 246, 248, 251, 261–63 DVDs, 3–4, 5, 129 Qwikster and, 140–42 shift to streaming from, xii, xvii, 140–41, 236 E Edmondson, Amy, xv Eichenwald, Kurt, 176 Eisner, Michael, 195 elephants, penguins versus, 174 Elite, xvii Emmy Awards, xvii, 145 “Emperor’s New Clothes” syndrome, 23–29 empowerment, 109, 133, 134 see also decision-making; decision-making approvals, eliminating; Freedom and Responsibility Engadget, 158 Enron, xiii entrepreneurship, 138 error prevention, and management style, 213–14, 220, 269–71 Escobar, Pablo, 132 Estaff meetings, 218–19, 243 Evening Standard, 25 Eventbrite, 50 expenses, see travel and expenses; travel and expense approvals, removing experimentation, 138 Explorer project, 154–55, 157 Express, 158 ExxonMobil, 213–14 F Facebook, xiii, 77, 97, 130, 137, 195 failures, 140, 152–59 asking what learning came from the project, 153, 155 not making a big deal about, 153–55 sunshining of, 153, 155–59 family business metaphor, 166–68 moving to sports team metaphor from, 168–70, 173–74 farming for dissent, 140–44, 158 Fast Company, xxiv, 213 fear of losing one’s job, xv, 178–80, 183–84 Fearless Organization, The (Edmondson), xv FedEx, 139 feedback, 14–17, 139, 175, 190, 240 annual performance reviews and, 191 belonging cues and, 24 brain’s response to, 20, 21 circle of (360-degree assessments), 26–27, 189–205 benefits of, 202–3 discussion facilitated by, 194 in Japan, 256 live, 197–203 stepping out of line during, 200–201 tips for, 199–200 written, names used in, 191–97 cultural differences and, 250-57, 260, 261–64 for drivers, 22 “Emperor’s New Clothes” syndrome and, 23–29 failure to speak up with, 18, 27, 141 4A guidelines for, 29–36, 255, 264 accept or discard, 31, 33 actionable, 30, 31, 33, 36, 193, 257 adding 5th A to (adapt), 264 aim to assist, 30, 31, 33, 36 appreciate, 31, 33 cultural differences and, 260 for giving feedback, 30 for receiving feedback, 31 frequency of, 18 Hastings and, 26–29 honesty in, 18; see also candor Japanese culture and, 251–57 loop of, 22–23 Meyer and, 19, 32 negative (criticism), 19–21, 23 belonging cues and, 24 brain and, 20, 21 cultural differences around the world, 251, 261 as disliked but needed, 20–22 language used in, 251–52 responding to, 24, 31 upgraders and downgraders in, 251–52 positive, brain and, 21 responding to, 24, 31 and speaking and reading between the lines, 253 spreadsheet system for gathering, 143–44 survey on, 21–22 teaching employees how to give and receive, 29–32 from teammates, 199 when and where to give, 31–34 see also candor Felps, Will, 8–9 firing, see letting people go Fisher Phillips, 50 five-year plans, 219–20 Flint, Joe, 178 flexibility, and leading with context or control, 220, 221 Fogel, Bryan, 207–8, 233 4K ultra high definition televisions, 65–66 Fowler, Geoffrey, 65–66 Fox, 221 France, 240, 251 Paris, 268–69 Freedom and Responsibility (F&R), xx–xxi, 191, 236, 267, 268 expenses and, 60–62 first steps to, 1–72 Informed Captain model in, 140, 149–52, 216, 223, 224, 231, 248 next steps to, 73–161 techniques to reinforce, 163–236 vacations and, 52–53 weight of responsibility in, 150–52 Friedland, Jonathan, 196 Fuller House, 145 G Game of Thrones, 131–32 Garden Grove, Calif., 22 Gates, Bill, 78 General Electric (GE), 177–78 Germany, 147–48, 250–51 Gizmodo, 178 Gladwell, Malcolm, 142 Glassdoor, xv, 50 global expansion and cultural differences, 237–65, 239–65 adjusting your style for, 257–61 Brazil, 137, 150, 224–26, 243, 247, 249–51, 257, 264 candor and, 250–55, 260, 263–64 culture map, 242–50 feedback and, 250–57, 260, 261–64 Google and, 240–41 Japan, 46–47, 183, 224, 225, 257, 261 in culture map, 243, 247, 248 feedback and criticism in, 251–57 Japanese language, 252–53 360 process and, 256 Netherlands, 242, 243, 246, 248, 251, 261–63 Schlumberger and, 240–41 Singapore, 243, 246, 248, 251, 257–59, 261, 264 trust and, 248, 249 Golden Globe Awards, xvii, 76 Goldman Sachs, 177 Golin, 50 Google, xvii, 77, 94–96, 98, 136 global expansion of, 240–41 gossip, 189 Guillermo, Rob, 207 H Handler, Chelsea, 115–16 happiness, xvii Harvard Business Review, xxii Hastings, Mike, 87 Hastings, Reed: childhood of, 10, 13 at Coherent Software, 101, 104 downloading issue and, 146–48 feedback and, 26–27 interview with, 173–80 in leadership tree, 224–25 marriage of, 13–15 Meyer contacted by, xxii–xxiii Netflix cofounded by, xi, 3–4 in Netflix’s offer to Blockbuster, xi–xii in Peace Corps, xxii, xxiii, 14, 101, 239–40 Pure Software company of, xviii–xix, xxiv, 3, 4, 6, 7, 13–14, 55, 64, 71, 101, 122, 123, 236 Qwikster and, 140–42 HBO, 113–14, 208 Hewlett-Packard (HP), 66–67 hierarchy of picking, 165–66 Hired, xvii hiring: hierarchy of picking and, 165–66 talent density and, see talent density honesty, xvi, xxiii, 178 and spending company money, 58–59 see also candor; transparency hours worked, 39 House of Cards, xvii, 65, 75, 171, 236 HubSpot, xvii, 50 Huffington Post, xxii Hulu, 208, 232 humility, 123 Hunger Games, The, 176 Hunt, Neil, 41, 45, 94, 98, 154, 196 downloads and, 146, 148 and Netflix as team, not family, 173–74 360s and, 197, 198 vacations of, 41 I Icarus, 207–8, 232–33 India, 83, 84, 147–48, 224–26 Mighty Little Bheem in, 228–31 industrial era, 269, 271 industry shifts, xvii–xviii, xix Informed Captain model, 140, 149–52, 216, 223, 224, 231, 248 innovation, xv, xix, xxi, 84, 135–36, 155, 271–72 decision-making and, 130, 131, 135, 136 and leading with context or control, 214–15, 217 Innovation Cycle, 139–40 asking what learning came from the project, 153, 155 celebrating wins, 140, 152 failures and, 140, 152–59 farming for dissent, 140–44, 158 not making a big deal about failures, 153–55 placing your bet as an informed captain, 140, 149–52 socializing the idea, 140, 144–45, 158, 159 spreadsheet system and, 143–44 sunshining failures, 153, 155–59 testing out big ideas, 140, 146–48 International Olympic Committee, 232 internet, 146–48, 154 internet bubble, 4 iPhone, 130 Italy, 131–32 J Jacobson, Daniel, 166–68 Jaffe, Chris, 153–57 Japan, 46–47, 183, 224, 225, 257, 261 in culture map, 243, 247, 248 feedback and criticism in, 251–57 Japanese language, 252–53 360 process and, 256 jerks, 34–36, 200 Jobs, Steve, xxiv, 130 Jones, Rhett, 178 K karoshi, 46 kayaking, 180 Keeper Test, xiv, 165–87, 240, 242 Keeper Test Prompt, 180–83 Key Performance Indicators (KPIs), 81, 191, 209 Kilgore, Leslie, 14–15, 81, 94, 171 expense reports and, 61–62 on hiring and recruiters, 95–96 “lead with context, not control” coined by, 48, 208–9 new customers and, 81–82 signing contracts and, 149–50 360s and, 192, 193, 197, 198 King, Rochelle, 27–29 Kodak, xviii, 236 Korea, 224, 225 Kung Fu Panda, 221 L Lanusse, Adrien, 148 Latin America, 136, 241, 249 Brazil, 137, 150, 224–26, 243, 247, 249–51, 257, 264 Lawrence, Jennifer, 176 lawsuits, 175 layoffs at Netflix, 4–7, 10, 77, 168 leading with context, not control, 48, 207–36 alignment in, 217–18, 231 on a North Star, 218–21 as tree, 221–31 control versus context, 209–12 decision-making in, 210, 216, 217 Downton Abbey-type cook example, 211–12, 218 error prevention and, 213–14, 220, 269–71 ExxonMobil example, 213–14 Icarus example, 207–8, 232–33 innovation and, 214–15, 217 Kilgore’s coining of phrase, 48, 208–9 and loose versus tight coupling, 215–17 Mighty Little Bheem example, 228–31 parenting example, 210–11 spending and, 59–62 talent density and, 212, 213 Target example, 213–15 lean workforce, 79 letting people go, 173–76 “adequate performance gets a generous severance,” xv, xxii, 171, 175–76, 242 employee fears about, xv, 178–80, 183–84 employee turnover, 184–85 in Japan, 183 Keeper Test, xiv, 165–87, 240, 242 Keeper Test Prompt, 180–83 lawsuits and, 175 at Netflix, 185 Netflix layoffs in 2001, 4–7, 10, 77, 168 post-exit communications, 117–20, 183–84 quotas for, 178 LinkedIn, 50, 51, 137 Little Prince, The (Saint-Exupéry), 215 loose versus tight coupling, 215–17 Lorenzoni, Paolo, 131–33, 135, 138 loss aversion, xv–xvi Low, Christopher, 258–60 M Mammoth, 51 Management by Objectives, 209 Man of the House, 222 Massachusetts Institute of Technology, 83 McCarthy, Barry, 14–15, 56 McCord, Patty, 4–7, 9, 10, 15, 27–28, 41, 53, 71, 173 all-hands meetings and, 108 departure from Netflix, 171 expense policy and, 55, 60–61 financial data and, 110 salary policy and, 78, 81, 94, 96 team metaphor and, 169 360s and, 197–99 vacation policy and, 40, 43, 45, 52–53 Memento project, 156, 157 Mexico, 136–38 Meyer, Erin, xxii The Culture Map, xxii, 19, 242–50 Hastings’ message to, xxii–xxiii keynote address of, 19, 32 Netflix employees interviewed by, xxiii, 19–20 in Peace Corps, xxii micromanaging, 130, 133, 134 Microsoft, 78, 122, 176–78 Mighty Little Bheem, 228–31 Mirer, Scott, 200–201 mistakes, 121–25, 271–72 distancing yourself from, 157 management style and, 213–14, 220, 270 sunshining of, 157 see also failures Morgan Stanley, 123 Moss, Trenton, 50–51 Mr.

The Revolution That Wasn't: GameStop, Reddit, and the Fleecing of Small Investors
by Spencer Jakab
Published 1 Feb 2022

He also showed uncommon sophistication—not to mention politesse—on a board known for crude insults and little regard for spelling and punctuation. “Pigs get fat. Hogs get slaughtered,” wrote another poster about DeepFuckingValue’s insistence on hanging on to a small fortune that could shrivel to nothing. “Again that’s not the way to maximize returns over the long-term. That’s trading in fear—loss aversion, a common emotional bias,” he replied. “RemindMe! if this stupid fuck lost it all in 1 week,” wrote another in response. As one former skeptic put it in a much later update to an old comment to Gill in the middle of the GameStop squeeze, his original post “aged like a tall glass of milk on a July afternoon in the Mojave Desert.”

…

Michaela Pagel of Columbia Business School came up with a psychological explanation: losing money pains us more than making money pleases us—a well-known effect in behavioral finance. Because the stock market is more likely to rise over longer periods than shorter ones, more frequent observations cause us to see more losses and nudge us to trade at inopportune times. This phenomenon is known as “myopic loss aversion.”[2] It is a psychological foible that has enriched brokers for years. Smartphone-based ones catering to a generation that grew up constantly checking the devices have supercharged this tendency to their benefit and customers’ detriment. An April 2021 study by Futu, a Chinese brokerage firm with a US subsidiary, found that members of Generation Z opened their trading app 8.2 times a day and traded 147 times a year on average.[3] Where do the forgone gains of frequent traders go?

…

Morgan Asset Management, 255–56 JPMorgan Chase, 160, 217 K KaloBios, 39 Kearns, Alex, 103–4 Kindleberger, Charles P., 179 Klarman, Seth, 184 Koss, 132, 169, 188, 224 Kruger, Justin, 28 Kynikos Associates, 77 L Ladies’ Home Journal, 150 Lamberton, Cait, 54, 62 Lamont, Owen, 80 Langer, Ellen, 27 Langlois, Shawn, 45 Laufer, Henry, 237 Lay, Kenneth, 85 Lebed, Jonathan, 163 Leder, Michelle, 239 Ledger, Heath, 138 Left, Andrew, 39, 116–26, 148, 191, 214, 217 GameStop and, 120–24, 129, 130, 133, 146 harassment of, 122 WallStreetBets and, 121–23, 126, 129, 130, 133, 136, 238 Lehman Brothers, 80, 117 Lending Tree, 162 Levie, Aaron, 26 Lewis, Michael, 16, 88 Lindzon, Howard, 24, 49, 176 LinkedIn, 239 Livermore, Jesse, 78–79 locating a borrow, 72–73, 80 Loeb, Dan, 111 Lombardi, Vince, 8 Long-Term Capital Management, 260 Loop Capital, 128 Los Angeles Times, 215 loss aversion, myopic, 236 lotteries, 62, 239, 241, 242 Lowenstein, Roger, 260 Lucid Motors, 164 M Mad Money, 254 Madoff, Bernie, 117, 206 MagnifyMoney, 162 Mahoney, Seth, 19, 31, 176–77 Malaysia, 75 Malkiel, Burton, 253 Manias, Panics, and Crashes (Kindleberger), 179 Manning, Peyton, 64 Man Who Solved the Market, The (Zuckerman), 237 Maplelane Capital, 217 March Madness, 57 Marcus, 257 margin calls, 203–5 margin debt, 58–59, 62, 67, 138, 188 Markets Insider, 103 MarketWatch, 45, 180 MassMutual, 87, 131, 171 Mavrck, 142 Mayday, 48–50, 66 McCabe, Caitlin, 128–29 McCormick, Packy, 23, 35, 104, 202 McDonald, Larry, 99 McDonald’s, 154 McHenry, Patrick, 239 McLean, Bethany, 85 Medallion Fund, 237 MedBox, 117 Melvin Capital Management, 6–8, 56, 72, 94–96, 110–12, 114, 119, 121, 123, 128–30, 132, 135, 136, 146, 189, 190, 202, 205, 217, 218, 222, 227 meme stocks, xii–xiv, 5, 7–9, 11, 12, 14, 22, 30, 32–34, 36, 39, 40, 47, 54, 63, 67, 72, 73, 76, 100, 108, 123, 125, 127, 129, 132–33, 135, 137–40, 146, 147, 153–55, 157, 159, 160, 162, 164, 169, 170, 178, 179, 181, 183, 185, 191, 193, 194, 198–99, 204–5, 208, 219, 220, 222, 227, 229, 230, 237, 238, 240, 246 AMC, 39, 93, 125, 127, 132, 169, 188, 220–21, 224–26 Bed Bath & Beyond, 115, 133, 188 BlackBerry, 93, 115, 133, 169, 178, 188, 224 bot activity and, 165, 166 GameStop, see GameStop, GameStop short squeeze insiders of, 224 Koss, 132, 169, 188, 224 margin debt and, 58 Naked, 132, 188 Nokia, 169, 178, 188 payment for order flow and, 207 Robinhood’s trading restrictions on, 187–89, 194, 195–200, 203, 206 Merton, Robert, 101, 102, 108 Microsoft, 46, 93 Mihm, Stephen, 48 millennials, 21, 26, 27, 56, 71, 88, 142, 143, 148, 162, 242, 246, 255 Minnis, Chad, 126, 157, 242 MoneyWatch, 59 monthly subscription services, 32 Morgan Stanley, 28, 55, 178, 219 Morningstar, 216, 244, 245, 254, 255 Motherboard, 131–32 Motter, John, 215–17, 226 Mudrick, Jason, 220–21 Mudrick Capital Management, 220 Mulligan, Finley, 230 Mulligan, Quinn, 142, 214 Munger, Charlie, 183–84, 241 Murphy, Paul, 78 Musk, Elon, 19, 75, 82–83, 92, 124, 143, 149, 152–53, 155–57, 160, 161, 167, 212, 216 tweets of, x, 60, 82, 83, 124, 144, 152–54, 161, 170 Must Asset Management, 221 mutual funds, 139, 151, 221, 234, 244, 245, 254–56 myopic loss aversion, 236 N Naked Brand, 132, 188 Nasdaq, 60, 92, 98, 104 Nasdaq Whale, 98, 104–6, 108, 109, 227 Nathan, Dan, 192 National Council on Problem Gambling, 31, 57 National Futures Association, 118 Nations, Scott, 99 Nations Indexes, 99 NCAA Basketball, 57 Netflix, x–xi, 15, 50, 98, 133, 208 Netscape, 24 Neumann, Adam, 105 New Yorker, 143 New York Mets, 8, 161 New York Post, 124, 172 New York Stock Exchange, 49 New York University, 20, 82, 177 Nikola, 64 NIO, 120 Nobel Prize, 101, 260 Nokia, 169, 178, 188 nudges, 31–32, 235–36 Nvidia, 98 O Obama, Barack, 13, 38 Ocasio-Cortez, Alexandria, 160, 197 Occupy Wall Street movement, 12, 125 Odean, Terrance, 235, 238, 243 Odey, Crispin, 126 Ohanian, Alexis, 12, 37–38, 125 O’Mara, Margaret, 38, 156, 157 Omega Family Office, 191 O’Neal, Shaquille, 64 Oppenheimer, Robert, 83 options, 34–35, 99–107, 217 call, see call options delta and, 107, 108 losses and quick approval processes for, 103 put, 46, 99, 106, 111–12, 148 Robinhood and, 34–35, 102–4, 106, 108–9 Options Clearing Corporation, 102 P Pagel, Michaela, 235 Palantir Technologies, 120 Palihapitiya, Chamath, 143, 144, 152–53, 155, 157–58, 160, 164, 212, 234, 246, 253 Palm, 84 PalmPilot, 84 Pao, Ellen, 38 Paperwork Crisis, 49 Parker, Sean, 38 payment for order flow, 10, 33, 153, 196, 206–9 Penn National, 57 penny stocks, 60, 120, 133, 166, 167 Permit Capital, 223 Pershing Square Holdings, 56 Pets.com, 90 PetSmart, 89 Pew Research, 71 Physical Impossibility of Death in the Mind of Someone Living, The (Hirst), 7 Piggly Wiggly, 78–79 PiiQ Media, 166 PIMCO, 216 Plotkin, Gabriel, 41, 56, 67, 73, 80, 85, 86, 95–96, 110–12, 114–15, 116, 122, 123, 129, 130, 133, 140, 146, 148, 157, 158, 161, 191, 197, 213–14, 217, 218, 227, 240, 246, 250, 253 at congressional hearing, 6–11 Porsche, 77 Portnoy, Dave, 57, 152–55, 158–59, 161, 181, 188–89, 212 Povilanskas, Kaspar, 195 Pruzan, Jonathan, 219 Psaki, Jen, 192 Public.com, 196, 207, 209 pump and dump, 163 put options, 46, 99, 106, 111–12, 148 Q Qualcomm, 46 R RagingBull, 163 Random Walk Down Wall Street, A (Malkiel), 253 Raskob, John J., 150–52, 154, 156 Raytheon, 153–54 RC Ventures LLC, 114 Reagan, Ronald, 156, 234 Reddit, xi, xii, 11–12, 19, 22, 23, 25, 36–39, 41, 42, 107, 122, 125, 162, 164, 199 founding of, 37–38 Gill’s influence on, 141–42; see also Gill, Keith; WallStreetBets karma on, 47, 141–42 mechanics and demographics of, and GameStop, 37 offensive subreddits on, 38 r/ClassActionRobinHood, 196 r/GMEbagholders, 140 r/investing, ix, 46 r/wallstreetbets, see WallStreetBets Super Bowl ad of, 12 Volkswagen squeeze and, 78 Reddit Revolution, xv, 41, 42, 75, 99, 152, 170, 192, 206, 211, 219, 220, 230, 246, 261 see also GameStop, GameStop short squeeze; WallStreetBets rehypothecation, 80, 92 reinforcement learning, 35 Reminiscences of a Stock Operator (Lefèvre), 78 Renaissance Technologies, 237 retail trading, xiii, xiv, xvi, 4, 7, 9–14, 49, 56–59, 63–64, 66, 67, 81, 98, 140–41, 143, 169–70, 178, 181, 183, 186, 194, 218, 237, 238, 244, 247 retirement accounts and pension funds, 5, 13, 27, 31–32, 41, 69, 76, 77, 81, 171, 182, 234, 235, 245, 252, 255, 256 Rise of the Planet of the Apes, 135–36 RiskReversal Advisors, 192 Ritter, Jay, 63, 65 Roaring Kitty (Gill’s YouTube persona), 2, 18, 45, 48–49, 92, 130, 133, 144, 171, 174–75, 191, 211, 213 Roaring Kitty LLC, 171 Robinhood, xi, xiii, xv, 4–6, 13–14, 19, 22–35, 41–42, 50, 53, 55, 57, 61, 66, 70, 81, 98, 139, 141, 153, 154, 157, 158, 161, 176, 178, 183, 184, 187–90, 193, 194, 195–210, 212–13, 219, 237–38, 243, 245, 246, 259 account transfer fees of, 54 average revenue per user of, 66–67 Buffett on, 240–41 call options and, 97–98 Citadel and, 10, 11 clearinghouse of, 187 commissions and, 49, 50 customer loan write-offs of, 205 daily average revenue trades of, 59 daily deposit requirement of, 205 former regulators hired by, 239–40 founding of, 3, 23–25, 90 funding crisis of, 187–88, 193, 198, 203, 205–6 gamification and, 29–31 Gold accounts, 32, 58, 97, 202 growth of, 25–26, 50 herding events and, 238 Hertz and, 61 hyperactive traders and, 193, 202, 207, 236 initial public offering of, 200–201, 219 Instant accounts, 32 Kearns and, 103–4 lawsuits against, 196 margin loans of, 58–59, 205 median account balances with, 50, 54 options and, 34–35, 102–4, 106, 108–9 payment for order flow and, 10, 33, 196, 206–9 revenue from securities lending, 73 risky behavior encouraged by, 202–3 Robintrack and, 53, 61 SPACs and, 64 stimulus checks and, 56 Super Bowl ad of, 28, 30, 200 technical snafus by, 53–54 Top 100 Fund and, 61 trading restricted by, 187–89, 194, 195–200, 203, 206, 209 valuation of, 49 WallStreetBets and, 22–23 wholesalers and, 33–35, 49, 104, 106 Robin Hood (charitable foundation), 196–97 robo-advisers, xv, 27, 257–58 Betterment, 27, 54, 183, 193, 242, 257, 258, 261 SoFi, 27, 56, 57, 158 Rockefeller, John D., 9 Rodriguez, Alex, 64 Rogers, Will, 163 Rogozinski, Jaime, 23, 39, 46, 50, 53, 55, 70–71, 97, 122, 138, 144, 190, 231 Roper, Barbara, 29–30, 35, 54, 185, 241 Rozanski, Jeffrey, 46 Rukeyser, Louis, 156 Russell 2000 Value Index, 125, 191 S S3 Partners, 76, 81, 130, 133, 170, 217 SAC Capital Advisors, 7, 110 Sanders, Bernie, 65–66, 198 S&P (Standard & Poor’s), 83 S&P Dow Jones Indices, 70, 254 S&P 500, 76 Sanford C.

Quantitative Trading: How to Build Your Own Algorithmic Trading Business
by Ernie Chan
Published 17 Nov 2008

Fortunately, there is a field of financial research called “behavioral finance” (Thaler, 1994) that studies irrational financial decision making. I will try to highlight a few of the common irrational behaviors that affect trading. The first behavioral bias is known variously as the endowment effect, status quo bias, or loss aversion. The first two effects cause some traders to hold on to a losing position for too long, because traders (and people in general) give too much preference to the status quo (the status quo bias), or because they demand much more P1: JYS c06 JWBK321-Chan September 24, 2008 Money and Risk Management 13:57 Printer: Yet to come 109 to give up the stock than what they would pay to acquire it (the endowment effect).

…

As I argued in the risk management section, there are rational reasons to hold on to a losing position (e.g., when you expect mean-reverting behavior); however, these behavioral biases cause traders to hold on to losing positions even when there is no rational reason (e.g., when you expect trending behavior, and the trend is such that your positions will lose even more). At the same time, the loss aversion bias causes some traders to exit their profitable positions too soon, even if holding longer will lead to a larger profit on average. Why do they exit the profitable positions so soon? Because the pain from possibly losing some of the current profits outweighs the pleasure from gaining higher profits.

…

See Sharpe ratio Information, slow diffusion of, 117–118 Interactive Brokers, 15, 73, 82, 83 Investors, herdlike behavior of, 118–119 J January effect, 143–146 backtesting, 144–146 Java, 80, 85 P1: JYS ind JWBK321-Chan October 2, 2008 14:7 178 K Kalman filter, 116 Kavanaugh, Paul, 149 Kelly formula, 95, 97, 100–103, 105, 107, 153, 161 calculating the optimal allocation based on, 100–102 calculating the optimal leverage based on, 99 simple derivation of, when return distribution is Gaussian, 112–113 Kerviel, Jérôme, 160 Khandani, Amir, 104 Kirk Report, 10 L LeSage, James, 168 Leverage, 5, 95–103 Liquidnet, 73 Lo, Andrew, 104 Logical Information Machines, 35, 36 Long-only versus market-neutral strategies, calculating Sharpe ratio for, 45–47 Long-Term Capital Management, 110, 157 Long-term wealth, maximizing, 96 Look-ahead bias, 51–52 Loss aversion, 108–109 M Market impact, 22 MarketQA (Quantitative Analytics), 35 Markov models, hidden, 116, 121 Printer: Yet to come INDEX R , 21, 32–34, MATLAB
137–139 calculating optimal allocation using Kelly formula, 100–102 a quick survey of, 163–168 using in automated trading systems, 80, 81, 83, 85 using to avoid look-ahead bias, 51–52 using to backtest January effect, 144–146 mean-reverting strategy with and without transaction costs, 61–65 year-on-year seasonal trending strategy, 146–148 using to calculate maximum drawdown and its duration, 48–50 using to calculate Sharpe ratio for long-only strategies, 46–47 using for pair trading, 56–58, 59–60 using to scrape web pages for financial data, 34 MCSI Barra, 35, 136 Mean-reverting versus momentum strategies, 116–119 Mean-reverting time series, calculation of the half-life of, 141–142 Millennium Partners, 12 Model risk, 107 ModelStation (Clarifi), 35 Momentum strategies, mean-reverting versus, 116–119 P1: JYS ind JWBK321-Chan October 2, 2008 14:7 Index Money and risk management, 95–113 optimal capital allocation and leverage, 95–103 psychological preparedness, 108–111 risk management, 103–108 Murphy, Kevin, 168 N National Association of Securities Dealers (NASD) Series 7 examination, 70 National Bureau of Economic Research, 10 Neural networks, 116 New York Mercantile Exchange (NYMEX), 16, 149 Northfield Information Services, 136 O Oanda, 37, 73 Octave, 33 O-Matrix, 33 Ornstein-Uhlenbeck formula, 140–141, 142 Out-of-sample testing, 53–55 P Pair trading of GLD and GDX, 55 Paper trading, 55 testing your system by, 89–90 Parameterless trading models, 54–55 PFG Futures, 73 Plus-tick rule, elimination of, 92, 120 Posit (ITG), 73 Position risk, 107 Printer: Yet to come 179 Post earnings announcement drift (PEAD), 118 Principal component analysis (PCA), 136–139 Profit and loss (P&L), 6, 89 curve, 20 Programming consultant, hiring a, 86–87 Psychological preparedness, 108–111 Q Qian, Edward, 154 Quantitative Analytics, 35 Quantitative Services Group, 136 Quantitative trading, 1–8 business case for, 4–8 demand on time, 5–7 marketing, nonnecessity of, 7–8 scalability, 5 the way forward, 8 special topics in, 115–156 exit strategy, 140–143 factor models, 133–139 high-frequency trading strategies, 151–153 high-leverage versus high-beta portfolio, 153–154 mean-reverting versus momentum strategies, 116–119 regime switching, 119–126 seasonal trading strategies, 143–151 stationarity and cointegration, 126–133 who can become a quantitative trader, 2–4 Quotes-plus.com, 37 P1: JYS ind JWBK321-Chan October 2, 2008 14:7 180 R Random walking, 116 REDIPlus trading platform (Goldman Sachs), 73, 82, 83, 84 Regime shifts, 25, 91–92 Regime switching, 119–126 academic attempts to model, 120–121 Markov, 121 using a machine learning tool to profit from, 122–126 Regulation T (SEC), 5, 14, 69–70 Renaissance Technologies Corporation, 104 Representativeness bias, 109 Reverse split, 38 Risk management, 103–108.

Beyond the Random Walk: A Guide to Stock Market Anomalies and Low Risk Investing
by Vijay Singal
Published 15 Jun 2004

Most investors suffer from the tendency to hold on to losers for too long because they are loss-averse and do not wish to realize a loss. Investors are also overconfident and do not believe that they made a bad decision. They hope that the stock will turn around. By the time they accept an error in judgment, it is too late. Investors also make a sharp distinction between paper losses or gains and realized losses or gains, fooling themselves into believing that a paper loss/gain is not a real loss/gain. Similarly and based on loss aversion, there is a tendency to realize profits quickly before the investment becomes a loss.

…

Consequently, the first worker is likely to be happier than the second. In the same vein, investors look at each stock individually, not as part of a portfolio as traditional economists assume. As a result, investors engage in mental accounting. They tend to value stocks that pay dividends more than stocks that pay capital gains. They tend to be loss-averse rather than risk averse. Some experiments find that investor behavior is consistent with frame dependence. For example, investors are known to hold losers for too long because they are averse to realizing a loss. On the other hand, investors sell winners too quickly because they don’t want to see the winner become a loser.

…

Journal of Finance 54(6), 2143–84. Kadlec, Gregory B., and John J. McConnell. 1994. The Effect of Market Segmentation and Illiquidity on Asset Prices: Evidence from Exchange Listings. Journal of Finance 49(2), 611–36. Kahneman, Daniel, Jack L. Knetsch, and Richard H. Thaler. 1991. Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias. Journal of Economic Perspectives 5(1), 193–206. Kahneman, Daniel, and Amos Tversky. 1979. Prospect Theory: An Analysis of Decision Under Risk. Econometrica 47(2), 263–92. Mackenzie, Craig. 1997. Where Are the Motives? A Problem with Evidence in the Work of Richard Thaler.

Irresistible: The Rise of Addictive Technology and the Business of Keeping Us Hooked
by Adam L. Alter
Published 15 Feb 2017

Back, and Stanley Schacter, Social Pressures in Informal Groups: A Study of Human Factors in Housing (Stanford, CA: Stanford University Press, 1950). Rewards are a: On the power of loss aversion and motivation: Thomas C. Schelling, “Self-Command in Practice, in Policy, and in a Theory of Rational Choice, American Economic Review 74, no. 2 (1984): 1–11; Jan Kubanek, Lawrence H. Snyder, and Richard A. Abrams, “Reward and Punishment Act as Distinct Factors in Guiding Behavior,” Cognition 139 (June 2015): 154–67; Ronald G. Fryer, Steven D. Levitt, John List, and Sally Sadoff, “Enhancing the Efficacy of Teacher Incentives Through Loss Aversion: A Field Experiment,” Working Paper 18237, National Bureau of Economic Research, Cambridge, MA, 2012; Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision under Risk,” Econometrica 47, no. 2 (March 1979): 263–92.

…

For the two students bidding up to $60 in my classroom, the motivation isn’t the thrill of winning $20—it’s the threat of losing to the other bidder. As neuroscientist Kent Berridge suggested, their facial expressions show that they want to keep bidding, but they’re certainly not liking the experience at all. You can see the same loss aversion even more clearly in so-called penny auction websites like Quibids.com, HappyBidDay.com, and Beezid.com. To begin using Beezid, for example, you buy a pack of bids. Packs range in size from forty bids (for $36, or 90 cents per bid) to one thousand bids (for $550, or 55 cents per bid). The Beezid site features hundreds of ongoing auctions for products like laptops, TVs, and headphones.

…

(IGN column), 178 Italian Job, The (movie), 191–92, 204 Jarecki, Andrew, 199 Jeong, Ken, 279–80 Jinx, The (real-life crime documentary), 199–200 Jobs, Steve, 1, 2, 4 John, Daymond, 279–80 Johnson, Eric, 209 Journey, 202 juice, 137–39 Just Press Play program, 304–5 Kagan, Jerome, 19–20 Kahneman, Daniel, 282 Kaiser Foundation, 245–46 Kappes, Heather, 161 Kardashian, Kim, 158 “Karma Police” (Radiohead), 195 karoshi (death from overworking), 186–87 Keas, 300–302 Kennedy, Joe, 279–80 khat leaf, 31 King, 137 Klosterman, Chuck, 109–10 Koenig, Sarah, 196, 197, 203 Kondo, Marie, 207–8 KonMari, 207–8 Kotler, Steven, 142 Kraft, Robert, 116 Krieger, Mike, 216–17 Kulagin, Mikhail, 172–73 Lancet, 296 Lantz, Frank, 164–65, 188–89, 300 Larson, Michael, 100–106 Larson, Teresa, 106 Lawrence, Andrew, 82–85 laziness, 305–6 leaderboards, 298 League of Legends (game), 228 learning, gamification of, 302–5 Lee, Hae Min, 196, 198, 200, 203 Lego, 174 Lewis, Michael, 119 Liar’s Poker (Lewis), 119 Life-Changing Magic of Tidying Up, The (Kondo), 207–8 likes/like button, 127–29, 217 Lindner, Emilee, 159 LinkedIn, 128 Litras, Janie, 103 Little Mister Cricket (game), 138–39 LiveOps, 306–7 Long, Ed, 103 Lord of the Rings (Tolkien), 305 loss aversion, 152–54 losses disguised as wins, 133–34 love, 75–78 Love and Addiction (Peele), 76 “Love Is Like Cocaine” (Fisher), 75–76 Lovematically, 128–29 Love (TV show), 212 Luckey, Palmer, 141–42 Lucky Larry’s Lobstermania (slot machine), 133–34 ludic loops, 177–79 Lumos Labs, 313 MacInnis, Cara, 265 Mad Men (TV show), 290 Making a Murderer (real-life crime documentary), 199–200 mapping, 139 marathon runners, and goal-setting, 95–97 Marks, Isaac, 76–77 Massachusetts Institute of Technology, 275 Matheus, Kayla, 282–83 Medalia, Hilla, 252 medical benefits, of gamification, 309–12 Meier, Darleen, 206–7 melatonin, 69–70 memory, and addiction, 57–60 micro-cliffhangers, 205–8 microfeedback, 136–37 Microsoft, 28–29 Milner, Peter, 52–57, 67 Miyamoto, Shigeru, 147–49, 155, 166 Mochon, Daniel, 173 Moment (app), 13–15 Morrissey, Tracie, 159 Moti, 282–83 motivated perception, 144–45 motivational interviewing, 258–62 MUDs (multiuser dungeons), 227–28 Murphy, Morgan, 48 Muscat, Luke, 164 Myst, 3 Myst (game), 178 nail-biting, 267–69 Nanya, 186 National Public Radio, 196 Nature, 312 NBC News Online, 197 Neanderthals, 30 near wins, 145–46, 181–83 negative reinforcement, 21 Netflix, 3, 199, 208, 210–12, 287–89, 291 NeuroRacer, 312 New York Times, 48, 141, 215–16 Nguyen, Dong, 42–43 nicotine, 31 nicotine gum, 267 Nintendo, 148, 171 Nixon, Richard, 47–48 no. 3 heroin, 46 no. 4 heroin, 47 nomophobia, 15 Norton, Michael, 173 O’Brien, Conan, 84, 243 obsession, 20–21 obsessive passion, 21–22 Oculus VR, 140–42 Olds, James, 52–57, 58, 67 O’Neill, Essena, 220–21 online shopping, 4 on-the-job training, gamification of, 308–9 opium, 31 optimal distinctiveness, 226 origami, 173–74 overcoming addictive behaviors, 263–92 behavioral architecture and, 273–92 distraction and, 267–73 habits and, 268–73 replacing bad routines with good, 268–71 subconscious attraction to ideas railed against and, 264–65 willpower, role of, 265–66 overworking, 186–88 pain, and gamification, 309–10 Pajitnov, Alexey, 170, 171, 172, 173 Parkinson’s disease patients behavioral addictions as side effect of drug treatments for, 82–85 overcoming small hurdles and, 93–95 Paskin, Willa, 212 passion, 21 Patrick, Vanessa, 272 Pavlok, 279–81 Peele, Stanton, 76, 77–79, 88 Pelling, Nick, 298, 306 Pemberton, John, 36–38, 273 Penfield, Wilder, 19 Penn, Hugh, 46 penny auction websites, 152–55 Peretz, Jeff, 194–94 perfectionism, 107–9 Perry, Steve, 202, 203 Petrie, Ryan, 226–27 Pettijohn, Adrienne, 103 Pfizer, 301–2 Phelps, Andy, 305 Philips Sonicare, 300 pineal gland, 69–70 Pinterest, 122 pituri plant, 31 planning fallacy, 289–90 “pleasure center” of brain, 55 points, 298, 299 Pokémon (game), 155 Pokhilko, Vladimir, 170 Polk, Sam, 118–19 Polkus, Laura, 216 Pommerening, Katherine, 41–42 Popular Science, 17 pornography, 4, 265–66 post-play, 208, 210–12 post traumatic stress disorder (PTSD), gamification as intervention for, 311–12 Powell, Mike, 100 Power of Habit, The (Duhigg), 268 Prelec, Dražen, 188 predatory video games, 155–59 Press Your Luck (TV show), 101–5 progress, 147–66 barriers to entry, lack of, 161–64 beginner’s luck and, 159–62 Dollar Auction Game, trap in, 149–52 energy systems, use of, 155–57 hooks in games and, 149–55 penny auction websites and, 152–55 positive feedback and, 158–59 smartphone delivery of games and, 164–66 proximity of temptation, 273–77 psychological response to experience of addiction, 73–75, 77–79 Pullen, John Patrick, 8 punding behaviors, 81–82 punishment, in breaking habits, 278–81 PurseForum, 207 Quest to Learn (Q2L), 302–4 Quibids.com, 152 Radiohead, 195 Rae, Cosette, 178–79, 249, 274 Raising Men Lawn Care, 307–8 rat experiments, of Olds and Milner, 52–57 readiness ruler, 259 Realism, 269–70 real-life crime documentaries, 196–201 Reddit, 122–25 red light, 69 regrams, 217 reinforcing good behaviors, 282–84 relational spending, 284 repression, 264 reSTART, 17–18, 62–63, 178, 228, 248–50, 255 reward, of habits, 268–69 Ricciardi, Laura, 199 Rift (game), 140–42 Robins, Lee, 50–52, 60, 67 Rochester School of Technology Just Press Play program, 304–5 Rolling Stone, 196 routine, of habits, 268–69 Routtenberg, Aryeh, 56–59 Rustichini, Aldo, 315 Ryan, Maureen, 203 Rylander, Gösta, 81, 84 Sacca, Chris, 140 Sales, Nancy Jo, 41–42 Saltsman, Adam, 155–56, 163–64 SAT vocabulary learning, gamification of, 296–97 Schachter, Stanley, 275–76 Schreiber, Katherine, 112–13, 115, 185 Schüll, Natasha Dow, 130, 134–35, 155, 183 Science, 168 Sedaris, David, 113–14 Self-Determination Theory (SDT), 260–62 “September” (Earth, Wind & Fire), 194–95, 196 Serial (podcast), 196–99, 200, 203, 204 Sesame Street (TV show), 247 Sethi, Maneesh, 279, 280–81 sexuality, 264–65 Shlam, Shosh, 252 shopping, compulsive, 205–8 Shubik, Martin, 149–51 Sign of the Zodiac (game), 130, 131 Sim, Leslie, 112–13, 114–15, 185–86 Simester, Duncan, 188 Simmons, Bill, 140 Simpsons, The (TV show), 145–46 Singer, Robert, 264 SiteJabber.com, 154 Sitzmann, Traci, 308–9 sleep deprivation, 68–70 Sleep Revolution, The (Huffington), 68–69 slot machines, 130–36, 183 smartphone addiction, 22 disruptive nature of, 15–16 overuse of, 13–15 Realism as treatment for, 269–70 scope of, 27–28 video games and, 164–66 smart watches, 113 Smith, Rodney, Jr., 307 Smith, Sandra, 112 SnowWorld, 309–10 SnŪzNLŪz, 278 social comparison, 118–19 social confirmation, 224–25 social interaction, 214–33 extensive online interactions at young ages, long-term effect of, 228–33 Hot or Not website and, 221–26 Instagram and, 216–17, 218 in multiuser games, 227–28 negative feedback in, 219–21 optimal distinctiveness and, 226 positive feedback in, 218–19 social confirmation and, 224–25 Sopranos, The (TV show), 201–3, 204 Space Invaders (game), 148 Spark Joy (Kondo), 208 Sperry, Roger, 19 SpongeBob SquarePants (TV show), 247 Steele, Robert, 48 Steiner-Adair, Catherine, 39, 41, 250–51 Stephen, Christian, 141 stereotypies, 84 Stern, Rick, 103 stopping rules, disruption of, 184–90 butt-brush effect and, 184 credit cards and, 188 exercise and, 185–86 overworking and, 186–88 video games and, 188–89 streaks, 115–16, 117 Strumsky, Dawn, 115 Strumsky, John, 115, 116 substance addiction, 8–9, 29–39 blurring of line behavioral addiction and, 81, 82–85 brain patterns and, 70–71 in early civilizations, 30–31 Freud’s research and experiments with cocaine and, 33–36 manufacturing process and, 31–32 Pemberton’s French Wine Coca (Coca-Cola) and, 37–38 punding behaviors and, 81–82 trial and error, discovery of drug effects by, 32 of Vietnam War veterans, 46–52 Sullivan, Roy, 111 Super Hexagon (game), 179–81 Super Mario Bros.

The Elements of Choice: Why the Way We Decide Matters
by Eric J. Johnson
Published 12 Oct 2021

This is demonstrated in Ungemach et al., “Translated Attributes: Aligning Consumers’ Choices and Goals Through Signposts.” 4. This example comes from Hardisty, Johnson, and Weber, “A Dirty Word or a Dirty World?,” study 2. These differences are usually described as loss aversion, the observation that people dislike losses more than equivalent gains. However, loss aversion is more of a label for the observation than an explanation. Query theory adds to the traditional explanation of loss aversion by involving memory retrieval as an essential process: see Wall et al., “Risky Choice Frames Shift the Structure and Emotional Valence of Internal Arguments: A Query Theory Account of the Unusual Disease Problem.” 5.

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(TV show), 70–72, 75 Johns Hopkins University, 186 Kahneman, Daniel, 139–40 Kang, Arum, 46–47 Kearns, Alexander, 283, 289–90, 350n Kedem, Assaf, 45–48, 103–4 kidney donors, 108–9, 338n Kienen, Anat, 140 Kipchoge, Eliud, 245–46 Klotz, Leidy, 247–48 Knetsch, Jack, 139–40 Krosnick, Jon, 187, 190 Kuhn, Gustav, 305 Kunreuther, Howard, 131–32 LaGuardia Airport, 21 larger-later outcome, 34–35, 37, 38 Larrick, Rick, 224–29, 234–35 Lauren’s Law, 339n lay theory, 301–2 LEDs (light-emitting diodes), 254 Lee, Kee Yeun, 50–51 LEED (Leadership in Energy and Environment Design), 248–49 Letterman, David, 4 Levin, Irwin, 59–62 Li, Ye, 67 Lichtenstein, Sarah, 101–2 life expectancy, 76–81, 136 annuities, 79–81, 336n Social Security benefits, 88–98 life-extension care, 310–13 light bulbs, 136–38, 254–55 load shedding, 23–24, 27, 28, 44, 61 local warming, 67–68 Loewenstein, George, 174–75 London Zoo, 57 loss aversion, 346n Lotz, Sebastian, 134–35, 143, 144–45 Lui, Kaiya, 136–37 Lulu, 204 lumens, 254 Lyft, 323 Lynch, John, 213–14 malicious choice architecture, 15–19, 314–18 Apple’s ad tracking, 16–18 dark patterns, 18–19, 81, 127–29, 267, 315 life expectancy and financial decisions, 79–80 sludge, 18–19, 267 Verizon’s privacy setting, 315–16 mandated-choice condition, 112–13 Mandel, Naomi, 65–66 marathon runners and target times, 245–47, 247 market design, 162–63 market makers, 288, 289, 350n Martin Van Buren High School, 162 mass defaults, 150–52, 154–55, 275 Match.com, 45 matching markets, 162–63 Mathematica, 212–13 McCabe, Jenny, 272 Mellers, Barbara, 352n Meloy, Margaret, 194–95 mentalists, 55–57, 302–5 mental priming force, 304–5, 320 menu calories, 237–38 menu design, 192–94, 214–20, 345n, 346n menu psychology, 215–19 Meszaros, Jacqueline, 131–32 meta-analysis, 13, 142–46, 167 health insurance, 179–80, 181–82 metrics, 164, 238–40 making meaningful, 250–59 scaling, 260–63 straight line, 240–44 targets, 245–50 Microsoft Bing, 18 computer mice, 231–32 miles per gallon (MPG), 223–29, 227 fuel economy labels, 232, 232–35, 233 Millennium High School, 169 mind writing, 57, 58, 305 “moral algebra,” 74 movie choices, 1, 202–5, 267 Netflix, 267–73 MPG illusion, 225–28, 230 Mullainathan, Sendhil, 104–5 Music Genome Project, 277 “mystery shoppers,” 104 National Bureau of Economic Research (NBER), 196–99 National Public Radio, 315 negative options, 127 neglect, 148, 308–14, 322–23 end-of-life decisions, 310–13 Netflix, 267–73 Netherlands and organ donation, 110, 113–14, 115 newspaper subscriptions, 314–15 New York City restaurant grades, 254 salt content labels, 256–59, 258 school choice, 159–60, 162–66, 168–69, 182–83 taxis and tipping, 12, 128–29 New York City Taxi and Limousine Commission, 12 New York High School Directory, 163–66, 168 New York–Presbyterian Hospital, 107–8 New York Times, 17–18, 245, 290 “Vows” section, 44–45 New York University, 200 NFL draft books, 230 Nike Vaporfly, 245 no-action defaults.

Effective Programming: More Than Writing Code
by Jeff Atwood
Published 3 Jul 2012

According to standard economic theory, the price reduction shouldn’t have led to any behavior change, but it did. Ariely’s theory is that for normal transactions, we consider both upside and downside. But when something is free, we forget about the downside. “Free” makes us perceive what is being offered as immensely more valuable than it really is. Humans are loss-averse; when considering a normal purchase, loss-aversion comes into play. But when an item is free, there is no visible possibility of loss. You will tend to overestimate the value of items you get for free. Resist this by viewing free stuff skeptically rather than welcoming it with open arms. If it was really that great, why would it be free?

…

When it comes to happiness, frequency is more important than intensity. Embrace the idea that lots of small, pleasurable purchases are actually more effective than a single giant one. 4. Buy less insurance Humans adapt readily to both positive and negative change. Extended warranties and insurance prey on your impulse for loss aversion, but because we are so adaptable, people experience far less regret than they anticipate when their purchases don’t work out. Furthermore, having the easy “out” of insurance or a generous return policy can paradoxically lead to even more angst and unhappiness because people deprived themselves of the emotional benefit of full commitment.

Working Identity, Updated Edition, With a New Preface: Unconventional Strategies for Reinventing Your Career
by Herminia Ibarra
Published 17 Oct 2023

Her experiments had thrown up an unexpected result, and she found herself wondering whether to quit one or more of her side gigs to invest more heavily in another. Still, she found it hard to put an end to the track that had defined her for so long. Sophie was experiencing what behavioral economist Dan Ariely calls “loss aversion,” our human tendency to want to keep all options open.15 In a famous series of experiments profiled in the New York Times, Ariely showed just how far we’ll go to keep from closing doors. Students in the experiment played a computer game that paid real cash to look for money behind three doors on the screen.

…

Ignoring those disappearing doors turned out to be impossible. In fact, participants wasted so many clicks rushing back to reopen doors that their earnings dropped 15 percent. And they frenetically continued keeping all their doors open even as penalties for switching got stiffer, costing not just clicks but cash fees. Loss aversion shows up in many forms, including our possible selves. Writing in the scientific journal Nature, University of Virginia professor Gabrielle Adams and coauthors report that people prefer adding things to getting rid of things.16 When asked to improve something—a Lego brick structure, an essay, a golf course, or a university—participants in their studies tended to suggest adding new things rather than stripping back what was already there, even when additions lead to subpar results.

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See financial issues interviews: analysis of, 193–194 methodology of, 191–193, 192f jelling events, 166–168 journaling, 97 Kets de Vries, Manfred, 165–166 learning-by-doing practice, 2, 183, 188. See also action Levinson, Daniel, 141–142 Lifton, Robert, 135 liminality, 59, 72. See also between-identity period London Writer’s Salon, 145–146 loss aversion, 123 making sense of changes. See sense-making Markus, Hazel, 42 McKenna, Elizabeth, 84 mentors: between-identity period and, 52, 58, 61, 63, 69, 70 connections with, 24, 129, 130–131, 135, 138, 141–142, 144, 148, 150 (see also guiding figures) experimentation support from, 103–104, 108 reinvention of working identity with, 20 midlife crises, 141, 190–191 narratives.

The Art of Execution: How the World's Best Investors Get It Wrong and Still Make Millions
by Lee Freeman-Shor
Published 8 Sep 2015

That night we stay out until 3:00am, have two helpings of chocolate decadence, and a variety of Aquavit at a Norwegian restaurant.”37 4. Fear The research of Shlomo Benartzi and Richard Thaler also showed that the pain of a short-term loss overpowers the pleasure of a long-term gain. This myopic (short-term) focus and a hatred of losing they called myopic loss aversion.38 This produces a fear which turns many investors into Raiders when a share starts doing well. The findings of Terrance Odean suggest that this problem has grown thanks to the unprecedented immediacy of the internet. He discovered that people who traded via telephone from 1991 to 1996 outperformed the market by 2.4% per year on average.

…

Choices in Repeated Gambles and Retirement Investments’, Management Science, by Shlomo Benartzi and Richard Thaler (1999). They showed that the pain of a short-term loss overpowers the pleasure of a long-term gain. This myopic (short-term) focus and a hatred of losing is what Thaler and Benartzi called myopic loss aversion. 39 ‘Online Investors: Do the Slow Die First?, EFA, by Brad Barber and Terrance Odean (1999). 40 ‘Trading is hazardous to your wealth: the common stock investment performance of individual investors’, The Journal of Finance, by Brad Barber and Terrance Odean (2000). 41 Kahneman and Tversky (1979). 42 ‘Focusing on the Forgone: How Value Can Appear So Different to Buyers and Sellers’, Journal of Consumer Research, by Ziv Carmon and Dan Ariely (2000). 43 The Psychology of Finance, by Lars Tvede (1999). 44 More Than You Know, by Michael Mauboussin (2006). 45 Mauboussin (2006). 46 Mean Genes, by Terry Burnham and Jay Phelan (2001). 47 Lynch (2000). 48 Thaler and Johnson (1990). 49 ‘Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency’, Journal of Finance, by Narasimhan Jegadeesh and Sheridan Titman (1993). 50 ‘Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends?’

Cheap: The High Cost of Discount Culture
by Ellen Ruppel Shell
Published 2 Jul 2009

But in the modern world these cognitive shortcuts sometimes lead us astray. When it comes to money, focusing too hard on scowls and growls can cause us to act in a way that seems irrational and can ultimately harm rather than help us. As illustration of this, Kahneman and Tversky evoked the universal phenomenon of loss aversion, the tendency of most people to strongly prefer avoiding losses rather than acquiring gains. At first blush this sounds counterintuitive. Doesn’t everyone want to win? The answer, of course, is yes, but not as much as we don’t want to lose. If you don’t play, you can’t win, but you also can’t lose.

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Not playing results in lost opportunities, but scientists have shown that humans are not wired to spontaneously factor in missed opportunities, particularly when those opportunities are projected far into the future. We are wired, however, to worry a good deal about losing. And when it comes to feelings of loss, it is not necessarily the actual loss but the perception of loss that keeps us from acting in what would seem to be a rational manner. Loss aversion is what spurs a scorned lover to cling to a bad relationship, an unhappy worker to cling to a bad job, and unhappy stockholders to cling to a plummeting stock rather than sell the loser and invest the proceeds in something more promising. In the latter case, the only logical reason to hold a stock is that you believe it is likely to grow in value.

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Kresge Company Kristof, Nicholas Kroger Supermarkets Krummeck, Elsie labor arbitrage labor exploitation in China, worldwide effects of labor movement labor unions Landsman, Janet Lasch, Christopher Lawrence, Robert Lawrie, George lean-retailing techniques Leonhardt, David Les Halles Levi Strauss Levitt, Alfred Levitt, William Levitt and Sons Levy, Leon Lichtenstein, Donald Lichtenstein, Nelson The Limited Lindell, Jens Lindgren, Charlotte Linnaeus, Carl livestock industry Locke, Richard Long, Huey P. Lord & Taylor Los Angeles Times loss aversion loss leaders Lowenstein, George Lowe’s Lundgren, Gillis luxury goods LVMH Macy’s Madoff, Michael mail-in rebates mail-order business mainstream retailers discount sections in markdowns by, growth of malls Gruen’s architectural designs improving patron satisfaction with mall attributes outlet (See outlet malls) Malmendier, Ulrike Mammoth Mart mangrove forest, shrimp farming’s impact on Mankiw, Gregory Mansfield, Jayne manual labor manufacturer’s suggested price (MSP) markdown money markdowns free of imported goods by mainstream retailers optimization of seasonal variations setting amount of types of market value Marshall Field’s mass production adoption of European-style techniques and clothing market cotton gin Ford’s assembly line gun manufacture home construction Matlock, Larry Mattel mattress industry maximum price regulations, during World War II, Maxwell, Sarah May Department Stores McDonald, David McDonald’s McGovern, Charles McKinley, William McNair, Malcolm P.

The Age of the Infovore: Succeeding in the Information Economy
by Tyler Cowen
Published 25 May 2010

But in the laboratory subjects typically are more averse to the prospect framed in terms of a loss (“loss aversion”). More specifically, once the outcome is framed in terms of a loss, people will accept greater gambles to try to avoid any loss at all, compared to the risks they will take when the position is framed in terms of gains. In the study, the autistic subjects did significantly better at seeing that the talk of “loss” and “gain” was mere framing and that the two options should be treated the same, although they too showed some degree of loss aversion. Skin conductance tests run during the experiment indicated that the autistics reacted less emotionally to framing the one option in terms of loss rather than gain.

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(Carr), 54 iTunes, 4, 5, 43 Jaiku, 74 James, Jamie, 182 Japan, 74, 207, 218–19 Jefferson, Thomas, 25, 166, 200 Johnson, Samuel, 25, 166 Jordan, Rita R., 195 Joyce, James, 166 Kant, Immanuel, 203–4 Keillor, Frank, 103 Kendall, Joshua, 29 Kidmondo, 9 Kindle, 43, 62 Klein, Naomi, 198 Knecht, Joseph (fictional character), 160–66 Krugman, Paul, 111–12 Lamoureux, Hugo, 190 late-talking children, 26 Laurie, Hugh, 154 least-common-denominator effect, 134 libraries, 43 Lil’Grams, 9 LinkedIn, 83 literature, 139, 146, 147–48, 170–71. See also Holmes, Sherlock LiveJournal discussion group, 35 Living to Tell the Tale (Márquez), 120 local processing or perception, 18, 19, 36 Locke, John, 177, 204 The Lord of the Rings (Tolkien), 127 loss aversion, 196 lunch, duration of, 43 Mackenzie, Henry, 168 macroeconomics, 138 magazines, 44 manipulation, 139–41 Mankiw, Greg, 111–12, 114 mantras, 95 The Man Who Made Lists (Kendall), 29 Marginal Revolution blog, 1 market economy, 201 Márquez, Gabriel García, 120 marriage, 217–18 Marx, Karl, 216 mathematics, 19, 24, 153 Maxim, 44 McLuhan, Marshall, 65–66 media coverage, 34, 135–36 meditation, 94–95, 96 meetways.com, 131 Mehrling, Perry, 96–97 Melville, Herman, 166 memory, 18, 130, 195 Mendel, Gregor, 25, 166 mental ordering.

How Democracy Ends
by David Runciman
Published 9 May 2018

When flooding or air pollution or water scarcity have become an acute threat, pragmatic authoritarianism has delivered on its promise to prioritise immediate results over long-term gains. It has much less to worry about when it comes to respecting the views of dissenters. But that is not enough to tip the balance in mature democracies. There the trade-off works the other way. Human beings tend to suffer from loss aversion: we don’t like to give up what we think is ours by right, regardless of the compensations on offer. It is very hard to imagine the citizens of Western democracies acquiescing in the loss of the personal dignity that comes with being able to kick the bastards out, even if it means bearing a collective material cost.

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The only thing worse than letting everyone vote is telling some people that they no longer qualify. Never mind who sets the exam, who is going to tell us that we’ve failed? Mill was right: democracy comes after epistocracy, not before. You can’t run the experiment in reverse. The cognitive biases that epistocracy is meant to rescue us from are what will ultimately scupper it. Loss aversion makes it more painful to be deprived of something we have that doesn’t always work than something we don’t have that might. It’s like the old joke. Q: ‘Do you know the way to Dublin?’ A: ‘Well, I wouldn’t start from here.’ How do we get to a better politics? Well, maybe we shouldn’t start from here.

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.: Pax Technica, 197–8, 205–6 Hungary, 175 I Iceland, 162, 163 identity politics, 72, 150, 178, 202–3 immigration, 55, 68, 183, 184, 210 India conspiracy theories, 65–6 independence, 120, 121 movement politics, 149 political enfranchisement, 76 pollution, 89 reform, 77 technology, 121–2 inequality and corporations, 131 and digital technology, 203 and environment, 90 and populism, 77–8 and violence, 78–80 information technology, 6–8, 196; see also internet interconnectedness, 112–15 International Monetary Fund (IMF), 32 internet, 152–3, 163 advertising, 157, 159 decision-making, 161 governance of, 198 invention of, 120 and liberation, 195–6 and surveillance, 153–5 of things, 197 utopias, 194–5 see also digital revolution Iraq War (2003), 75 Italy, 50, 148, 162 J Japan, 207, 208, 209–11 Hiroshima, 83–4, 84–5, 93–4 immigration, 210 Nagasaki, 94 population, 209 violence, 210 Jaurès, Jean, 71 Jews conspiracy theories, 65, 66, 68 Dreyfus Affair, 69 Holocaust, 84 juntas, 38, 50 K Kazcynski, Jaroslaw, 65, 66 Kazcynski, Lech, 66 Kennedy, President John F., 87–8, 108 Khrushchev, President Nikita, 108 Kimera Systems (digital technology company), 189 Kissinger, Henry, 56, 95, 96 knowledge acquisition of, 153 and discrimination, 180 internet and, 153 political, 188–9 and power, 186–7, 204 social, 196 social scientific, 183 Krugman, Paul, 90 Kubrick, Stanley: Dr Strangelove or: How I Learned to Stop Worrying and Love the Bomb (film), 95–6, 109 L Land, Nick, 165–7 Le Pen, marine, 149 Lenin, Vladimir Ilych: The State and Revolution, 171 libertarianism, 194 Lilla, mark, 150 Limbaugh, Rush, 20 Lincoln, President Abraham, 14 Lloyd George, David, 71 Long, Huey, 49 loss aversion, 175, 188 Luttwak, Edward: Coup D’État: A Practical Handbook, 41, 44 M McCarthy, Cormac: The Road, 113, 118–19 McGinnis, Joe: The Selling of the President, 158 machines, 121–2, 125–6, 127, 196, 197, 199, 200–201, 202, 205, 219; see also artificial intelligence; computers; robots; technocracy; technology McKinley, President William, 74 Macron, President Emanuel, 148, 149–50 Man on Wire (film), 117–18 Marx, Karl: ‘The Fragment on machines’, 196–7 Marxism-Leninism, 171 Mason, Paul: Postcapitalism, 196, 197, 199, 205 Mélenchon, Jean-Luc, 58 Mencius Moldbug see Yarvin, Curtis metadata, 154 Mill, John Stuart, 182–3, 185 Miller, Stephen, 13 mindlessness, 84, 86–8 Mitchell, David: The Bone Clocks, 113 Modi, Narendra, 65–6, 149 monarchs, 167 Monsanto (company): ‘The Desolate Year’, 88 Mugabe, President Robert, 48 Mullin, Chris: A Very British Coup, 58 N NATO, 59 Nazis, 85, 97, 99 Netherlands, 148 networks and anarchism, 193 and change, 196 interconnectedness, 112–15 political movements, 149 social 136, 151, 160, 177; see also Facebook; social media; Twitter utopian, 200 see also internet New York crime, 211 World Trade Center, 117–18 New York Times, 159–60 New Yorker (magazine), 82–3, 84, 106 news, fake, 64, 75, 98, 156, 157 Nixon, President Richard, 56, 90, 158 North Korea, 213 Nozick, Robert: Anarchy, State, and Utopia, 193–4, 195 nuclear disarmament, 107 Campaign for Nuclear Disarmament (CND), 94–5 nuclear weapons, 56, 83–4, 86, 94, 95, 96–7, 102, 103–104, 106, 107 Nunn, Sam, 95 O Obama, President Barack and climate change, 92 and conspiracy theory, 64 executive initiatives, 55 and inequality, 79 and Trump’s election, 13, 14, 15, 16, 18 oil companies, 131 Orban, Viktor, 175 Osborne, George, 208 Oxford and Cambridge Review, 120 P Papademos, Lucas, 39 Papandreou, Andreas, 27 Papandreou, George, 39 paranoia, 67, 74 Parent, Joe, 62 Parfit, Derek, 100, 202–3 Paul, Rand, 154 Perry, William, 95 pesticides, 87–9 Petit, Philippe, 117–18 Piergiacomi, Alessio, 167–8 Piketty, Thomas: Capital in the Twenty-First Century, 78 Pinker, Steven: The Better Angels of our Nature, 211 Plato, 179 Poland, 65, 66 police, 171 political parties, 214 artificiality, 145–6 charisma, 147 and identity politics, 150 as machines, 127 membership, 146, 147–8 ‘Net’, 162 partisan nature, 146 ‘Pirate’, 162 United States, 146–7, 221 politicians: and trust, 144–5, 164, 214 pollution, 89, 90 populism, 13, 175 and banality, 98–9 causes of, 67 and conspiracy theory, 65–7, 72, 168 and disconnect, 141 and economic growth, 192 and inequality, 77–8 and movement politics, 148–9 United States, 67–70, 73 and war, 75 precautionary principle, 100–101 pressure groups, 89 prisons, 151, 152, 212 Putin, President Vladimir, 157 R racism, 143 Rand, Ayn, 194 rational choice theory, 108–9 referendums, 47–8, 179, 183 France, 70 Turkey, 52 United Kingdom, 48 reform, 70, 71, 78, 79, 185; see also social change revolutions, 41, 78, 196; see also digital revolution risk, 101–5, 110–12, 116 robots, 7, 103, 111, 128–9, 130, 168, 210 Rockefeller, John D., 131–2 Roosevelt, President Theodore, 70, 71, 131 Russia ‘competitive authoritarianism’, 175 Cuban missile Crisis (1962), 107–8 data harvesting, 156 foreign policy, 30 S Sacco, Justine, 143 San Francisco, 162, 163 Sandberg, Cheryl, 137 Sanders, Bernie, 58, 149 Sarandon, Susan, 198 Scarry, Elaine: Thermonuclear Monarchy: Choosing between Democracy and Doom, 104 Scheidel, Walter: The Great Leveler, 78 Schlesinger, James, 56 Schultz, George, 95 Shita, Mouna, 189–90 Simon, Herbert, 153 el-Sisi, General Abdul Fatah, 48–9 slavery, 23, 35, 73, 123–4 sleepwalking, 115, 116, 117 Snowden, Edward, 151–2 Snyder, Timothy: On Tyranny: Twenty Lessons from the Twentieth Century, 97–8, 99 social change, 192, 219; see also reform social media, 149; see also Facebook; networks: social; Twitter socialism 171 Socrates, 38 Spain, 162 Stalinism, 99, 169, 171 suffrage, universal, 187–8 Sulzberger, Cyrus, 27, 28 surveillance, 152–5 Sweden, 162, 163 T taxation, 70, 72, 193 technocracy, 180–81, 191–2, 198, 205, 214 technology, 125, 126 corporations, 131 digital, 144, 151, 154, 161, 162–3; see also internet and dignity, 203 information, 7–8 and mortality, 24–5 and risk management, 105 and ‘the shock of the old’, 122 threat of, 103, 120–21 see also machines terrorism, 74 terrorists, 97, 212 Texas, 163 Thiel, Peter, 198 tightrope-walking, 117–18 totalitarianism, 98; see also tyrannies tribalism, 163–4 Truman, President Harry S., 84–5 Trump, Melania, 13 Trump, President Donald, 49 behaviour, 20–21, 22–3, 159, 173 and change, 198 and Charlottesville demonstrations, 4 and climate change, 93 and conspiracy theory, 64–5 and dignity, 173 election of, 1–2, 5, 13, 16–18, 19, 20, 25, 118, 149, 156 and executive aggrandisement, 92 and fake news, 157 inaugural speech, 11–14, 74 military’s influence on, 59 novels inspired by, 57 and nuclear war, 86 and political violence, 212 presidency, 213 and Silicon Valley firms, 137 supporters of, 98 on surveillance, 154 use of Twitter, 143 Tsipras, Alexis, 33–4, 209 Turkey, 50–3 conspiracy theories, 65, 66 coups, 50–2, 53, 66 and Cyprus, 38 elections, 51 Justice and Development Party (AKP), 51 movement politics, 149 referendum (2017), 52 Twitter, 65, 137, 142, 143, 156 tyrannies 61; see also totalitarianism U United Kingdom austerity, 208 Boer War, 75 Brexit, 48, 156, 179 Campaign for Nuclear Disarmament (CND), 94–5 Conservative Party, 146, 209 general election (2017), 95 Labour Party, 58, 70, 94–5, 148–9, 150 metadata, 154 political enfranchisement, 76 reform, 185 welfare state, 76 United States, 23–4, 25, 49–50 CIA, 28, 30 climate change, 92 Congress, 19 conspiracy theories, 62, 67 corporations, 132 Cuban missile Crisis (1962), 107–8 democratic failure, 2, 14 demonstrations, 4 direct democracy, 163 economic growth, 175 and Egypt compared, 49–50 environment, 87–90 and Greece, 30 immigration, 183 inequality, 79 judiciary, 19 McCarthyism, 67 metadata, 154 military, 17, 18 National Security Agency (NSA), 152 New Deal, 76, 78 and nuclear war, 86, 95 ‘pax Americana’, 198 pesticides, 87–9 political enfranchisement, 76 political parties Democrats, 15, 62, 64, 108, 146–7, 221 Republicans, 62, 146–7, 221 politicians, 164 populism, 67–70, 73 presidential elections, 14, 16, 54–5, 58, 68, 220–24 Kennedy, John F., 108 Trump, Donald, 1–2, 5, 13, 19, 20, 25, 118, 149 prisons, 212 reform, 70 rights, 72 road accidents, 211–12 Silicon Valley, 137, 204 ‘tyranny of the majority’, 142 violence, 73–4, 211–12 war with Spain (1890s), 75 see also Chicago; New York; San Francisco; Texas Uscinski, Joe, 62 utopias, 126, 194, 195, 201 V Varoufakis, Yanis, 32–4, 116–17, 209 Venezuela, 154–5, 208 violence, 6, 73–5 ancient Athens, 38 decline of, 13 and environmental disaster, 93 Greece, 31, 210 and inequality, 78–80 Japan, 210 online, 142–4 political, 16–17, 18 United States, 73–4, 211–12 voting AI and, 189–90 right to, 76, 183–4 systems, 182–3 see also elections W wars, 74–7 citizens’ experience of, 77 and conspiracy theory, 77 First World War, 76, 115 of national survival, 75 nuclear, 83–4, 84–5, 87, 93–7, 109, 213 and populism, 75 total, 76–7 United States and North Korea, 115 see also Cold War wealth: and death, 204; see also elites Weber, Max, 127, 131, 147, 164, 187–8 welfare states, 70, 76, 109–10 whistleblowers see Snowden, Edward Wilson, President Woodrow, 69, 71, 75–6 Y Yarvin, Curtis, 167 Z Zimbabwe, 48 Zuckerberg, Mark, 131, 133, 135, 137, 138, 140, 157–8, 215; see also Facebook ALSO FROM PROFILE BOOKS Political Order and Political Decay: From the Industrial Revolution to the Globalisation of Democracy Francis Fukuyama The most important book about the history and future of politics since The End of History.

Cultish: The Language of Fanaticism
by Amanda Montell
Published 14 Jun 2021

They’re the same reasons you might put off a necessary breakup: denial, listlessness, social stresses, fear they might seek revenge, lack of money, lack of outside support, doubt that you’ll be able to find something better, and the sheer hope that your current situation will improve—go back to how it was at the start—if only you hold on a few more months, commit a fraction more. The behavioral economic theory of loss aversion says that human beings generally feel losses (of time, money, pride, etc.) much more acutely than gains; so psychologically, we’re willing to do a lot of work to avoid looking defeats in the eye. Irrationally, we tend to stay in negative situations, from crappy relationships to lousy investments to cults, telling ourselves that a win is just around the corner, so we don’t have to admit to ourselves that things just didn’t work out and we should cut our losses.

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Irrationally, we tend to stay in negative situations, from crappy relationships to lousy investments to cults, telling ourselves that a win is just around the corner, so we don’t have to admit to ourselves that things just didn’t work out and we should cut our losses. It’s an emotional example of the sunk cost fallacy, or people’s tendency to think that resources already spent justify spending even more. We’ve been in it this long, we might as well keep going. As with confirmation bias, not even the smartest, most judicious people are immune to loss aversion. It’s deeply embedded. I’ve been in my fair share of toxic one-on-one relationships, and noticing the similarities between abusive partners and cultish leaders has been, to say the least, humbling. So while power abuse can look like poisoned punch and purple shrouds, the linchpin is what it sounds like.

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As I continued to traverse Scientology’s Bridge to Total Freedom (the path to going clear), I’d come to learn about supernatural concepts like Xenu the galactic overlord and invisible “body thetans” (spirits of ancient aliens that cling to humans and cause destruction). It would have been lunacy. But I’d have to keep going. The sunk cost fallacy and loss aversion would tell me I can’t quit. Not this far in. Plus, my superiors would insist, if I leave right now in the middle of an upper level of auditing, I could pull in misfortune. I could pull in disease, even death. One ex-Scientologist named Margery Wakefield, a longtime officer in the OSA (Office of Special Affairs, Scientology’s “intelligence agency”), wrote about how she was off-loaded (kicked out) in the early ’80s for her perceived decline in mental state.

The Undoing Project: A Friendship That Changed Our Minds
by Michael Lewis
Published 6 Dec 2016

In the second case, with the choice framed as a loss, they did the reverse, and ran the risk that they’d kill everyone. People did not choose between things. They chose between descriptions of things. Economists, and anyone else who wanted to believe that human beings were rational, could rationalize, or try to rationalize, loss aversion. But how did you rationalize this? Economists assumed that you could simply measure what people wanted from what they chose. But what if what you want changes with the context in which the options are offered to you? “It was a funny point to make because the point within psychology would have been banal,” the psychologist Richard Nisbett later said.

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“Science is a conversation and you have to compete for the right to be heard. And the competition has its rules. And the rules, oddly enough, are that you are tested on formal theory.” After they finally sent a draft of their paper to the economics journal Econometrica, Danny was perplexed by the editor’s response. “I was kind of hoping he’d say, ‘Loss aversion is a really cool idea.’ He said, ‘No, I like the math.’ I was sort of shattered.” By 1976, purely for marketing purposes, they changed their title to “Prospect Theory.” “The idea was to give the theory a completely distinct name that would have no associations whatsoever,” said Danny. “When you say ‘prospect theory,’ no one knows what you’re talking about.

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But by then it was clear that no matter how often people trained in statistics affirmed the truth of Danny and Amos’s work, people who weren’t would insist that they knew better. * * * Upon their arrival in North America, Amos and Danny had published a flurry of papers together. Mostly it was stuff they’d had in the works when they’d left Israel. But in the early 1980s what they wrote together was not done in the same way as before. Amos wrote a piece on loss aversion under both their names, to which Danny added a few stray paragraphs. Danny wrote up on his own what Amos had called “The Undoing Project,” titled it “The Simulation Heuristic,” and published it with both their names on top, in a book that collected their articles, along with others by students and colleagues.

SuperFreakonomics
by Steven D. Levitt and Stephen J. Dubner
Published 19 Oct 2009

Once the monkeys figured out that the two-grape researcher sometimes withheld the second grape and that the one-grape researcher sometimes added a bonus grape, the monkeys strongly preferred the one-grape researcher. A rational monkey wouldn’t have cared, but these irrational monkeys suffered from what psychologists call “loss aversion.” They behaved as if the pain from losing a grape was greater than the pleasure from gaining one. Up to now, the monkeys appeared to be as rational as humans in their use of money. But surely this last experiment showed the vast gulf that lay between monkey and man. Or did it? The fact is that similar experiments with human beings—day traders, for instance—had found that people make the same kind of irrational decisions at a nearly identical rate.

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Paul Glimcher, Colin Camerer, Ernst Fehr, and Russell Poldrack (Academic Press, Elsevier, 2009). / 212 “Nobody ever saw a dog”: see Adam Smith, An Inquiry into the Nature and Causes of the Wealth of Nations, ed. Edwin Cannon (University of Chicago Press, 1976; originally published in 1776). / 214 Day traders are also loss-averse: see Terrance Odean, “Are Investors Reluctant to Realize Their Losses?” Journal of Finance 53, no. 5 (October 1998). SEARCHABLE TERMS Note: Entries in this index, carried over verbatim from the print edition of this title, are unlikely to correspond to the pagination of any given e-book reader.

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See Genovese, Kitty, murder of Krueger, Alan, 62, 63–64 Kyoto Protocol, 115 laboratory experiments artificiality of, 123 and crash-test data, 153–55 games as, 108–11 See also specific researcher or experiment Lake Toba (Sumatra), volcanic eruption at, 189 Lakshminarayanan, Venkat, 212 LaSheena (prostitute), 19–20, 26, 27, 30, 54 Latham, John, 201, 202 Latham, Mike, 201 law of unintended consequences, 138–41 Leave It to Beaver (TV program), 102 LeMay, Curtis, 147 Lenin, Vladimir, 63 leverage, 193 Levitt, Steven D., 17 life expectancy, 20 life insurance, 94, 200 life span, extending the, 82–87 List, John, 113–20, 121, 123, 125 locavore movement, 166 LoJack (anti-theft device), 173–75 London, England, terrorism in, 92 loss aversion, 214 Lovelock, James, 166, 170, 177 Lowell, Mike, 92 macroeconomics, 16–17, 211 Madison, Wisconsin, home-sales data in, 39 Maintenance of Parents Act, Singapore, 106 Major League Baseball, birthdays among players of, 61, 62 malaria, experiment about, 177, 180, 181 manipulation, and altruism, 125 March of Dimes, 145 marijuana, 66 Martinelli, César, 27–28 Masters, Will, 142 Matthews, H.

Bootstrapped: Liberating Ourselves From the American Dream
by Alissa Quart
Published 14 Mar 2023

Even though on paper many were relatively affluent, their limited educations and the fact that they were older meant that they would be almost unhirable in their communities if they were laid off. Before many of the steel plants started to shut down, said Kemper, these men “sat high, the head of your table, the family member who makes all this fucking money.” Scholars have called this a state of “loss aversion.” Studies by cognitive psychologists, including landmark scholarship in 1992 by Amos Tversky and Daniel Kahneman, have found that losing—money, status, or anything of value—is twice as powerful as the joy resulting from gaining something beneficial. This creates a bias against loss. And that fear of disappearance explains why some choose to avoid losing ground over making gains and why Trump supporters who earned an average of $72,000 a year in 2016—a middle-class income—still had a fear of evaporating social status.

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“Now you have lost this status within your social structure. Look, people my age, the last thing we want to do is go out there and find another job.” This antagonism to even the appearance of diminishment is not the province of Trump supporters alone. In reporting my last book, I met progressive, loss-averse middle-class people, so I’d been thinking about this condition in its various forms for nearly a decade. Some were riven with anxiety about their futures and saw themselves as existing in a self-interested society. Kemper also saw why some of his men had felt disregarded by the Democratic Party and gone over to Trump.

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a hearty number of union members also went for Trump in 2020: While Biden won 57 percent of union households nationwide, still 40 percent supported Trump. Ian Kullgren, “Union Workers Weren’t a Lock for Biden. Here’s Why That Matters,” Bloomberg Law, November 10, 2020, https://news.bloomberglaw.com/daily-labor-report/union-workers-werent-a-lock-for-biden-heres-why-that-matters. evaporating social status: “Loss aversion” was also present, I think, along with white nationalism and all sort of other terrible impulses on dark day of January 6, when rioters stormed the US Capitol: violent, mostly white crowds gathered to falsely claim Trump’s presidential win. While some conspirators looked like stills from a hooligan version of a Wes Anderson movie, covered in Daniel Boone pelts, lost-cause-true-believers fighting “for our beloved President Donald J.

Innovation and Its Enemies
by Calestous Juma
Published 20 Mar 2017

The first is “status quo bias” and the second is “omission bias.”93 The former describes the disproportionate tendency to stick with the status quo when choosing between alternatives.94 For example, when asked to decide whether or not to participate in an organ donation program, offering the default option “yes” on the relevant insurance forms produces much higher participation rates than offering “no” as the default option and asking people to take action through opting into the program.95 New technologies that alter not only social habits but also what is perceived to be the status quo lead to negative responses because they threaten the customs that people have grown comfortable with. Loss aversion also causes people to underestimate the risks of doing nothing and sticking with the status quo. The tendency to favor inaction over action is called omission bias. This bias prevails even when people know the outcomes of omission and commission. For example, people tend to think that it is worse to vaccinate a child when the vaccination could cause harm than not to vaccinate, even though delivering the vaccination could significantly reduce the risk of harm through disease to the overall population and probably to the individual child as well.

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Thus, communication that encourages a positive cognitive approach to new technologies—for example, through emphasizing benefits of specific aspects of the technology—can increase the likelihood that people will accept the technology.100 It follows that challenges to innovation can be reduced by three psychological factors. First, new ideas are more easily adopted if they work through existing or entirely new habits rather than attempt to break existing ones. Second, framing the potential outcomes of new technologies in terms of gains or losses has a significant impact on whether loss aversion will produce risk-seeking or risk-avoiding behavior.101 Third, people are more likely to adopt a new technology when communication encourages positive attitudes toward it—for example, by emphasizing the concrete benefits of specific aspects of the technology. An equally important psychological factor in defining the challenge to new technologies is political empathy.

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Bauer, Andrew Jordan, Christoffer Green-Pedersen, and Adrienne Héritier, Dismantling Public Policy: Preferences, Strategies, and Effects (Oxford: Oxford University Press, 2012); Stegmaier, Kuhlmann, and Visser, “Discontinuation.” Chapter 1 1. Eyal Ert and Ido Erev, “On the Descriptive Value of Loss Aversion in Decisions under Risk: Six Clarifications,” Judgment and Decision Making 8, no. 3 (2013): 214–235. 2. National Academy of Engineering, Grand Challenges for Engineering (Washington, DC: NAE, 2008). 3. Graeme Laurie, Shawn Harmon, and Fabiana Arzuaga, “Foresighting Futures: Law, New Technologies, and the Challenges of Regulating for Uncertainty,” Law, Innovation and Technology 4, no. 1 (2012): 1–33. 4.

Finance and the Good Society
by Robert J. Shiller
Published 1 Jan 2012

Ine cient Markets: An Introduction to Behavioral Finance. Oxford: Oxford University Press. Shubik, Martin. 2009. A Proposal for a Federal Employment Reserve Authority. Economics Policy Note 09-5. New York: Levy Economics Institute. Simmons, Joseph P., and Nathan Novemsky. 2009. “From Loss Aversion to Loss Acceptance: Context E ects on Loss Aversion in Risky Choice.” Working Paper. New Haven, CT: Yale School of Management. Small, Deborah A., George Loewenstein, and Paul Slovic. 2007. “Sympathy and Callousness: The Impact of Deliberative Thought on Donations to Identi able and Statistical Victims.” Organizational Behavior and Human Decision Processes 102(2):143– 53.

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Certainly it is that, but it is unique among entertainment forms in that it cultivates and ampli es to a considerable extent human risk-taking impulses, sometimes with disastrous consequences. The puzzle comes down to why one would be willing to place even one single bet at a casino. Research by psychologists Daniel Kahneman and Amos Tversky has shown that people exhibit a tendency toward loss aversion.1 They are pathologically avoidant of even small losses. If o ered an asymmetrical bet on a coin toss—to win $20 if it comes up heads, to lose $10 if it comes up tails—most people will turn down the bet, even though it has a positive expected return of $5. How then are gambling casinos able to induce people to place bets with a negative expected return, and to do so again and again despite having experienced repeated losses?

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See also debt; mortgages Lobbying Disclosure Act of 1995, 90 lobbyists: for accountants’ groups, 102; for financial industry, 87, 88–89, 90, 92; former members of Congress, 88; gifts, 90; motives, 91; power, 87–88, 89–90, 92; public perceptions, 91; reforms, 92–93; regulation of, 88, 89–90, 92–93; roles, 87–89, 90–91 Locke, John, 145 Lorenz, Konrad, 226, 229 loss aversion, 160 lotteries, 140. See also gambling lottery-linked savings, 177 loyalty, 215 Lummis, Cynthia, 193 Luther, Martin, 141 Macmillan, Harold, 212 MacroMarkets LLC, 98 MacroShares, 98 Madoff, Bernard, 17, 95–96, 98 manics, 173 Mao Zedong, 174, 182, 233 market designers, 69–71, 73–74. See also financial engineers; financial innovations market makers, 57, 61–62.

Rationality: What It Is, Why It Seems Scarce, Why It Matters
by Steven Pinker
Published 14 Oct 2021

The only difference is the starting point, which frames the outcomes as a “gain” with the first choice and a “loss” with the second. With this shift in framing, people’s risk aversion goes out the window: now they seek a risk if it offers the hope of avoiding a loss. Kahneman and Tversky conclude that people are not risk-averse across the board, though they are loss-averse: they seek risk if it may avoid a loss.29 Once again, it’s not just in contrived gambles. Suppose you have been diagnosed with a life-threatening cancer and can have it treated either with surgery, which incurs some risk of dying on the operating table, or with radiation.30 Experimental participants are told that out of every 100 patients who chose surgery, 90 survived the operation, 68 were alive after a year, and 34 were alive after five years.

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See active open-mindedness, lack of cluster illusion, 146–48 cognitive reflection, lack of, 8–10, 311 collider fallacy, 261, 262–63 confirmation bias, 13–14, 142–43, 216, 290, 342n26 conjunction fallacy (Linda problem), 26–29, 115, 116, 156 correlation implies causation, 245–47, 251–52, 312, 321, 323–24, 329–30 data snooping, 145–46, 160 denying the antecedent, 83, 294 dieter’s fallacy, 101 discounting the future too steeply, 320 dread risk, 122 exponential growth bias, 10–12, 320–21 expressive rationality, 297–98 false dichotomy, 100 forbidden base rates, 62, 163–66 framing effects, 117–18, 168–70, 178, 188–92, 192–96, 321, 323, 349–50n27 garden of forking paths, 145, 185, 348n60 genetic fallacy, 91, 92–93, 291 guilt by association, 91 heretical counterfactuals, 64–65 hot hand fallacy, 131 hot hand fallacy fallacy, 131–32 hyperbolic discounting. See myopic temporal discounting illusory correlation, 245–46, 251–52, 321 imaginability. See availability heuristic intransitivity, 176, 185–88 irrelevant alternatives, sensitivity to, 177–78, 188–92, 350n8 loss aversion, 192–94 mañana fallacy, 101 Meadow’s fallacy (multiplying probabilities of interdependent events), 129–30, 131 money pump, 176, 180, 185, 187–88 monocausal fallacy, 260, 272–73 motte-and-bailey tactic (moving the goalposts), 88 moving the goalposts (motte-and-bailey), 88 myopic temporal discounting, 52–56, 54 myside bias, 294–96, 297, 312–13, 316, 317, 357n73 mythology mindset, 301–9 no true Scotsman fallacy, 88 openness to evidence, lack of, 310–11, 356–57n67 outrage, communal, 123–27 overconfidence, 20, 29–30, 33, 115, 216, 255, 323 paradoxical tactics, 58–62 paradox of the heap, 101 post hoc probability, 141–48, 160, 321 preference reversal, 52–53, 55 probability neglect, 11, 28, 321 propensity confused with probability, 21–22, 118, 139–40, 198, 216 prosecutor’s fallacy, 140–41 questionable research practices, 145–46, 160 regression to the mean, unawareness of, 254–56, 320, 353n13 regret avoidance, 17, 190 representativeness heuristic, 27, 155–56 resistance to evidence.

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See Bayesian reasoning evolution, 69, 173, 288, 308–9 excludability, 265 exclusive or (xor), 104–5, 133 expected utility Beccaria’s argument against cruel punishment and, 332–33 calculation of, 179–80 and certainty, 182–83, 188–90, 192 definition, 179 diminishing marginal utility and, 181–84, 181 emotions and, 179, 181–83, 190–92 Erasmus’s argument against war and, 331–32 intuitive calculation of, 180 lives and, 183–84 loss-aversion and, 192–94 maximization of, as rational choice, 174, 179 money and, 181–83 possibility, certainty and, 190–92, 195–96 risk-aversion and, 182–83, 192 as term, 179 See also rational choice exponential future discounting, 50–52, 53, 54 exponential growth bias, 10–12, 320–21 expressive rationality, 297–98 extraterrestrials, 286 Facebook, 127 fact-checking, 41, 300–301, 314, 316, 317–18 Fail Safe (film), 61 fairness, 164, 165, 217 fake news ease of spreading, 6 as entertainment, 306–7 fictionalized history as, 303 historical, 287–88 the irrationality crisis and, 284–85 openness to evidence vs., 311 religious miracles as, 287 rumor and, 308 false dichotomy, 100 family resemblance categories classical logic vs., 98–101, 346n28 definition, 99 informal fallacies arising from, 100–101 the representativeness heuristic and, 155 true/false values and, 100 See also deep learning famine, reduction of, 326 Farley, Tim, 321–23 Fauci, Anthony, 283–84 feminism, Enlightenment and, 336–37 finances, 237–38, 320–21 financial industry, 142–43, 165–66, 188, 327 Fischer, Bobby, 144 Fischhoff, Baruch, 323–24 Floyd, George, 123, 124–25 focal points, 124, 234–35 Forbes magazine, 313 forbidden base rates, 62, 163–66 forecasting accuracy of regression equations vs. human experts, 278–80 the conjunction rule and, 22–29 importance of, 22–23 superforecasters, 162–63 unpredictability of human behavior, 280–81 weather, 114, 127, 133, 220 Fore people, 153 formal fallacies affirming the consequent, 83, 85, 139 definition, 74, 83 denying the antecedent, 83, 294 detecting, 84 formal reconstruction exposes, 85–87 fortuitous randomization, 267 Foucault, Michel, 90 Fox News, 267–68 framing effects, 178, 188–96, 321, 323 Frank and Ernest (cartoon), 255 Franklin, Benjamin, 196 Freakonomics (Levitt and Dubner), 266 Frederick, Shane, 9–10, 50, 342nn17,19 freedom of speech, 41, 313–14 French Revolution, 337 frequency as factor in memory recall, 119 probability reframed as, 28–29, 117–18, 168–70, 349–50n27 frequentist interpretation of probability, 116, 117, 118, 128 Freud, Sigmund, 80, 90 full moon, and hospital ERs, 251–52 future, discounting of, 47–56, 320 See also goals—time-frame conflicts fuzzy categories.

Capital Allocators: How the World’s Elite Money Managers Lead and Invest
by Ted Seides
Published 23 Mar 2021

Competitive advantage Successful investing requires capturing an elusive “edge.” Michael Mauboussin uses the acronym “BAIT” to describe the competitive advantage a manager may have, and which allocators seek to identify.43 B: Behavioral – managing around behavioral biases, including overconfidence, confirmation bias, anchoring, loss aversion and recency bias. A: Analytical – processing the same information better than other investors, including raw intellectual horsepower or skill in portfolio construction. I: Informational – possessing better information than other participants, such as computational power over short time horizons.

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If you can’t calm yourself or slow yourself down, your fears will run away with you.” – Michael Mervosh Positivity “We are always plagued with our fears, doubts and insecurities. It doesn’t matter who you are in the world.” – Chatri Sityodtong “People generally put much more emphasis on bad things than good things. That emanates from this concept of loss aversion.” – Michael Mauboussin “Take yourself seriously, but not too seriously.” – Seth Masters “Private optimism and public despair are an interesting conflict. On one side people are quite optimistic about themselves, but at the same time are pessimistic about the rest of the world.” – Tali Sharot “There is only so much that fits into a basket of worries.

Actionable Gamification: Beyond Points, Badges and Leaderboards
by Yu-Kai Chou
Published 13 Apr 2015

That was quite an impressive figure, mainly motivated by Core Drive 4: Ownership & Possession as well as Core Drive 8: Loss & Avoidance. In fact, in higher-level Octalysis studies, you will see that building Core Drive 4 often reinforces the power of Core Drive 8, and that the Endowment Effect connects directly to our irrational sense of Loss Aversion). Monopolizing Billions Another great example of Collection Sets is seen in McDonald’s Monopoly Game28. McDonald’s wants people to buy more fast food from them - the Desired Action, so it created the McDonald’s Monopoly game where every time you hit the Win-State of “buying more burgers and fries” you will get a piece of property on the Monopoly Board.

…

This is because gaining something and preventing a loss is incredibly different from the standpoint of motivation. Studies234 have shown over and over that we are much more likely to change our behavior to avoid a loss than to make a gain. It forces us to act differently and plays by different mental rules. In fact, Nobel Prize winner Daniel Kahneman indicates that on average, we are twice as loss-averse compared to seeking a gain5. This means that we have a tendency to only take on a risk if we believed the potential gain would be double the potential loss if the risk were realized. Through using the Octalysis Framework, this differentiation improves behavioral design by specifically identifying opportunities to integrate proactive loss-avoidance mechanics that generate a more suble set of motivational dynamics.

…

Have you ever been on a website, where you click around before you stumble upon the conversion page (“sign-up” or “purchase”), and then see some offer that reads, “Purchase now and instantly get a 20% discount!” or “Sign-up now to receive 3000 free credits”? Often, we dismiss these offers as gimmicky, and a poor appeal to Core Drive 4: Ownership & Possession, so we ignore them. However, some sites integrate game techniques into the experience by harnessing our loss aversion tendencies. Imagine as you click around a website, there is a little popup widget that says, “Great! Your actions have earned you 500 credits!” As you click on more places, it will continue to say, “Great! Your actions have earned you 1500 credits!” Finally, when you get onto the landing page, the text reads, “You now have 3000 credits.

The End of Jobs: Money, Meaning and Freedom Without the 9-To-5
by Taylor Pearson
Published 27 Jun 2015

Your status was lowered in the tribe for the rest of your life; you might never find a mate, reproduce, and pass on your genes. This principle is called loss aversion: when directly compared to each other, losses loom larger than gains. Consider: You are offered a gamble on the toss of a coin. Heads, you win $150. Tails, you lose $100. How do you feel about it? Although the expected value is obviously positive (if you repeated the bet 100 times, you’d almost certainly come out on top), most people decline the bet. When asked: “What is the smallest gain that you need to balance an equal chance to lose $100?” most people answer $200—twice as much as the loss.28 The ratio of loss aversion has been measured at between 1.5 and 2.5, meaning people typically want to see a 150–250% expected return to make the bet.

Bold: How to Go Big, Create Wealth and Impact the World
by Peter H. Diamandis and Steven Kotler
Published 3 Feb 2015

Thinking in first principles protects you from these errors.” When it comes to scale, these aren’t the only errors one must guard against. Daniel Kahneman and Amos Tversky won the Nobel Prize for their work on human irrationality. One great example of this is what happens when two of the most common cognitive biases—loss aversion and narrow framing—begin to overlap. Loss aversion is the idea that humans are more sensitive to losses—even small losses—than gains, while narrow framing is our tendency to treat every risk we encounter as an isolated incident. In combination, what this means is when we go to assess risk, we tend not to look at the entire picture.

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R., 27 LIDAR, 43–44, 44 life-extension projects, 66, 81 Li’l Abner (comic strip), 71 Lincoln, Abraham, 109, 194 Lindbergh, Charles, 112, 244, 245, 259–60 linear growth, 7, 9 linear industries, 38, 116 exponential technologies in disrupting of, 17, 18–22 linear organizations, 15, 17, 18, 19, 20, 21, 76, 85, 116 LinkedIn, 77, 213, 231 Lintott, Chris, 220 Linux, 11, 163 Littler Workplace Policy Institute, 60 live-streaming, in crowdsourcing campaigns, 207 Lloyd, Gareth, 4 Local Motors, 33, 217, 223–25, 231, 238, 240, 241 Locke, Edwin, 23, 74, 75, 103 Lockheed, 71–72, 75 Lockheed Martin, 249 Longitude Prize, 245, 247, 267 long-term thinking, 116, 128, 130–31, 132–33, 138 Los Angeles, Calif., 258 loss aversion, 121 Louis Pasteur Université, 104 Lovins, Amory, 222 MacCready, Paul, 263 McDowell, Mike, 291n machine learning, 54–55, 58, 66, 85, 137, 167, 216 see also artificial intelligence (AI) Macintosh computer, 72 McKinsey & Company, 245 McLucas, John, 102 Macondo Prospect, 250 macrotasks, crowdsourcing of, 156, 157–58 Made in Space, 36–37 Made to Stick: Why Some Ideas Survive and Others Die (Heath and Heath), 248 MakerBot printers, 39 Makers (Doctorow), 38 MakieLabs, 39 manufacturing, 33, 41 biological, 63–64 digital, 33 in DIY communities, 223–25 robotics in, 62 subtractive vs. additive, 29–30, 31 3–D printing’s impact on, 30, 31, 34–35 Marines, US, 222 Markoff, John, 56 Mars missions, 99, 118–19, 128 Mars Oasis project, 118 Maryland, University of, 74 Maryniak, Gregg, 244 Mashable, 238 massively transformative purpose (MTP), 215, 221, 230, 231, 233, 240, 242, 274 in incentive competitions, 249, 255, 263, 265, 270 mastery, 79, 80, 85, 87, 92 materials, in crowdfunding campaigns, 195 Maven Research, 145 Maxwell, John, 114n Mead, Margaret, 247 Mechanical Turk, 157 meet-ups, 237 Menlo Ventures, 174 message boards, 164 Mexican entrepreneurs, 257–58 Michigan, University of, 135, 136 microfactories, 224, 225 microlending, 172 microprocessors, 49, 49 Microsoft, 47, 50, 99 Microsoft Windows, 27 Microsoft Word, 11 microtasks, crowdsourcing of, 156–57, 166 Mightybell, 217, 233 Migicovsky, Eric, 175–78, 186, 191, 193, 198, 199, 200, 206, 209 Millington, Richard, 233 Mims, Christopher, 290n MIT, 27, 60, 100, 101, 103, 291n mobile devices, 14, 42, 42, 46, 46, 47, 49, 124, 125, 135, 146, 163, 176 see also smartphones Modernizing Medicine, 57 monetization: in incentive competitions, 263 of online communities, 241–42 Montessori education, 89 moonshot goals, 81–83, 93, 98, 103, 104, 110, 245, 248 Moore, Gordon, 7 Moore’s Law, 6–7, 9, 12, 31, 64 Mophie, 18 moral leadership, 274–76 Morgan Stanley, 122, 132 Mosaic, 27, 32, 33, 57 motivation, science of, 78–80, 85, 87, 92, 103 incentive competitions and, 148, 254, 255, 262–63 Murphy’s Law, 107–8 Museum of Flight (Seattle), 205 music industry, 11, 20, 124, 125, 127, 161 Musk, Elon, xiii, 73, 97, 111, 115, 117–23, 128, 134, 138, 139, 167, 223 thinking-at-scale strategies of, 119–23, 127 Mycoskie, Blake, 80 Mycroft, Frank, 180 MySQL, 163 Napoléon I, Emperor of France, 245 Napster, 11 Narrative Science, 56 narrow framing, 121 NASA, 96, 97, 100, 102, 110, 123, 221, 228, 244 Ames Research Center of, 58 Jet Propulsion Laboratory (JPL) of, 99 Mars missions of, 99, 118 National Collegiate Athletic Association (NCAA), 226 National Institutes of Health, 64, 227 National Press Club, 251 navigation, in online communities, 232 Navteq, 47 Navy Department, US, 72 NEAR Shoemaker mission, 97 Netflix, 254, 255 Netflix Prize, 254–56 Netscape, 117, 143 networks and sensors, x, 14, 21, 24, 41–48, 42, 45, 46, 66, 275 information garnered by, 42–43, 44, 47, 256 in robotics, 60, 61 newcomer rituals, 234 Newman, Tom, 268 New York Times, xii, 56, 108, 133, 145, 150, 155, 220 Nickell, Jake, 143, 144 99designs, 145, 158, 166, 195 Nivi, Babak, 174 Nokia, 47 Nordstrom, 72 Nye, Bill, 180, 200, 207 “Oatmeal, the” (web comic), 178, 179, 193, 196, 200 Oculus Rift, 182 O’Dell, Jolie, 238–39 oil-cleanup projects, 247, 250–53, 262, 263, 264 Olguin, Carlos, 65 1Qbit, 59 operational assets, crowdsourcing of, 158–60 Orteig Prize, 244, 245, 259, 260, 263 Oxford Martin School, 62 Page, Carl, 135 Page, Gloria, 135 Page, Larry, xiii, 53, 74, 81, 84, 99, 100, 115, 126, 128, 134–39, 146 thinking-at-scale strategies of, 136–38 PageRank algorithm, 135 parabolic flights, 110–12, 123 Paramount Pictures, 151 Parliament, British, 245 passion, importance of, 106–7, 113, 116, 119–20, 122, 125, 134, 174, 180, 183, 184, 248, 249 in online communities, 224, 225, 228, 231, 258 PayPal, 97, 117–18, 167, 201 PC Tools, 150 Pebble Watch campaign, 174, 175–78, 179, 182, 186, 187, 191, 200, 206, 208, 209, 210 pitch video in, 177, 198, 199 peer-to-peer (P2P) lending, 172 Pelton, Joseph, 102 personal computers (PCs), 26, 76 Peter’s Laws, 108–14 PHD Comics, 200 philanthropic prizes, 267 photography, 3–6, 10, 15 demonetization of, 12, 15 see also digital cameras; Kodak Corporation Pink, Daniel, 79 Pishevar, Shervin, 174 pitch videos, 177, 180, 192, 193, 195, 198–99, 203, 212 Pivot Power, 19 Pixar, 89, 111 Planetary Resources, Inc., 34, 95, 96, 99, 109, 172, 175, 179, 180, 186, 189–90, 193, 195, 201–3, 221, 228, 230 Planetary Society, 190, 200 Planetary Vanguards, 180, 201–3, 212, 230 PlanetLabs, 286n +Pool, 171 Polaroid, 5 Polymath Project, 145 Potter, Gavin, 255–56 premium memberships, 242 PricewaterhouseCoopers, 146 Prime Movers, The (Locke), 23 Princeton University, 128–29, 222 Prius, 221 probabilistic thinking, 116, 121–22, 129 process optimization, 48 Project Cyborg, 65 psychological tools, of entrepreneurs, 67, 115, 274 goal setting in, 74–75, 78, 79, 80, 82–83, 84, 85, 87, 89–90, 92, 93, 103–4, 112, 137, 185–87 importance of, 73 line of super-credibility and, 96, 98–99, 98, 100, 101–2, 107, 190, 203, 266, 272 passion as important in, 106–7, 113, 116, 119–20, 122, 125, 134, 174, 249, 258 Peter’s Laws in, 108–14 and power of constraints, 248–49 rapid iteration and, 76, 77, 78, 79–80, 83–84, 85, 86, 120, 126, 133–34, 236 risk management and, see risk management science of motivation and, 78–80, 85, 87, 92, 103, 254, 255 in skunk methodology, 71–87, 88; see also skunk methodology staging of bold ideas and, 103–4, 107 for thinking at scale, see scale, thinking at triggering flow and, 85–94, 109 public relations managers, in crowdfunding campaigns, 193–94 purpose, 79, 85, 87, 116, 119–20 in DIY communities, see massively transformative purpose (MTP) Qualcomm Tricorder XPRIZE, 253 Quirky, 18–20, 21, 66, 161 Rackspace, 50, 257 Rally Fighter, 224, 225 rapid iteration, 76, 77, 78, 79–80, 83–84, 85, 86, 236 feedback loops in, 77, 83, 84, 86, 87, 90–91, 92, 120 in thinking at scale, 116, 126, 133–34 rating systems, 226, 232, 236–37, 240 rationally optimistic thinking, 116, 136–37 Ravikant, Naval, 174 Raytheon, 72 re:Invent 2012, 76–77 reCAPTCHA, 154–55, 156, 157 registration, in online communities, 232 Reichental, Avi, 30–32, 35 Rensselaer Polytechnic Institute, 4 reputation economics, 217–19, 230, 232, 236–37 Ressi, Adeo, 118 ReverbNation, 161 reward-based crowdfunding, 173, 174–80, 183, 185, 186–87, 195, 205, 207 case studies in, 174–80 designing right incentives for affiliates in, 200 early donor engagement in, 203–5 fundraising targets in, 186–87, 191 setting of incentives in, 189–91, 189 telling meaningful story in, 196–98 trend surfing in, 208 upselling in, 207, 208–9 see also crowdfunding, crowdfunding campaigns rewards, extrinsic vs. intrinsic, 78–79 Rhodin, Michael, 56 Richards, Bob, 100, 101–2, 103, 104 Ridley, Matt, 137 risk management, 76–77, 82, 83, 84, 86, 103, 109, 116, 121 Branson’s strategies for, 126–27 flow and, 87, 88, 92, 93 incentive competitions and, 247, 248–49, 261, 270 in thinking at scale, 116, 121–22, 126–27, 137 Robinson, Mark, 144 Robot Garden, 62 robotics, x, 22, 24, 35, 41, 59–62, 63, 66, 81, 135, 139 entrepreneurial opportunities in, 60, 61, 62 user interfaces in, 60–61 Robot Launchpad, 62 RocketHub, 173, 175, 184 Rogers, John “Jay,” 33, 38, 222–25, 231, 238, 240 Roomba, 60, 66 Rose, Geordie, 58 Rose, Kevin, 120 Rosedale, Philip, 144 Russian Federal Space Agency, 102 Rutan, Burt, 76, 96, 112, 127, 269 San Antonio Mix Challenge, 257–58 Sandberg, Sheryl, 217, 237 Santo Domingo, Dominican Republic, 3 Sasson, Steven, 4–5, 5, 6, 9 satellite technology, 14, 36–37, 44, 100, 127, 275, 286n scale, thinking at, xiii, 20–21, 116, 119, 125–28, 148, 225, 228, 243, 257 Bezos’s strategies for, 128, 129, 130–33 Branson’s strategies for, 125–27 in building online communities, 232–33 customer-centric approach in, 116, 126, 128, 130, 131–32, 133 first principles in, 116, 120–21, 122, 126, 138 long-term thinking and, 116, 128, 130–31, 132–33, 138 Musk’s strategies for, 119–23, 127 Page’s strategies for, 136–38 passion and purpose in, 116, 119–20, 122, 125, 134 probabilistic thinking and, 116, 121–22, 129 rapid iteration in, 116, 126, 133–34 rationally optimistic thinking and, 116, 136–37 risk management in, 116, 121–22, 126–27, 137 Scaled Composites, 262 Schawinski, Kevin, 219–21 Schmidt, Eric, 99, 128, 251 Schmidt, Wendy, 251, 253 Schmidt Family Foundation, 251 science of motivation, 78–80, 85, 87, 92, 103 incentive competitions and, 148, 254, 255, 262–63 Screw It, Let’s Do It (Branson), 125 Scriptlance, 149 Sealed Air Corporation, 30–31 Second Life, 144 SecondMarket, 174 “secrets of skunk,” see skunk methodology Securities and Exchange Commission (SEC), US, 172 security-related sensors, 43 sensors, see networks and sensors Shapeways.com, 38 Shingles, Marcus, 159, 245, 274–75 Shirky, Clay, 215 ShotSpotter, 43 Simply Music, 258 Singh, Narinder, 228 Singularity University (SU), xi, xii, xiv, 15, 35, 37, 53, 61, 73, 81, 85, 136, 169, 278, 279 Six Ds of Exponentials, 7–15, 8, 17, 20, 25 deception phase in, 8, 9, 10, 24, 25–26, 29, 30, 31, 41, 59, 60 dematerialization in, 8, 10, 11–13, 14, 15, 20–21, 66 democratization in, 8, 10, 13–15, 21, 33, 51–52, 59, 64–65, 276 demonetization in, 8, 10–11, 14, 15, 52, 64–65, 138, 163, 167, 223 digitalization in, 8–9, 10 disruption phase in, 8, 9–10, 20, 24, 25, 29, 32, 33–35, 37, 38, 39, 256; see also disruption, exponential Skonk Works, 71, 72 skunk methodology, 71–87, 88 goal setting in, 74–75, 78, 79, 80, 82–83, 84, 85, 87, 103 Google’s use of, 81–84 isolation in, 72, 76, 78, 79, 81–82, 257 “Kelly’s rules” in, 74, 75–76, 77, 81, 84, 247 rapid iteration approach in, 76, 77, 78, 79–80, 83–84, 85, 86 risk management in, 76–77, 82, 83, 84, 86, 87, 88 science of motivation and, 78–80, 85, 87, 92 triggering flow with, 86, 87 Skunk Works, 72, 75 Skybox, 286n Skype, 11, 13, 167 Sloan Digital Sky Survey, 219–20 Small Business Association, US, 169 smartphones, x, 7, 12, 14, 15, 42, 135, 283n apps for, 13, 13, 15, 16, 28, 47, 176 information gathering with, 47 SmartThings, 48 smartwatches, 176–77, 178, 191, 208 software development, 77, 144, 158, 159, 161, 236 in exponential communities, 225–28 SolarCity, 111, 117, 119, 120, 122 Space Adventures Limited, 96, 291n space exploration, 81, 96, 97–100, 115, 118, 119, 122, 123, 134, 139, 230, 244 asteroid mining in, 95–96, 97–99, 107, 109, 179, 221, 276 classifying of galaxies and, 219–21, 228 commercial tourism projects in, 96–97, 109, 115, 119, 125, 127, 244, 246, 261, 268 crowdfunding campaigns for, see ARKYD Space Telescope campaign incentive competitions in, 76, 96, 109, 112, 115, 127, 134, 139, 246, 248–49, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269 International Space University and, 96, 100–104, 107–8 Mars missions in, 99, 118–19, 128 see also aerospace industry Space Fair, 291n “space selfie,” 180, 189–90, 196, 208 SpaceShipOne, 96, 97, 127, 269 SpaceShipTwo, 96–97 SpaceX, 34, 111, 117, 119, 122, 123 Speed Stick, 152, 154 Spiner, Brent, 180, 200, 207 Spirit of St.

Ghost Road: Beyond the Driverless Car
by Anthony M. Townsend
Published 15 Jun 2020

But give us that same choice and tick one option as the default—and we’re highly likely to go with the flow. One reason why we abhor change is fear, a related phenomenon behavioral economists call loss aversion. Sometime long ago in our evolutionary history, it seems that natural selection favored those individuals inclined to weigh the risk of future losses more heavily than the possibility of future gains. Loss aversion is what keeps us from pursuing our dreams, confessing a crush, or trying a new flavor. We simply find it hard to imagine the world getting any better—or more precisely, we can’t imagine it being any different—than it is now.

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“self-driving,” 38 early self-steering schemes, 5–6 modern-day myths about future, xv–xvi scan, study, and steer as basic tasks, 34–38 self-driving shoes, 52, 53 three big stories of the driverless revolution, 16–20, 187–88, 238, 248, 253 see also financialization of mobility; materialization; self-driving vehicle research; specialization driving as coming-of-age story, 21–22 cruise control and, 24–25, 26 decline in teen driving, 22–23 distracted driving, 25, 28–29, 32–33 drinking and, 24 graduated licensing, 23 drones, 40, 127, 246–47 EasyMile, 60, 103–4, 104 Ebee, 129 e-commerce, 17, 117, 118–19, 120 see also Amazon; continuous delivery Edgar, John, 247 electrification and automation as symbiotic technologies, 54–55 electronic tolling, 169–72 Endeavor space shuttle, 74 English Civil War, 161 e-Palette, 125, 142 EUREF, 129 Evans, Alex, 116 éX-Driver (anime series), 149–50 Facebook, 67 fear of intelligent automobiles, 39, 43, 45 FedEx, 27, 130 “fifth-generation” (5G) wireless grid, 42 financial crisis of 2007–2008, 7, 164, 182 financialization, general, 163–64 financialization of mobility curb pricing and curb-access fees, 220–21, 222–23 electronic tolling, 169–72 monetization of vehicle owner data, 32 overview, 17, 163–65, 244 realignment of money and power, 181–83 see also congestion pricing first mile, 60 fleet learning, 37 Florida Automated Vehicles Summit, 55 Ford, Henry, 12 Ford Motor Company, 12, 32, 58, 218–19, 231, 233 forecasting vs. predicting the future, 13 free roads, end of, 163, 165 free transfer in transit system, 89, 90, 91 Frey, Carl Benedikt, 153, 154, 236 Frost, Robert, 249 fulfillment centers and distribution centers, 121, 123, 132, 136–37, 152, 158, 196n fulfillment zone, 187, 188, 196–99, 198–99 Futurama (1939 World’s Fair), 5 future car of the 1950s, 50–52, 51 Future of Humanity Institute, 238 future shock, 120 Gao Lufeng, 65 Gates, Bill, 237–38 General Motors (GM) AVs tested in San Francisco, xv disengagements by Chevy Bolts, 41 Futurama (1939 World’s Fair), 5 in-car surveillance and driver monitoring, 32 Super Cruise, 29 Gensler, 191 ghost cars, 27 ghost Main Street businesses, 140–42 ghost restaurants, 139–40, 197 ghost road, defined, xvi Gibson, Mel, 28 Gibson, William, 10, 245 GitHub, 248 Glaeser, Edward, 130, 206 Goldsmith, Stephen, 222 Google ambitions, 183 Android operating system, 7 busing of workers, 100 self-driving car project, xiv–xv, 7, 8, 35, 84, 133, 230 and vehicular specialization, 54 Waze acquired by, 87 see also Waymo Gould, Jay, 180 GPS tracks, 35 Grab, 177 graduated licensing, 23 Green Summit, 139 guardian angels, 246–47 Hackett, Jim, 32 Hawking, Stephen, 237–38 Heppner, Henning, 129 Herron, Ron, 74 Hidalgo, Anne, 220 highwaymen (England’s East Midlands), 161 Hitachi, 67, 79 HopSkipDrive, 95 horsecars, 174–75 houses, increased size of, 116 human intelligence tasks (HITs), 41 IBM, 36 Icebox, 243–44 IDEO, 125 IKEA, 72–73 Image of the City, The (Lynch), 228–30 immutable objects, 49 Impellitteri, Vincent, 165 Induct, 103, 104 infill housing, 204, 253–55 informal transit, 99–100, 106 Inrix, 9 Intel, 8, 35 “Introducing the self-driving bicycle in the Netherlands,” 62 Intuit, 125 Jacobs, Jane, 57, 228 JD.com, 118, 119, 137 Jelbi MaaS app (Berlin), 109, 110, 216 Jevons paradox, 144–45 Jevons, William Stanley, 143–44, 145 just-in-time inventory approaches, 157 Ju, Wendy, 40 Kalanick, Travis, 140, 179 Kamen, Dean, 62 Keller, David H., 84–85, 94 Keolis, 104 Khashoggi, Jamal Ahmad, 178 Khosrowshahi, Dara, 98, 179 Kia, 31 Kim, Sangbae, 46 King, David, 132, 247 King, Steven, 42 kipple, defined, 142–43 Kitchen United, 139 Kiva Systems, 136, 137 Kiwibots, 57 Knightley’s (Wichita, KS), 192 Koch, Charles and David, 40 Kohlhase, Janet, 130, 206 Kohn Pedersen Fox, 209, 211 Koolhaas, Rem, 206 KPMG, 117, 218 Kurzweil, Ray, 234 Ladd, Brian, 80 last mile continuous delivery and, 121–29 conveyors and, 124–25 cost savings, 130 driverless shuttles, 60, 123n falling costs and demand, 130–32, 131 in food delivery, 140, 147 freight AVs, 125–26, 130 Hannah school buses, 127 nighttime delivery, 128–29, 130, 217 origin of term, 122 package lockers and, 127, 130, 219, 221 piggybacking deliveries, 126–27 term use in shipping, 123n legibility, 229–30, 231 Legible London, 230 Leonhardt, David, 8–9 Les Vergers Ecoquartier (Switzerland), 202 Levandowski, Anthony, 40, 68 Levy, Frank, 150, 151, 152 lidar, 34–35 Ligier Group, 103 Lime Bike, 67 “Living Machine, The” (Keller), 83–85, 94, 237 loss aversion, 50 Lowe’s, 116 Lufa Farms, 147 Luks, George, 174 Lyft competition with Uber, 177–78, 179 initial public offering, 97, 177 market cap, 97 number of vehicles, 10 relationship with transit, 215 specialization and variety of rides, 95, 96 subscriptions, 244 taxibots, 97 traffic congestion and, 168 Lynch, Kevin, 228–30 MaaS.

The Social Animal: The Hidden Sources of Love, Character, and Achievement
by David Brooks
Published 8 Mar 2011

In the nonaroused state, 20 percent said they would try to have sex with their date after she said no. In the aroused state, 45 percent said they would keep trying. Finally, there is loss aversion. Losing money brings more pain than winning money brings pleasure. Daniel Kahneman and Amos Tversky asked people if they would accept certain bets. They found that people needed the chance of winning $40 if they were going to undergo a bet that might cost them $20. Because of loss aversion investors are quicker to sell stocks that have made them money than they are to sell stocks that have been declining. They’re making self-destructive decisions because they don’t want to admit their losses.

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Ornstein, Multimind: A New Way of Looking at Human Behavior (New York: Houghton Mifflin, 1996), 86. 21 high Social Security numbers Dan Ariely, “The Fallacy of Supply and Demand,” Huffington Post, March 20, 2008, http://www.huffingtonpost.com/dan-ariely/the-fallacy-of-supply-and_b_92590.html. 22 People who are given Hallinan, 50. 23 “Their predictions became” Jonah Lehrer, How We Decide (New York: Houghton Mifflin Co., 2009), 146. 24 They just stick with Thaler and Sunstein, 34. 25 The picture of the smiling Hallinan, 101. 26 In the aroused state Ariely, 96 and 106. 27 Daniel Kahneman and Amos Tversky Jonah Lehrer, “Loss Aversion,” The Frontal Cortex, February 10, 2010, http://scienceblogs.com/cortex/2010/02/loss_aversion.php. CHAPTER 12: FREEDOM AND COMMITMENT 1 In Guess culture Oliver Burkerman, “This Column Will Change Your Life,” The Guardian, May 8, 2010, http://www.guardian.co.uk/lifeandstyle/2010/may/08/change-life-asker-guesser. 2 Thirty-eight percent of young Americans “Pew Report on Community Satisfaction,” Pew Research Center (January 29, 2009): 10, http://pewsocialtrends.org/assets/pdf/Community-Satisfaction.pdf. 3 In Western Europe William A.

The Fissured Workplace
by David Weil
Published 17 Feb 2014

If the pay of the group just above me is too high—or if the gap widens over time—I may be less and less happy with the pay I receive, regardless of its absolute level. Psychologists have long known that people care more about a small loss in income than about an equal gain in income.25 This effect—called loss aversion—means that perceptions of being adversely affected by a change in a current situation will make people feel worse than a comparable improvement in position makes them feel better. In a workplace context, loss aversion means that if a worker’s pay situation changes in a way that is judged unfair, it will have larger effects than if the situation changes in a way that is judged as improving fairness. Wage comparisons across different occupations or jobs can have this effect.

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In so doing, “a predictable salary progress schedule not only should help to reduce uncertainty about future pay but also should prevent the development of false expectations. In addition it should minimize dysfunctional competition between individuals for favored treatment.” Quoted in Foulkes (1980, 186). 25. Fehr, Goette, and Zehnder (2009, 378). The literature on loss aversion and “framing” in psychology is extensive. The seminal papers are Tversky and Kahneman (1974) and Kahneman and Tversky (1984). Kahneman (2011) provides an overview of the extensive research in the field in the decades following those landmark works. 26. Slichter, Healy, and Livernash (1960) explained the common practice of uniform pay increases with job grades with minimal performance evaluation in union and nonunion facilities as an outgrowth of union avoidance and the constant problems of defending merit-based evaluations in the minds of workers.

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Yensavage, 347n50 Lenny, Richard, 115 Lettire Construction, 233–234 Liability: as issue in fissuring, 78; tests to assess, 186, 190; vicarious liability defined, 189; and subcontracting, 190–192; and multiemployer settings, 192–195, 233–234; and franchising, 195–201; and supply chain relationships, 201–203 Lilly Ledbetter Fair Pay Act, 209 Litton Industries, 36 Locke, Richard, 174, 264, 365n39, 366n41 Logistics industry: and outsourcing, 57–58, 63; and fissuring, 96, 117–118, 160–167 Loss aversion, 85–86 LRSolutions, 275–276 Lyons and Sons, 115–117, 321n55 Maintenance services, and outsourcing, 55–56 Markets. See Capital markets; Financial markets; Internal labor markets; Labor market; Stock market Marriott, 2–3, 146, 150; and branding, 331n54 Massachusetts: and franchising, 198–199; and presumption of employee status, 204–205 Massey Doctrine, 102–103, 107, 369n17 Massey Upper Big Branch (mine disaster), 238 McDonald’s, 127, 259, 261, 323n4, 325n14; Miller v.

#Republic: Divided Democracy in the Age of Social Media
by Cass R. Sunstein
Published 7 Mar 2017

But there is narrowing as well, in the form of communities of niches. One of my own fields is behavioral science, and with the help of Twitter, those of us who are interested in that field can easily find each other. If you want to learn about the latest developments, Twitter is a great help. For example, behavioral scientists are interested in “loss aversion,” which means that people dislike losses more than they like corresponding gains. If you’re interested in new examples, exceptions, or elaborations of the behavioral finding, Twitter is terrific. And yes, there is a #lossaversion. That’s great, but for academics in any particular field, there’s a risk that Twitter will contain echo chambers just for them.

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Kelly, 114–15 Gates, Bill, 52–53, 134, 196 general-interest intermediaries: bias and, 148; citizens and, 166; Daily Me and, 20; as default, 25; improving, 230; judgment and, 43; mass media and, 18–19; newspapers and, 13 (see also newspapers); polarization and, 84; power of, 18; republicanism and, 253, 257, 260–61; self-insulation and, 13; shared experiences and, 152; social clarity and, 142–43; spreading information and, 140–43, 148, 151–52, 156; television and, 13 (see also television); as unacknowledged public forums, 41–44, 58 genetically modified organisms (GMOs), 59, 100, 130–32, 217 Gentzkow, Matthew, 115–16, 120–21 Gerken, Heather, 85 Germany, ix, 6, 17, 19, 25, 32, 34, 73, 203, 237 global warming, 68–69, 88, 217 Goodman, Jack, 197–98 Google, 3, 28, 37, 53, 118, 229, 265n2 greenhouse gases, 9, 127, 130–32, 218 group identity, 75–78, 81 Guess, Andrew, 116–17 Habermas, Jürgen, 46–47 Haberstam, Yosh, 120 hacking, 109, 178, 184, 186, 188, 201 Hale, Scott, 105–6, 108 Hamilton, Alexander, 49, 54 Hand, Learned, 249–50 Hardball (TV show), 120 Hardin, Russell, 240 Harvard University, 160 hashtags, 3, 43, 119, 245, 271n23; Congress and, 82; Democrats and, 80–81; entrepreneurs and, 4, 79–82; Internet Relay Chats and, 79; polarization and, 59, 79–82; Republicans and, 80–81; serendipity and, 79–81; Trump and, 83 hate groups, 67–68, 70, 87, 236 HBO, 179 health issues: Affordable Care Act (ACA) and, 81, 129; AIDS/HIV and, 110; bandwagon diseases and, 100; conspiracies and, 125–26; cybercascades and, 99–101; deliberative opinion polls on, 133; democracy and, 23, 29; false information and, 110; famine as metaphor and, 149; GMOs and, 59, 100, 130–32; insurance premiums and, 129; OSHA and, 218–19 Hebrew University, 112 Her (Jonze film), 20–22, 33 heterogeneous society: anti-federalists and, 48; fragmentation and, 51, 135; improving, 216; mass media and, 19; opinion polls and, 134; polarization and, 84, 86, 88–89; public forums and, 38–39, 41, 43; republicanism and, 257, 262; shared experiences and, 140; social problems and, 7; spreading information and, 140, 145, 155; US Constitution and, 48–51 Himelboim, Itai, 118–19 Hitler, Adolf, ix HIV, 110 holidays, 7, 141–42, 242 Holmes, Oliver Wendell, 52–57, 247–48, 250 homogeneous society: Facebook and, 125; polarization and, 69, 86, 92; social media bias and, 135; spreading information and, 151; Twitter and, 119; US Constitution and, 48–51; virtual world and, 13 homophily, 1–2, 5, 117–22 How to Win Friends and Influence People (Carnegie), 160 Huffington Post, 117, 123 Hughes, Chris, 82 human rights, 107 Hurricane Katrina, 19, 139 Hussein, Saddam, 94 Huxley, Aldous, x, 21 ideal speech situation, 47 identity: culture and, 129–35; group, 75–77, 81; judgment and, 129–35; shared, 239 ideologies: cultural cognition and, 129–30; cybercascades and, 115–23, 127, 131; polarization and, 61–62, 65, 81, 94–95; republicanism and, 260; spreading information and, 140; values and, 11, 14–15, 22, 30, 52, 75, 101, 113, 126–32, 142, 145, 163, 165, 169, 227, 232, 235, 253, 258; website choice and, 5, 25 ILOVEYOU virus, 176–78, 186, 191, 207 immigration, 1, 3–4, 11, 19, 39, 66, 129, 159, 235, 246 inert people, ix, 56, 145, 204, 261 information: advertising and, 146, 152–53; algorithms and, 3, 15, 21–22, 28–29, 32, 122–24, 257, 265n2; backfiring corrections and, 93–97, 111; bias and, 151–53 (see also bias); conspiracies and, 124–26; consumer sovereignty and, 52–53 (see also consumer sovereignty); copyright and, 29, 184–85, 195, 200–201, 219; cultural cognition and, 129–30; customized filtering and, 52–53 (see also filtering); cybercascades and, 98–136; Daily Me and, 1–4, 14–15, 19–21, 30–31, 52, 56, 58–59, 114, 153, 253, 255; disclosure policies and, 215, 218–23; easy creation of, 27–28; Emergency Planning and Community Right-to-Know Act and, 218; exposure and, 40–41; false, 11, 23, 109–10, 135, 155, 237, 250; fragmentation and, 140–41, 146, 149–55 (see also fragmentation); general-interest intermediaries and, 140–43, 148, 151–52, 156; hashtags and, 3–4, 43, 59, 79–83, 119, 245, 271n23; Internet and, 31, 138, 143–44, 148–54 (see also Internet); must-carry rules and, 215, 226–29; News Feed and, 2, 14–16, 41, 124, 232–33; preferences and, 58; producers and, 145–46; as product, 149; propaganda and, 109, 160, 236–37, 239, 245, 248–50; public forums and, 142, 156 (see also public forums); as public good, 45, 51, 57–58, 147–48, 260; reinforcement and, 63, 72, 78, 81, 114–15, 132, 148, 260–61; rumors and, 103, 108–11, 125, 236–37; self-imposed echo chambers and, 5–13, 17, 20, 50, 57, 59, 68, 71, 81, 90, 93, 114–18, 122–24, 131–32, 153, 163, 244, 262–64; social glue and, 7, 140, 143, 155, 260; social media and, 138–39, 148, 150, 152, 154–55; solidarity goods and, 58, 141–44; sound bites and, 43, 151, 224, 268n19; tipping points and, 102–4, 108–11; trending petitions and, 106; up/down votes and, 112–13; as wildfire, 102–4 innovation, 5, 133, 183, 243 “Inspire” (online terrorist journal), 236 Instagram, 22; cybercascades and, 114; polarization and, 79, 83, 89; public forums and, 36–37; regulation and, 179; spreading information and, 138, 149; terrorism and, 237–38 Internet: access to, 30; advertising and, 28; algorithms and, 3, 15, 21–22, 28–29, 32, 122–24, 257, 265n2; architecture of, 13; baselines and, 23; beginnings of, 181–86; Berners-Lee and, 183; bomb-making instructions and, 192, 235–37; browsing habits and, 5, 21–22, 116, 124; citizens and, 158, 160, 164, 169, 171–74; commercialization of, 183; consumer effect of, 171–74; conveniences of, 31–32; copyright and, 29; cybercascades and, 102, 108–11, 115–16, 123, 133–35; DARPA and, 182–83; death of mass media and, 19; deliberative domains and, 215–17; filtering and, 25–26 (see also filtering); forms of neutrality and, 207–10; free content and, 28; freedom of speech and, 192, 201–10; hashtags and, 3–4, 43, 59, 79–83, 119, 245, 271n23; ideologies and, 5, 25; ILOVEYOU virus and, 176–78, 186, 191, 207; improving, 215–16, 223, 226, 228–29; information available on, 31; isolation index and, 116, 120; legal issues and, 184–88; most popular sites on, 171–72; music and, 3, 21, 31–34, 64, 102, 104–8, 159, 192; online behavior and, 22–23, 65, 83, 98, 116–17, 130, 234–35; overload and, 63–68; Pariser and, 265n2; partyism and, 10; polarization and, 59–60, 64–68, 70, 72, 76–79, 86, 89; politics and, 116–17; potential of, 24; as public forum, 36; public sphere and, 153; regulation and, 178, 182–90; republicanism and, 253–61; self-insulation and, 13; shared experience and, 143; social media and, 22 (see also social media); sovereignty and, 52, 55; spreading information and, 138, 143–44, 148–54; tabloidization and, 223–24; terrorism and, 234–38, 240–43, 245–47; as threat, ix–x; websites and, 3, 6, 13, 22, 27–28, 32–33, 59–60, 62, 65, 67, 106–12, 146, 154, 166, 179, 185–94, 207–8, 212–17, 222–25, 229, 235, 242, 255, 268n18 Internet Relay Chats, 79 Iraq, 18, 42, 64, 93–94, 98, 242 ISIS, 238, 244 Islamic State of Iraq and Levant (ISIL), 98, 234, 236, 239, 241–48, 283n22 isolation: filtering and, 27, 38, 64, 98, 115–16, 120–22, 234, 242–43, 254, 265n2; index for, 115–16, 120–21; self-imposed echo chambers and, 5–13, 17, 20, 50, 57, 59, 68, 71, 81, 90, 93, 114–18, 122–24, 131–32, 153, 163, 244, 262–64 Israel, 6, 87, 91, 140–41, 245–46, 284n31 Italy, 6, 124, 203 Jacobs, Jane, 12–13, 260 jarring of parties, 49, 54 Jefferson, Thomas, 51–52 Jews, 96, 185 Jiabao, Wen, 139 jihad, 239, 241–42 John, Peter, 105–6, 108 Jonze, Spike, 20–22, 33 judgment: citizens and, 167, 169–70; cybercascades and, 99, 101–2, 127–35; freedom of speech and, 206; general-interest intermediaries and, 43; identity and, 129–35; insulation and, 51; prediction and, 28; republicanism and, 259, 261; sound bites and, 268n19; strengthening preexisting, 34; terrorism and, 235 Kahan, Dan, 129–31 Kahneman, Daniel, 17–18 Kennedy, Anthony, 36–37 King, Gary, 160–61 Knight, Brian, 120 Koran, 239 Kossinets, Gueorgi, 118 Ku Klux Klan, 109 Lazzaro, Stephanie, 127 legal issues: behavior and, 220–21; Brandeis and, 52–56, 145, 203, 220, 228, 247–48, 250–51; child-support and, 133; commercial speech and, 193, 205, 207; communications and, 220, 227; constitutional doctrine and, 192–201, 204 (see also constitutional doctrine); copyright and, 29, 184–85, 195, 200–201, 219; deliberative democracy and, 25, 34, 44, 48, 55–56, 86, 92, 133–35, 169, 195, 215–17, 220, 222, 228; Dewey and, 252; disclosure policies and, 215, 218–23; educational programming for children and, 170, 181, 197–99, 202, 204–5, 210–11, 221, 226; Facebook’s complicity in terrorism and, 246; First Amendment and, 36, 193, 195–207, 212, 227–28, 231; forms of neutrality and, 207–10; Fourteenth Amendment and, 199; fraud and, 2–6, 74, 109, 200–201, 258; freedom of speech and, 55–56, 191–212; Hand and, 249–50; Holmes and, 52–56, 247–48, 250; interference in communications market and, 177–79; Internet and, 184–88; must-carry rules and, 215, 226–29; national security and, 4, 42, 74, 178, 186, 216, 246; Nuremberg Files and, 191–92, 208; President’s Advisory Committee on the Public Interest Obligations of Digital Television Broadcasters and, 196–98; privacy and, 178, 225, 237, 243; property rights and, 179–90, 194, 258; Second Amendment and, 119, 198, 234; self-protection against illegal immigrants and, 235; sexual harassment and, 101; terrorism and, 246–47; unfreedom and, 163; US Constitution and, 247 (see also US Constitution) Lessig, Lawrence, 184 liberals: blogs and, 231; Colorado experiment and, 68–70, 77; confirmation bias and, 123; cybercascades and, 114–23; differing points of view and, 230; Facebook and, 3, 232; foxnews.com and, 228; fragmentation and, 10; polarization and, 61, 64, 68–70, 74, 84–85, 90, 94–95 liberty, 5, 11, 52, 138, 174, 204 limited argument pool, 72, 76 limited options, 164–67, 174 Littleton, Colorado attack, 236 lone-wolf attacks, 241, 244–45 long tails, 149–51, 171 Lorenz, Jan, 113–14 Los Angeles Times, 19, 152 loss aversion, 59 machine learning, 4–5 Madison, James, 45, 51–52, 203, 261 magazines: bias and, 152; choice of, 18; isolation and, 116; free content and, 229; general-interest intermediaries and, 41–42, 257; points of view and, 18, 66, 230; public forums and, 41–42; regulation and, 179, 181–82, 184, 187, 189; Twitter and, 118 majority rule, 53–54, 169–70 Malik, Tashfeen, 241 manipulation, 17, 28–29, 95, 164 Mao Tse-tung, ix Margetts, Helen, 105–6, 108 Marginal Revolution, 22 Martin, Gregory J., 61 martyrdom, 241 mass media: behavior and, 19; bias and, 151–52; death of, 19; echo chambers and, 116; freedom of speech and, 203; as general-interest intermediaries, 18–19; heterogeneous society and, 19; improving, 222; opposing viewpoints and, 71, 84, 207, 215, 231–33, 255; public forums and, 36–37; public sphere and, 153–54.

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See also specific format Mateen, Omar, 236 Matthews, Chris, 120 Mbeki, Thabo, 110 McCain, John, 82 Messina, Chris, 79 Microsoft, 118 migration, 1, 3–4, 11, 19, 39, 66, 114–17, 129, 132, 159, 235–36, 246 Mill, John Stuart, x–xi, 252–53 monopolies, 28–29, 140, 179, 195 Montesquieu, Baron de, 48–49 Montreal Protocol, 132 Moonves, Leslie, 197 Morgan, Jonathan, 243–44 mosques, 237, 242 movies, 3, 7, 27, 159, 256; cybercascades and, 104, 118; filters and, 32–34; Jonze and, 20–22, 33; long tail and, 171; machine learning and, 32; Netflix and, 32–33, 150, 229; preference formation and, 162; spreading information and, 140, 142–43, 146, 150; Twitter and, 74; unauthorized copying of, 192 Moynihan, Daniel P., 126–27 MSNBC, 61–62 Muchnik, Lev, 112 murder, 9, 109, 113–14, 192, 249 music, 21; artificial lab for, 102–4; consumption and, 3, 31–34, 64, 159, 192; cybercascades and, 102–8; preferences and, 3, 32–33; Rodriguez and, 103–5 Muslims, 79, 109, 235, 237, 239, 242, 248 must-carry rules, 215, 226–29 nanotechnology, 95–96, 129 National Association of Broadcasters, 197–98, 221 National Public Radio (NPR), 64–65 National Review magazine, 231 National Rifle Association, 198, 236 National Science Foundation, 183 national security, 4, 42, 74, 178, 186, 216, 246 Nation magazine, 230 Nazism, 87 NBC, 61–62, 152, 179–80, 198 Negroponte, Nicholas, 1–2 Netflix, 32–33, 150, 229 net neutrality, 29 Netscape, 171 New England Journal of Medicine, 100 newspapers: bias and, 151–52; curation and, 1; customized, 53; decline of, 20; education and, 20; freedom of speech and, 196; free society and, 18–19; ideological segregation and, 121; improving, 222–23, 227, 229, 233; isolation and, 116; particular histories of, 20; polarization and, 61, 66, 71, 84; public forums and, 13, 41–42; regulation and, 179, 181–82, 187, 189; republicanism and, 257; tabloidization and, 223–24; technology and, 152–53 Newsweek magazine, 18–19, 42 New York Review of Books, 19, 153 New York Times, 18, 22, 94–95, 120, 144, 152–53, 232 niches: cybercascades and, 115, 118; enclaves and, 85–89, 238, 253–56; fragmentation and, 23; markets and, 20, 27, 149–51, 253, 256; polarization and, 59–60; rise of, 8; spreading information and, 149–51 Nineteen Eighty-Four (Orwell), ix, 12, 21 Nobel Prize, 17, 187 Noell-Neumann, Elisabeth, 73 nostalgia, 8, 57–58, 259–61 Nuremberg Files, 191–92, 208 Obama, Barack, 2, 11, 79, 81–83, 87, 100, 104, 109, 113, 168, 246, 263 Occupational Safety and Health Administration (OSHA), 218–19 Oklahoma City bombing, 236 online behavior, 22–23, 65, 83, 98, 116–17, 130, 234–35 Open Government Partnership, 219 open-source software, 79, 184 opinion polls, 126, 133–35, 216, 268n19 opposing viewpoints, 71, 84, 207, 215, 231–33, 255 optimism, 8, 97 organic food, 131 Orkut, 22 Orlando nightclub shootings, 236 Orwell, George, ix, 12, 21 Ostrom, Elinor, 187–88 overload, 63–68 Palestine, 239, 245–46, 284n31 Pandora, 22, 32–33 Pariser, Eli, 265n2 partyism, 9–12, 25 paternalism, 167 PBS, 179, 225–26 penalties, 34–35, 92, 210–12, 246 Perez, Heather, 244 Periscope, 83 pessimism, 8, 16, 259–61 Pew Research Center, 126 Pokémon Go game, 22 polarization: abortion and, 66, 81, 90, 191–92, 208–9; advertising and, 63; appropriately slanted stories and, 62–63; backfiring corrections and, 93–97, 111; balkanization and, 66, 70, 73, 89, 111, 259; behavior and, 59, 61, 65–66, 83; bias and, 63, 92, 97; blogs and, 63, 79; Colorado experiment and, 68–70, 77; communications and, 25, 60, 63–64, 70, 75, 84–86, 89–92; confidence and, 74–75; conservatives and, 61, 64, 68–70, 72–75, 80, 84–85, 90, 94–95; conspiracies and, 124–26; consumer sovereignty and, 89; corroboration and, 74–75; crime and, 64, 92; cybercascades and, 82; cyberpolarization and, 68; Daily Me and, 59; deliberative democracy and, 86, 92; Democrats and, 61, 65, 70, 72, 76, 80–81, 90, 95; depolarization and, 89–92; diversity and, 69, 85–86; echo chambers and, 59, 68, 71, 81, 90, 93; emotion and, 82, 96–97; enclaves and, 85–89; entreprenuers of, 238; extremism and, 7, 67, 69, 72, 74, 76, 78, 86, 88; Facebook and, 64, 71–72, 82–83, 86, 89; fairness doctrine and, 84–85, 207, 221, 227; filtering and, 60–62, 64, 66, 71, 79, 82; fragmentation and, 5, 7, 64, 77, 83–86, 89, 221; general-interest intermediaries and, 84; group, 68, 70–79, 82–91, 93, 97, 118, 133, 146, 155, 234, 240, 253–54, 259, 277n67; groupism and, 63–68; hashtags and, 59, 79–82; heterogeneous society and, 84, 86, 88–89; homogeneous society and, 69, 86, 92; ideologies and, 61–62, 65, 81, 94–95; importance of group identity and, 75–78, 81; improving situation of, 213–14, 221; Instagram and, 79, 83, 89; Internet and, 59–60, 64–68, 70, 72, 76, 78–79, 86, 89; Jon Stewart strategy and, 77; liberals and, 61, 64, 68–70, 74, 84–85, 90, 94–95; limited argument pool and, 72, 76; loss aversion and, 59; newspapers and, 61, 66, 71, 84; niches and, 59–60; online, 76–79; overload and, 63–68; persuasive arguments and, 71–72; preferences and, 65, 68; producers and, 146; public forums and, 84, 88; racism and, 80–81; radicalization and, 45, 74–75, 235, 237, 241–42, 244–46; radio and, 64, 66, 71–73, 75, 83–85; Republicans and, 61–65, 70, 72, 76, 80–81, 83, 90, 95; reputational considerations and, 72–74; social media and, 59, 62–63, 66, 68, 70–71, 73, 75, 78–89, 93, 95; strong convictions and, 63, 66–67, 72, 95–97; television and, 62, 64, 66, 71–73, 75, 77, 83–84; terrorism and, 234, 238, 240; Twitter and, 59, 64, 71–72, 74, 79–83, 87, 89, 95, 278n16; unbalanced views and, 92–93; unfamiliar issues and, 95–96 Political Turbulence (Margetts, John, Hale, and Yasseri), 105–6 politics: activists and, 80, 82, 178, 234, 235, 242; authoritarianism and, x, 11, 38, 73, 98, 108, 160, 165, 254; campaign finance and, 193–94; Citizens United case and, 193–94; Clintons and, 15, 59, 104, 109, 117; Colorado experiment and, 68–70, 77; communications and, 54; communism and, ix, 4, 80, 164, 189, 248; confirmation bias and, 123; conservatives and, 61 (see also conservatives); cybercascades and, 104–8; democracy and, 4 (see also democracy); Democrats and, 10 (see also Democrats); Dennis v.

Phishing for Phools: The Economics of Manipulation and Deception
by George A. Akerlof , Robert J. Shiller and Stanley B Resor Professor Of Economics Robert J Shiller
Published 21 Sep 2015

Or, in another example, insurance companies played on desires for immortality through advertising that, curiously, portrayed the deceased father in after-death family pictures.21 Social psychologist/marketer Robert Cialdini has written a book full of impressive evidence of psychological biases.22 According to his “list,” we are phishable because we want to reciprocate gifts and favors; because we want to be nice to people we like; because we do not want to disobey authority; because we tend to follow others in deciding how to behave; because we want our decisions to be internally consistent; and because we are averse to taking losses.23 Following Cialdini, each of these respective biases is paired with common salesman’s tricks. One such example concerns how his brother, Richard, paid his way through college. Every week, Richard would purchase two or three cars from the advertisements in the local newspapers. He would clean them up and offer them for sale again. Here, Richard put “loss aversion” to work. Richard did not, as most of us would do, schedule his prospective buyers to come at different times. Instead, intentionally, he scheduled them with overlap. Each buyer, whatever the merits of the prospective car, was then apprehensive that he might lose out: that other guy might get his car.24 Information Phools A great deal of phishing comes from another source: from supplying us with misleading, or erroneous, information.

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Robert B. Cialdini, Influence: The Psychology of Persuasion (New York: HarperCollins, 2007). 23. These correspond to Cialdini’s categories of “reciprocation,” “liking,” “authority: directed deference,” “social proof,” “commitment and consistency,” and “scarcity.” We have referred to “scarcity” as “loss aversion” since Cialdini emphasizes (ibid., p. 204) that “the way to love anything is to realize it might be lost [sic].” Behavioral economists would, we think, have a slightly different classification. 24. Ibid., pp. 229–30. 25. London School of Economics economist Eric Eyster told George that he witnessed this magic trick used in a con game on the Chicago subway.

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It is useful to look at Cialdini’s list of behaviors, since they encompass most of the psychological biases that have formed the basis for behavioral economics. According to Cialdini, the purchasers of his brother Richard’s cars are embedding themselves in a story in which they are thinking of the possibility that they will “lose” the car (they are what Kahneman has called loss averse); what we here call stories, he calls “mental frames.” For the other five items on Cialdini’s list we can again view people as making their decisions from the point of view of a “story.” People want to reciprocate gifts and favors: to do so they must be taking part in a story in which someone gives a gift, and it would be wrong not to reciprocate.

Capital Ideas Evolving
by Peter L. Bernstein
Published 3 May 2007

Then in his early twenties, Samuelson had already made his reputation by having published more papers than he was years old. bern_c03.qxd 40 3/23/07 9:01 AM Page 40 THE THEORETICIANS then he adds, “There is a separate love for the sport of gambling. But that is independent of the way they respond to the more serious business of managing their wealth. There loss aversion prevails.”  From Samuelson’s point of view, the existence of a positive alpha somewhere is not an exception to the Efficient Market Hypothesis but a kind of vindication of the logic of it. There are rare occasions when an investor succeeds in earning positive alpha—beating the market after adjustment for risk by gaining access to information earlier than other investors or by discovering mispriced assets other investors ignored.

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“Y * Unless otherwise specif ied, all quotations are from personal interviews or correspondence. 100 bern_c08.qxd 3/23/07 9:05 AM Page 101 Harry Markowitz 101 Today’s Harry Markowitz has no preconceived notions about how “recognizable economic agents” actually make decisions and act, even though he has strong convictions on how they should act. After all, as he points out, you can look at stock prices swinging up and down every day, but what you observe reveals nothing about what is going on under the surface, such as the degree to which investors are succumbing to the overconfidence and loss aversion featured in Behavioral Finance. Markowitz has wanted to explore in detail how stock prices would behave in a market where some investors have behavioral quirks while others are coolly rational. He is also interested in studying the consequences for stock prices when some investors take on risks that differ from the risks other investors are taking.

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The Winner ’s Curse: Paradoxes and Homilies of Economic Life, Princeton, NJ: Princeton University Press. Thaler, Richard, and Eric Johnson, 1990. “Gambling with the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice,” Management Science, Vol. 36, No. 6 ( June), pp. 643– 660. Thaler, Richard, Daniel Kahneman, and J. L. Knetsch, 1992. “The Endowment Effect, Loss Aversion and Status Quo Bias,” in Richard Thaler, The Winner ’s Curse, Princeton, NJ: Princeton University Press. Temin, Peter, and Hans-Joachim Voth, 2003. “Riding the South Sea Bubble,” MIT Economics Department Working Paper No. 04-02 ( December). Treynor, Jack, 1961. “Toward a Theory of Market Value of Risky Assets.”

Essentialism: The Disciplined Pursuit of Less
by Greg McKeown
Published 14 Apr 2014

If you’ve already made a casual commitment you’re regretting, find a nice way to worm your way out. Simply apologize and tell the person that when you made the commitment you didn’t fully realize what it would entail. GET OVER THE FEAR OF MISSING OUT We’ve seen ample evidence in this chapter suggesting that the majority of us are naturally very loss-averse. As a result, one of the obstacles to uncommitting ourselves from a present course is the fear of missing out on something great. TO FIGHT THIS FEAR, RUN A REVERSE PILOT One of the ideas that has grown popular in business circles in recent years is “prototyping.” Building a prototype, or large-scale model, allows companies to test-run an idea or product without making a huge investment up front.

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Gillman, “Supersonic Bust.” 4. Michael Rosenfield, “NH Man Loses Life Savings on Carnival Game,” CBS Boston, April 29, 2013, http://boston.cbslocal.com/2013/04/29/nh-man-loses-life-savings-on-carnival-game/. 5. Daniel Kahneman, Jack L. Knetsch, and Richard H. Thaler, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias,” Journal of Economic Perspective 5, no. 1 (1991): 193–206, http://users.tricity.wsu.edu/~achaudh/kahnemanetal.pdf. 6. Tom Stafford, “Why We Love to Hoard … and How You Can Overcome It,” BBC News, July 17, 2012, www.bbc.com/future/story/20120717-why-we-love-to-hoard. 7. I originally wrote this in a blog post for Harvard Business Review called “The Disciplined Pursuit of Less,” August 8, 2012, http://blogs.hbr.org/2012/08/the-disciplined-pursuit-of-less/. 8.

Traffic: Why We Drive the Way We Do (And What It Says About Us)
by Tom Vanderbilt
Published 28 Jul 2008

An Exploratory Examination of Eye Movement in High Speed Driving.” Paper 04-2602, Proceedings of the 83rd Annual Meeting of the Transportation Research Board (Washington D.C., January 2004). “loss aversion”: The notion of loss aversion was first hypothesized by Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica, vol. 47 (1979), pp. 263–91. sensitive to loss: See Sabrina M. Tom, Craig R. Fox, Christopher Trepel, and Russell A. Poldrack, “The Neural Basis of Loss Aversion in Decision-Making Under Risk,” Science, vol. 315, no. 5811 (26 January 2007), pp. 515–18. See also William J. Gehring and Adrian R. Willoughby, “The Medial Frontal Cortex and the Rapid Processing of Monetary Gains and Losses,” Science, vol. 295, no. 5563 (2002), pp. 2279–82.

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We spend only about 6 percent of our driving time looking in the rearview mirror. In other words, we’re much more aware of what is passing us than what we have passed. The fact that we spend more time seeing losses than gains while driving in congestion plays perfectly into a well-known psychological theory called “loss aversion.” Any number of experiments have shown that humans register losses more powerfully than gains. Our brains even seem rigged to be more sensitive to loss. In what psychologist Daniel Kahneman has called the “endowment affect,” once people have been given something, they are instantly more hesitant to give it up.

Alpha Trader
by Brent Donnelly
Published 11 May 2021

Flush your mind and start fresh tomorrow.” That said, when I lose money nine days in a row (which still happens!) I can’t help but get a little itchy and red-faced. In the middle of the ninth consecutive day I still might fantasize about turning my keyboard sideways and smashing it over the front edge of my desk. “Loss Aversion” by Laurenrosenberger, Creative Commons 4.0 (CC BY-SA 4.0) But I don’t do that, because I know it wouldn’t help. If you have ever played poker in a casino, you understand the concept of mental endurance. Sometimes (just like in trading) you have to sit there for hours and fold every hand because there is nothing to play.

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So even though two cents is meaningless to your P&L, the difference between selling below or above your cost is the difference between winning and losing. Human beings are hard-wired to avoid losses. As discussed earlier, research shows the pain we feel from losses is about double the pleasure we get from gains of equal size. This bias is called loss aversion. A related bias is the disposition effect. This is the strong and consistent tendency for investors to prefer to sell assets that have increased in value while holding onto assets that show a loss. If it goes up, you sell it. If it goes down, you hold on to it and pray for a bounce. These related forms of bias all come down to the same thing: humans don’t like to lose88.

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See reassessment triggers activity bias, 241 adaptability, trader attribute of, 35, 75, 104, 106-107 See also adaptation, trader adaptation, trader, 439—451 to fast/crisis markets, 440—445 growth mindset and, 451 to major structural market changes, 450—451 to new trading methods, 449 to rangebound markets, 447—448 to trending markets, 448, 449 to volatile markets, 445—447 algorithms, 163, 164, 173 All-Weather portfolio, 87 alpha, definitions of, 21 alpha trader feeling uncomfortable and, 112 formula for success, 72, 75 grit and, 92 having plan, 126 as healthy skeptic, 104 intellectual humility, 184 numeracy, 248 as rational thinker, 34, 76 as risk-seeker, 32 See also trader attributes, positive cognitive; trader attributes, positive miscellaneous; trader attributes, positive non-cognitive “AM/FX” newsletter, 84, 96, 202, 220 Amazon (AMZN) stock, 231, 347, 412 analogs, idea generation and, 402—405 analytical skill, 69 anchoring bias, 67, 225—229, 480, 484 See also disposition effect; loss aversion Anderson, Irina, 69 apophenia, 64, 216—225 See also pattern recognition; Turnaround Tuesday Apple (AAPL) stock, 219, 241, 276, 277, 278, 289, 290, 291, 291, 442 Arcsine Law, 251—254 Art of Currency Trading, The (B. Donnelly), 172 Asch, Solomon, 82 Asch Conformity Experiment, 82, 82, 83 Asian Financial Crisis (1998), 441 “Aspen Trading” (newsletter), 492 asymmetrical information, 156, 160 Axelrod, Bobby, 102 backtesting, 81, 221 curiosity and, 89—90 Bank of America, 42 Global Fund Manager Survey, 345 Bank of Canada, 308 bank traders annual performance and pay, 354 free capital, 356 information asymmetry and, 156 simple indicators and, 166 success rate, 40 bankrupt company, common stock of idea generation and, 406 rallies, 406 typical path followed by, 238 Batnick, Michael, 67—68 Baumeister, Roy, 56—57, 95, 106, 491 Bayes Theorem, 80, 81 Bayesian thinking, 201 Bayesian updating, 80, 81, 84 Beane, Billy, 451, 460—461 beauty contest analogy (Keynes), 311, 325 behavioral bias, 66, 179—180, 198 See also specific types of bias', biased thinking Bernanke, Ben, 208 Bernstein, Jake, 234, 345 bet size, varying, 107, 379 bet sizing, 256 better-than-average effect, 182—183 bias idea generation and, 396 post-trade, 416 See also specific types of bias', apophenia; biased thinking; gambler’s fallacy; herding; hot hand biased thinking, 155 See also specific types of bias; behavioral bias; bias bid/offer spread, 276 finding data on, 278 Big Five Personality Traits, 46—49 agreeableness, 47, 49, 50 conscientiousness, 47—48, 49, 50, 51, 52, 56-58, 71 effect of IQ and on male earnings, 49, 50 extraversion, 47, 49, 50 financial success and, 49—50, 52 lifetime stability of, 48 neuroticism, 47, 49, 50, 51, 52, 58, 71 openness to experience, 47, 49 versus Myers-Briggs (MBTI), 47 See also grit; personality; self-control birthday paradox, 254—255, 255 bitcoin bubble (2017-2018), 185, 239, 370, 405 bleed, slow, 167—168 blogs, 77 “Cheap Convexity,” 492 “Exante,” 492 “Liberty Street Economics,” 280 “No Mercy, No Malice,” 492 Bloomberg, 65, 97, 162, 194, 218, 259, 290, 321, 371, 393, 399, 482 blowups, 117—118 bonds as safe haven, 262 stocks and, 260—261, 261 Borish, Peter, 87, 163 breakout traders, 106 breakout trading, 173 breakouts, 161, 289 Breath (J.

A Mathematician Plays the Stock Market
by John Allen Paulos
Published 1 Jan 2003

My reaction, painful to recall, was, “At these prices I can finally get out of the hole.” I bought more shares even though I knew better. There was apparently a loose connection between my brain and my fingers, which kept clicking the buy button on my Schwab online account in an effort to avoid the losses that loomed. Outside of business, loss aversion plays a role as well. It’s something of a truism that the attempt to cover up a scandal often leads to a much worse scandal. Although most people know this, attempts to cover up are still common, presumably because, here too, people are much more willing to take risks to avoid losses than they are to obtain gains.

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Kozlowski, Dennis Kraus, Karl Krauthammer, Charles Kudlow, Larry Lakonishok, Josef Landsburg, Steven Lay, Ken LeBaron, Blake Lefevre, Edwin Leibweber, David linguistics, power law and Lo, Andrew logistic curve lognormal distribution Long-Term Capital Management (LTCM) losing through winning loss aversion lotteries present value and as tax on stupidity Lynch, Peter MacKinlay, Craig mad money Malkiel, Burton management, manipulating stock prices Mandelbrot, Benoit margin calls margin investments buying on the margin as investment type margin calls selling on the margin market makers decimalization and World Class Options Market Maker (WCOMM) Markowitz, Harry mathematics, generally Greek movies and plays about outguessing the average guess risk and stock markets and Mathews, Eddie “maximization of expected value” principle mean value. see also expected value arithmetic mean deviation from the mean geometric mean regression to the mean using interchangeably with expected value media celebrities and crisis mentality and impact on market volatility median rate of return Merrill Lynch Merton, Robert mnemonic rules momentum investing money, categorizing into mental accounts Morgenson, Gretchen Motley Fool contrarian investment strategy PEG ratio and moving averages complications with evidence supporting example of generating buy-sell rules from getting the big picture with irrelevant in efficient market phlegmatic nature of mu (m) multifractal forgeries mutual funds expert picks and hedge funds index funds politically incorrect rationale for socially regressive funds mutual knowledge, contrasted with common knowledge Nash equilibrium Nash, John Neff, John negatively correlated stocks as basis of mutual fund selection as basis of stock selection stock portfolios and networks Internet as example of price movements and six degrees of separation and A New Kind of Science (Wolfram) Newcomb, Simon Newcombe, William Newcombe’s paradox Niederhoffer, Victor Nigrini, Mark nominal value A Non-Random Walk Down Wall Street (Lo and MacKinlay) nonlinear systems billiards example “butterfly effect” or sensitive dependence of chaos theory and fractals and investor behavior and normal distribution Nozick, Robert numbers anchoring effect Benford’s Law and Fibonacci numbers and off-shore entities, Enron Once Upon a Number (Paulos) online chatrooms online trading optimal portfolio balancing with risk-free portfolio Markowitz efficient frontier of options. see stock options Ormerod, Paul O’Shaughnessy, James P/B (price-to-book) ratio P/E ratio interpreting measuring future earnings expectations PEG variation on stock valuation and P/S (price to sales) ratio paradoxes Efficient Market Hypothesis and examples of Newcombe’s paradox Parrondo’s paradox St.

I.O.U.: Why Everyone Owes Everyone and No One Can Pay
by John Lanchester
Published 14 Dec 2009

I’d never heard of them until Kahneman won the Nobel,* and when I first read about their work, it seemed to me to consist of things which were surprising only to economists. One of their interests was “hindsight bias,” the way in which a random sequence of events is given structure and narrative by the false perspective of looking back over it from its outcome. Another was “loss aversion,” the fact that people place a higher value on not losing money than on gaining it; another was on “the law of small numbers,” referring to people’s tendency to draw overconfident conclusions from small amounts of evidence. Their particular interest was in “heuristics,” the patterns of thinking people use to interpret data, and the strong conclusion they reached was that people’s heuristics are often wrong; we are much less accurate and less rational in our thinking than we believe ourselves to be.

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Morgan, 152 derivatives and, 64–71, 116, 120–21, 183 Exxon deal and, 67–68, 70, 121 Glass-Steagall Act and, 64–65 JPMorgan Chase, 190, 227 Julian, Harriette, 130–31 Kahneman, Daniel, 136–42, 193 Kerviel, Jérôme, 51 Keynes, John Maynard, 49n, 136 predictions of, 213–15 risk and, 55, 145 Kindleberger, Charles P., 104 King, Mervyn, 167, 178, 206 King’s Cross station, 88–90 Kreuger, Ivar, 105 Kynaston, David, 21, 23 leaderless group challenge, 139–40 Leeson, Nick, 51–52, 54 Lehman Brothers, 190, 204, 225 collapse of, 39, 75, 78 Leland, Hayne, 151 leverage: of banks, 35–36, 41–42, 70, 190 debt and, 60–61, 181, 190 deleveraging and, 41–42, 83 derivatives and, 51, 54–55 housing and, 60–61, 83, 95, 97 regulation and, 181, 186, 188, 190 risk and, 35–36 Li, David X., 115–17, 157–58 liabilities, 31–35 in balance sheets, 25–28, 31–34, 37 of banks, 25, 32–35, 37, 41, 204 of individuals, 27–28, 35 leverage and, 35, 41, 60 libel law, 93 life expectancies, 17, 213 liquidity, 212 housing and, 28–29, 90, 96–97 investments and, 60–61 Lloyds TSB, 36, 38–40 loans, lending, 74–76, 108–9 in balance sheets, 27, 30, 34 of banks, 22, 24, 27, 33–36, 41–42, 58–60, 67, 69–70, 74, 83–84, 91–94, 102, 117, 127, 129–30, 143, 146, 165, 187, 216–17, 229 credit and, 209, 216–17 derivatives and, 50–51, 55, 66–75, 80, 121–22 Exxon deal and, 67–68 interest rates and, 59–60, 66, 74, 102, 108, 145, 172–73 paying the bill and, 220–21 predatory, 122, 127–32 risk and, 66–67, 69–72, 74–75, 80, 95, 117, 145, 174 securitization in, 69, 74 see also mortgages London, 53, 84 housing in, 88–90 see also City of London Long-Term Capital Management (LTCM): collapse of, 142, 162, 164–65, 230–31 derivatives and, 54–56, 80 loss aversion, 137 Lovelock, James, 231 Lowenstein, Roger, 161 Macmillan, Harold, 216 Madoff, Bernard, 105, 171, 191–92, 195 Mailer, Norman, 172 Manias, Panics, and Crashes (Kindleberger), 104 manufacturing, 4, 13, 58, 109, 229 and financial vs. industrial interests, 197, 199 Marxist analysis of, 15–16 stocks and, 148–49 market discipline, 183–84 Markopolos, Harry, 192 Markowitz, Harry, 147–49, 158 mark to market, 42, 105–6 Marx, Karl, 15–16 Maryland, housing in, 125–31 Masters, Blythe, 68, 121 mathematics, 5, 231 derivatives and, 47–48, 52–54, 115–17, 166 risk and, 46, 55–56, 74, 133, 136, 146–50, 154, 158, 160–67, 202 of share pricing, 147–48 Meriwether, John, 54 Merrill Lynch, 39, 77, 120, 190, 227 Merton, Robert, 54–55 microeconomics, 137 Minsky, Hyman, 104 Monetary Policy Committee, 178–79 money: assumptions based on primacy of, 202–4 cost of, 102–3 flows of, 7–9, 26 inconceivable amounts of, 8 Money Machine, The (Coggan), 25 Moody’s Investors Service, 62, 70, 114, 119, 208, 210 Morgan, John Pierpont, 20, 64 Morgan Stanley, 40, 64, 227 Morris, Charles, 42 mortgages, 38–40, 83–87, 89–95, 97–102, 110–32 in balance sheets, 27–28 balloon payments on, 100 and buy-to-let properties, 177 conforming, 112, 124 credit ratings and, 123–24, 126 of Cutter family, 126–27 defaults on, 159–60, 163, 165, 229 derivatives and, 38, 57–58, 75–76, 112–22, 132, 157–60, 172, 210–12 discriminatory practices and, 99–101, 127 durations of, 95 endowment, 86–87, 89–90, 146 Iceland’s economic crisis and, 10–11 interest and, 8, 58, 86, 89, 91–92, 95, 100, 102, 108, 110, 112–14, 122, 128, 145–46, 174, 176, 212 “liar,” 126, 132 “no doc,” 132 No Income, No Job or Assets (NINJA), 126 piggyback, 132 predatory lending and, 122, 127–32 regulation and, 99–100, 185 risk and, 145, 158–60, 163–65 sizes of, 92–94 subprime, 38, 75, 83, 100, 113–19, 122–25, 127, 132, 157–59, 165, 202, 210 see also houses, housing, home ownership Nasdaq, 104 nationalization, 24, 39–40, 228–30 New York Times, The, 77, 98, 208 “Night in Tunisia, A,” 45 Nikkei 225, 51–52, 54 9/11 terrorist attacks, 2, 107 Northern Rock, 5, 39, 94, 194, 206 Obama, Barack, 77, 205 regulation and, 188, 190, 223–24 Objectivism, 142–43, 173 oil, 3–4, 107–8, 148–49 “On Default Correlation” (Li), 116 options, 50–52, 151, 174, 184 how they work, 46–47, 50–51 Osaka exchange, 54 Pacioli, Luca, 26 panic of 1893, 64 panic of 1907, 20, 64 Parker, Charlie, 45 Paulos, John Allen, 8 pensions, 76–77, 165, 204 in balance sheets, 27–28, 31 Phillips, Julia, 199 politics, politicians, 5–6, 19–21, 23–25, 81, 118–19, 169–70, 176–78, 217–26, 228–32 AIG bailout and, 76–78 banks and, 25, 33, 43, 182, 186, 195, 202, 207, 211, 217, 228–31 bonds and, 29–30, 61–62, 103, 109, 118, 144, 176–77, 208–9 derivatives and, 57, 183–86 financial industry’s ascent and, 19–20 free-market capitalism and, 14–15, 19, 21, 23–24 housing and, 87–89, 91, 96–101, 177–78 Iceland’s economic crisis and, 9–10, 12, 24, 223 interest rates and, 102–3, 107–8, 172–80, 221 paying the bill and, 219–23 regulation and, 15, 19–21, 24, 169, 180–92, 195, 199, 201, 223–26 risk and, 142–43, 164–66, 174, 184 Ponzi, Charles, 105 Ponzi schemes, 191–92 poor, poverty, 3–4, 13, 21, 82, 179, 196 housing and, 100, 113, 118, 121–23, 126–27, 130–31, 163 pork bellies, 48–49 portfolio insurance, 151–52, 162 “Portfolio Selection” (Markowitz), 147 Posner, Richard A., 120, 174, 182, 193 Powell, Anthony, 62 price, prices, 105–11, 203 and banking-and-credit crisis, 216–18, 220 bonds and, 61, 63, 102–3, 108–10, 144 derivatives and, 38, 46–52, 54, 56, 75, 158–59, 166 of houses, 5, 28–29, 37–38, 61, 71, 86–91, 101, 109–11, 113, 115, 125, 157, 160, 164–66, 173–76, 194, 208 of oil, 3–4, 107–8, 148–49 risk and, 145–50, 158–59, 164–66 of stocks, 102, 105–6, 109–10, 147–51, 158, 174 of toxic assets, 37–38, 42 volatility of, 47–48, 148–50 “Pricing of Options and Corporate Liabilities, The” (Black and Scholes), 45, 47–48, 147 probabilities, 46, 55, 74, 115, 141, 145, 153–55, 160–63 profits, 20, 28, 104–6, 110, 192, 226–28, 230 banks and, 33, 35, 67, 78, 227–28 and benefits of debt, 59–60 derivatives and, 50, 54, 57, 63, 65, 106, 114, 121–22 Enron and, 105–6 regulation and, 204, 226 risk and, 150, 226 Protection of Homeowners in Foreclosure Act, 131 “Quiet Coup, The” (Johnson), 19–20, 185–86 Ragtime (Doctorow), 64 Rand, Ayn, 142–43, 173 Reagan, Ronald, 14, 19–20, 24, 142, 185 recessions, 42, 89, 94, 142, 171, 175, 219 regulation, deregulation, 15, 19–22, 24, 169, 180–202 banking and, 21, 33, 180–91, 194–96, 199–200, 202, 204–5, 208, 211, 223–27 bond ratings and, 208–9 derivatives and, 68, 70, 73, 153, 183–86, 200–201 framework and regime of, 189–92 market discipline and, 183–84 mortgages and, 99–100, 185 proposals for, 223–26 risk and, 143, 153, 164, 187–88, 191, 195, 202, 204–5, 212, 224, 226 in U.K., 21–22, 105n, 180–82, 194–96, 199–201, 218 in U.S., 181, 184–92, 195, 199–200, 223–24, 227 Reykjavík, 10, 12, 170 risk, risks, 49–58, 66–76, 133–36, 141–67, 211–12, 219 AIG and, 75–76 assessment of, 46, 55–56, 74, 133, 135–36, 141–43, 145–67, 187–88, 191, 202, 205, 212, 216, 226 banks and, 19, 34–37, 41, 133, 135–36, 143, 150–54, 156–57, 160, 165–66, 174, 187–88, 191–95, 202, 204–7, 216, 224, 226, 228, 230 bonds and, 61–63, 103, 118, 144, 154, 208 derivatives and, 46–47, 49–52, 54–55, 57–58, 66–75, 78–80, 114–15, 117–22, 151, 153, 158–60, 163, 166–67, 184–85, 205, 212 desirability of, 144, 146, 150, 206–7 diversification and, 146–48 Greenspan and, 142–43, 164–66, 174, 184 hedging of, 49–50, 52, 58, 115, 205 historical data and, 163, 166 housing and, 88, 94–97, 112–13, 125, 129, 145, 158–60, 163–65 investing and, 5, 68, 70, 88, 103, 144, 146–53, 158, 165, 184, 190 leverage and, 35–36 LTCM and, 55–56 overconcentration of, 72–73 regulation and, 143, 153, 164, 187–88, 191, 195, 202, 204–5, 212, 224, 226 securitization and, 69–70, 163, 165 of stairs, 134–35 VAR and, 151–57, 162–63 risk-adjusted return on capital (RAROC), 150–51 Ritholtz, Barry, 219–20 Robinson, Phillip, 128–31 Rogers, Jim, 221 Royal Bank of Scotland (RBS), 34–36, 120, 227 bailout of, 32, 40, 204 Russia, 3, 15–16, 18, 53 bond default of, 55–56, 162, 164–65 Salomon Brothers, 63 Sanford, Charles, 150 Santander, 40 savings, 28, 86, 107, 177, 179, 187 savings and loan crisis, 73, 185, 220 Scholes, Myron, 45, 47–48, 54–55, 147 Securities and Exchange Commission (SEC), 195 credit ratings and, 209–10 regulation and, 153, 186, 189–92 securitization, 20, 22, 200 derivatives and, 69–70, 74, 113–14, 117–19, 122, 212 risk and, 69–70, 125, 163, 165, 212, 224 selling, sales, 34, 42, 104, 174, 203 of bonds, 59, 61–63, 144 derivatives and, 46–50, 52, 56, 65, 67–68, 73–74, 120 of equity, 58–59 of houses, 28–29, 71, 89–90 risk and, 151–52, 165, 224 Shiller, Robert, 106, 145n, 194 Simon, David, 83–84 Singapore exchange, 54 Skilling, Jeffrey, 106 small numbers, law of, 137 Sociét Générale, 51, 77 solvency, insolvency, 28–29 of banks, 36–38, 40–43, 64, 74–75, 120 Spain, 15, 40, 177, 214 contracting economy of, 222–23 housing in, 92, 110 special purpose vehicles (SPVs), 70, 120 stairs, deaths caused by, 134–35 Standard & Poor’s (S&P), 62, 114, 151, 209 statistics, 160–62 Stefánsdóttir, Rakel, 9–10, 12 stock market, stocks, 22, 54–55, 61, 76, 80, 101–11, 115, 226 bubbles and implosions in, 3, 42, 103–9, 142, 175–76 derivatives and, 50–52, 54 investing in, 59, 73, 101–7, 111, 146–52, 158, 175, 192 new-economy, 103 1929 crash of, 152, 199, 213 October 1987 crash of, 142, 151–52, 161–62, 164–65 prices of, 102, 105–6, 109–10, 147–51, 158, 174 structured investment vehicles (SIVs), 120 Summa de Arithmetica (Pacioli), 26 Summers, Lawrence, 43, 74, 188 Taleb, Nassim, 53, 155–56 Tax Reform Act of 1986 (TRA), 100 technology, 42, 104, 149, 155, 166 terrorism, 2, 12, 18, 107 Tett, Gillian, 121, 193 Thatcher, Margaret, 199, 217, 222 free-market capitalism and, 14, 21, 24 on housing, 87, 91, 98 regulation and, 21, 195–96 torture, end of ban on, 18 tranching, 117–18, 122 Treasury, British, 181–82 Treasury, U.S., 43, 54, 64, 74, 76–78 AIG bailout and, 76, 78 regulation and, 188–90 Treasury bills (T-bills), 29–30, 62, 103, 118, 144, 208 China’s investment in, 109, 176–77 Trichet, Jean-Claude, 92 Trillion Dollar Meltdown, The (Morris), 42 Troubled Assets Relief Program (TARP), 37, 189 Turner, Adair, 181 Tversky, Amos, 136–38, 141 UBS, 36, 120 uncertainty, 96 fair value theory and, 147–48 risk and, 55–56, 153, 163 United Kingdom, 9, 11–12, 18, 28–29, 61, 122–24, 134, 139, 194–202, 216–18 banking in, 5, 11, 32–36, 38–40, 51–54, 76–77, 89, 94, 120, 146, 180, 194–96, 199, 202, 204–6, 211–12, 217, 227–28 bill of, 220–22, 224 and City of London, 21–22, 32, 195–97, 200, 217–18 credit ratings and, 123–24, 209 derivatives and, 72, 200–201 financial vs. industrial interests in, 196–99 free-market capitalism in, 14–15, 21, 230 GDP of, 32, 214, 220 Goodwin’s pension and, 76–77 housing in, 38, 87–98, 110, 122, 177–78 interest rates in, 102, 177–80 personal debt in, 221–22 prosperity of, 214, 216 regulation in, 21–22, 105n, 180–82, 194–96, 199–201, 218 United Nations, 4 United States, 17–22, 34, 62–71, 120–31, 134n, 165, 199–201 AIG bailout and, 76–78 banks of, 36–37, 39–40, 43, 63–71, 73, 75, 77–78, 84, 116, 120–21, 127, 150, 152, 163, 183, 185, 190, 195, 204, 211–12, 219–20, 225, 227–28 bill of, 219–20 China’s investment in, 109, 176–77 credit and, 109, 123–24, 195, 208–9, 211 free-market capitalism in, 14–15, 230 housing in, 37, 82–86, 95, 97–101, 109–10, 114–15, 122, 125–31, 157–58, 163 interest rates in, 102, 107–8, 173–77 regulation in, 181, 184–92, 195, 199–200, 223–24, 227 urban desolation in, 81–86 value, values, 42, 74–75, 78–80, 103–4, 179, 181, 217–18, 220, 227 bonds and, 61, 103 derivatives and, 38, 48–49, 185, 201 housing and, 28–29, 71, 90, 92–95, 111, 176 investing and, 60–61, 104, 198 LTCM and, 55–56 notional, 38, 48–49, 80 value at risk (VAR), 151–57, 162–63 Vietnam War, 18, 220 Viniar, David, 163 volatility, 20, 158 risk and, 47–48, 148–50, 161 Volcker, Paul, 20 Waldrow, Mary, 127 Wall Street, 22, 53, 64, 129, 188 Washington Post, The, 18 wealth, 4, 10, 19–21, 64, 204, 206 financial industry’s ascent and, 20–21 in free-market capitalism, 15, 19, 230 housing and, 87, 90, 121 Keynes’s predictions on, 214–15 in West, 218–19 Weatherstone, Dennis, 152 Wells Fargo, 84, 127 Wessex Water, 105n West, 14–18, 43, 213, 231 conflict between Communist bloc and, 16–18 free-market capitalism in, 14–15, 17, 21, 23 wealth in, 218–19 wheat, 49n, 52 When Genius Failed (Lowenstein), 161 Williams, John Burr, 147 Wilson, Lashawn, 130–31 Wire, The, 83–84 World Bank, 58, 65, 69 * GDP, which will be mentioned quite a few times in this story, sounds complicated but isn’t: it’s nothing more than the value of all the goods and services produced in an economy.

Are You Smart Enough to Work at Google?: Trick Questions, Zen-Like Riddles, Insanely Difficult Puzzles, and Other Devious Interviewing Techniques You ... Know to Get a Job Anywhere in the New Economy
by William Poundstone
Published 4 Jan 2012

“I would really regret it if I switched and lost. It’s best to stay with your first choice.” These are expressions of loss aversion. It’s universal human nature to recoil from a decision that might leave one worse off, even when the odds are favorable. “Better safe than sorry.” Anyone who invents new products would do well to keep this in mind. The consumer thinking of switching boxes or brands may be motivated by reasons that have nothing to do with logic. Math geniuses are as loss averse as everyone else. It’s said that the famed mathematician Paul Erdös got this puzzle wrong the first time he heard it.

Cogs and Monsters: What Economics Is, and What It Should Be
by Diane Coyle
Published 11 Oct 2021

For example, RCTs or econometric evaluations will sometimes produce conclusions that run against politically acceptable ideas. One study by University of Chicago researchers into what incentives produced the largest impact on test scores for Chicago schoolchildren found that by a long way the biggest effect came from paying teachers in advance a large bonus conditional on their pupils’ results; the power of loss aversion meant these teachers were determined to achieve the outcomes so they would be able to keep the money (Fryer et al. 2012). How would advance bonuses in the public sector play politically? This is, of course, a rhetorical question. The findings of RCTs may well conflict with political or cultural beliefs about the right course of action.

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, Technological Forecasting and Social Change, 114, 254–280. Friedman, M., 1966, ‘The Methodology of Positive Economics’, in Essays in Positive Economics, Chicago: University of Chicago Press, 3–16. Fryer, R., S. Levitt, J. List, and S. Sadoff, 2012, ‘Enhancing the Efficacy of Teacher Incentives through Loss Aversion: A Field Experiment’, NBER Working Paper 18237, National Bureau of Economic Research, Cambridge, MA. Furman, Jason et al., 2019, ‘Unlocking Digital Competition’, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/785547/unlocking_digital_competition_furman_review_web.pdf.

What's Wrong With Economics: A Primer for the Perplexed
by Robert Skidelsky
Published 3 Mar 2020

Think of a newspaper article that claims it can help you imitate Mark Zuckerberg’s morning routine. The obvious implication is that you too could become a billionaire if you just wore grey t-shirts and ate the right breakfast, but this ignores the multitudes of non-billionaires doing just that. 2. Loss aversion It is fairly well established that people hate losing something more than they love gaining it. Dropping a $10 note is more bitter than finding one is sweet. We are hard-wired, to some extent, to hold on to what we’ve got. Students given coffee mugs free from the campus bookstore will not part with them for $6 even though this junk fell out of the sky, and had they desired them they could have got them at the nearby store for the price of $6. 3.

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A bunch of Japanese tourists drove their car into the sea because their satnav told them they were on a road. Airplane crashes have happened because pilots trusted their faulty navigation systems rather than the evidence of their eyes. 6. Sunk cost fallacy This is a combination of anchoring and loss aversion. People will keep on ploughing money into a failed investment, because they can’t face the psychological pain of admitting that it had failed, or carry on waging a war that they should have abandoned long ago, because they cannot bring themselves to admit that it was in vain. 7. Hindsight bias This is central to human thinking and makes the social and economic worlds appear much more predictable and less erratic than they really are.

Red-Blooded Risk: The Secret History of Wall Street
by Aaron Brown and Eric Kim
Published 10 Oct 2011

There is no doubt that it is both real and important. Among other things, it explains part of loss aversion. Losing something you have is more painful than getting something equivalent is pleasurable. This is not inconsistent with utility theory, but it requires that individuals’ utility function change as they acquire or lose goods. The simple version of utility theory that says all risk is bad cannot accommodate that, but sophisticated modern utility theory has no difficulty. Why would evolution allow animals—including humans—to hold inconsistent values? The weight of the evidence is that loss aversion evolved to reduce intraspecies fighting and to allow investment.

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(Livio) Jackknife, the Bootstrap, and Other Resampling Plans, The (Efron) Jackpot Nation (Hoffer) Jessup, Richard John Bogle on Investing (Bogle) Johnson, Barry Johnson, Simon JPMorgan Junk bonds Kahneman, Daniel Kamensky, Jane Kaplan, Michael Kassouf, Sheen Kelly, John Kelly bets/levels of risk Kelly principles/investors Keynes, John Maynard Key performance indicators (KPIs) Key risk indicators (KRIs) King of a Small World (Bennet) Korajczyk, Robert Knetsch, Jack Knight, Frank Kraitchik, Maurice Krüger, Lorenz Laplace, Pierre-Simon Lehman Brothers Leitzes, Adam Lepercq de Neuflize Leverage Levine, David Levinson, Horace Lewis, Michael Limits of Safety, The (Sagan) Liquidity Livio, Mario Logic of Failure, The (Dorner) Long-Run Collaboration on Games with Long-Run Patient Players, A (Fudenberg and Levine) Loss aversion Lowenstein, Roger Mackay, Charles Madoff, Bernie Mallaby, Sebastian Man with the Golden Arm, The (Bennet) Managed futures Managing risk Mandelbrot, Benoit Market: “beating” the efficiency (see also Efficient markets theory) equilibrium (see Equilibrium) portfolio prices return sympathy Mark-to-market accounting Markowitz, Harry.

Competition Overdose: How Free Market Mythology Transformed Us From Citizen Kings to Market Servants
by Maurice E. Stucke and Ariel Ezrachi
Published 14 May 2020

Although as we shop for a hotel room we haven’t actually purchased that reservation for $26, some part of us feels like we have and we now feel attached to it. With drip pricing, a former chief economist of the UK competition authority noted, “Consumers feel they’ve already made the decision to purchase [which] creates loss aversion—consumers have committed time and effort to the search before being hit with extra charges.”33 A third weakness is something we often perceive as a strength, namely commitment and consistency. Here, after having invested a lot of time and effort into this purchase, we want to see the purchase through to its end.34 Economists call this the sunk cost fallacy.

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See also CoreCivic Costco and other club stores, 102–3 cream skimming, 169–70, 175, 183–87 credit card industry, 68, 69, 70–71, 75–77 credit default swaps, 128 criminal sentencing, 80–81, 176–77 crony capitalism, 160, 163, 230, 285 Cruz, Ted, 266 cultural conditioning college as route to social mobility, 29–30 competition delivers quality at a low price, 47–48, 49 competition is always good, x, 284 embarrassment for gullibility, 70 and escalation paradigm, 35–36 lifelong superior achievement goal, 32–33 to love competition, 125 to not compete, 122 self-interest, 71, 235–36 and status competition, 28 See also human nature culture, x–xi Dalai Lama, 253 Dale, Stacy, 36–37 D’Angelo, Jonathan, 112–13 Dartmouth College, 25 Darwin, Charles, 39 data for Amazon’s personal recommendations, 105–6, 107 analytical power of, 218–19 children’s data gleaned for advertisers, 193–95 college scorecard data, 300–304, 318n86 on consumers’ behavior, 87–91, 204–5, 207–9 Gamemakers attract bidders/advertisers, 207–9 Gamemakers’ harvesting techniques, 194, 203–7 in Las Vegas, 87–91 dating services, online overview, 108–9 competition levels, 109–12, 111 InterActiveCorp, 109–11, 111, 115–16 and marriage, 114–15 profiting from choice overload, 113–14, 115–16 slow dating, 117 deadbeat customers, 70–71 Dear Genevieve effect, 12–15, 16, 19–20, 38–39 death bonds, 245 decision aids for choice overload, 101–2 de-escalating the arms race colleges and universities, 25–27, 40, 133–34 collegiate sports, 134–38, 140–41 students and parents, 27–34, 40 Deloitte, 277 deregulation of banks, 126–30 derivatives market, 261–63 diaper apps, 197 digital ad market, 210 diminishing returns, 96–97 dissent, noncompliance, and change, 284–87 divergence of individual and collective interests, 12–20, 39–40, 242–43, 263 DOJ (US Department of Justice), 128, 172–73, 174, 232 do not track features on mobile phones, 212–13 Dostoyevsky, Fyodor, 71 drip pricing overview, 147 and anchor value, 80–81 and brain fatigue, 81–82 Caesars opposition to, 84–87 casino lobbyists combat FTC’s plan, 150–52 vs. consumer’s ability to compare prices, 155–57 consumers’ loss aversion, 81 countries with laws against, 148 FTC attempt to legalize, 148–50 in hotels, 78–80, 82–84, 147, 148–54 lobbyists use competition ideology, 150–52 and sunk cost fallacy, 81 Duhigg, Charles, 227 Duke University, 16–17, 24 dynamic ads, 204–5 Easterbrook, Frank H., 234–35 Economist, 197 Eliot, T.

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, The (movie), 258 InterActiveCorp (IAC), 109–11, 111, 115–16 International Monetary Fund (IMF), 182–83 investment ratings industry, 178–79 invisible hand principle, 3–4, 62, 96–97 Ireland, 46 irrationality of competition, 34–38 irrationality of consumers, 71–74 Ivory Coast, Africa, 54–55 Iyengar, Sheena, 98–99, 100 jam choices experiments, 98–99, 100–101 Japan, 249 job satisfaction levels, 227 judges earn kickbacks from private prison industry, 174 Justice Policy Institute, 173 Kahneman, Daniel, 237 Kaptur, Marcy, 158 Kauffman Foundation, 272 Kay Forensic Services, United Kingdom, 182 Kelloggs, 64–65 kenosis, 257–58 King, Martin Luther, Jr., 231 Knight Commission on Intercollegiate Athletics, 134, 137–38 Krueger, Alan B., 36–37 Krugman, Paul, 128–29 kudzu and kudzu-ing overview, xiii, 146–47 competition ideology for deregulating, 155–57 lack of competition in private prison market, 176–77 with lobbying, money, and competition ideology, 160 politicians fighting legislation to end price dripping in the US, 152–53 by Trump, to financial regulations to protect consumers, 268–69, 285–86 and UK privatizing some health services, 184–87 labor and pressure to lower prices, 54 laptop cameras and data harvesting, 214 Lay, Ken, 246 lemon markets, 63–64, 124 Lepper, Mark, 98–99, 100 life satisfaction levels, 247–49, 252 Little Ivies staying out of collegiate sports arms race, 138–39, 140 lobbyists, 146–61 overview, xiii casino lobbyists oppose ending drip pricing, 150–51 and Citizens United case in US Supreme Court, 155 kudzu and kudzu-ing the competition, 146–47 and money suffocating democratic values, 160 for private prisons, 173–76 local economies, supporting, 289–91 loss aversion, 81 low-value customers, 87 Mactaggart, Alastair, 286 MacTavish, Craig, 4 marginal benefit and marginal cost, 96–97 market economy competition ideologues relying on, 127 derivatives market, 261–63 farmers markets, 287–89 regulation of the market, 260 social capital necessary for, 249–51 social outcome of, 124, 125 trust and fairness as foundation, 244 marketing, 59–60, 67–68 marriage and online dating, 114–15 “Massive-Scale Emotional Contagion” study by FB, 219 Match.com, 108–9, 113–14 matriculation from elite private schools, 31–34, 296–98 McCaskill, Claire, 150, 154 McDonald’s, 52 McNamee, Roger, 199–200 measuring success colleges and universities, 15, 34, 38–39 InterActiveCorp, 109–11, 111, 115–16 public school ranking system, 7–9, 282 Medicaid expansion by states, 286 mental health and stress of superior achievement goal, 34 MGM Grand Hotel, Las Vegas, 154 middle class net worth, 160 Milgram, Stanley, 279–82, 285 Mill, John Stuart, 94 minimum wage increases by states, 286 mobile phones, 107–8, 196–98, 212–13 Moore, Don, 35 morality continuum, 257–58.

How to Speak Money: What the Money People Say--And What It Really Means
by John Lanchester
Published 5 Oct 2014

An example is “loss aversion,” in which people are provably more unwilling to take risks that involve losses than to take risks involving gains, even when the outcomes are, in mathematical terms, identical. The fact that people don’t always behave rationally may not come as news in the wider world, but the intellectual challenge provided to conventional economics by behavioral economics is big and important. It’s also a field that offers useful takeaways for the ordinary person, because you can catch yourself doing some of the things described by behavioral economists, such as loss aversion and “hindsight bias,” i.e., the tendency to explain things that happened in terms of how they turned out, rather than how they seemed at the time.

The Wisdom of Frugality: Why Less Is More - More or Less
by Emrys Westacott
Published 14 Apr 2016

And he won’t allow the second to value or admire anything but wealth and wealthy people or to have any ambition other than the acquisition of wealth or whatever might contribute to getting it.8 The “sunk cost” fallacy: Research by psychologists Daniel Kahneman and Amos Tversky in the 1970s led them to conclude that for most human beings a concern to avoid losses is a more powerful motivator than the desire to realize gains. Many other psychologists have followed in their footsteps and investigated the phenomenon of loss aversion. A study by Hal Arkes and Catherine Blumer is representative and revealing. Participants in their study were asked to imagine that they had bought two nonrefundable tickets, a $100 ticket for a skiing trip to Michigan, and a $50 ticket for a skiing trip to Wisconsin. They were then told that the dates of these trips conflicted, so they could go on only one of them.

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See self-sufficiency individualism, 173, 284 inequality, 151–52, 172–73, 218, 240, 285 Irvine, William, 7 Islam, 31, 78 It’s a Wonderful Life, 97 Jackson, Michael, 169 Jains, 31, 272 Jesus, 44, 45, 63, 103, 104, 141, 201, 283 Johnson, Samuel, 10, 65 Judaism, 31, 78 Kahneman, Daniel, 144, 153, 222 Kant, Immanuel, 35, 273 Kardashian, Kim, 214 karma, 74 Kazez, Jean, 88 Keynes, John Maynard, 80, 166, 241, 242 kibbutzim, 22 Kozlowski, Dennis, 168–69 Krugman, Paul, 226 Kublai Kahn, 147 Lafargue, Paul, 80, 81 Laham, Simon, 114 leisure, 77, 242. See also work Lichtenberg, Judith, 246 Linder, Staffan, 242 living cheaply, 14–18, 21, 37, 276. See also frugality; simple living locavorism, 261–64 loss aversion, 144 Louv, Richard, 132 luxuries: definition of, 177–78. See also luxury luxury, 33, 140, 146–47, 158–59, 171, 216; dangers of, 52, 55, 56, 111–16, 208–10 Lycurgus, 53 magnificence, 193 Mahavira, 32 Mandela, Nelson, 64 Mandeville, Bernard, 158–59, 216 Marcus Aurelius, 7, 24, 50, 57, 75, 98, 103, 117, 120, 148, 275 Marie Antoinette, 169 Marley, Bob, 102–4 Marx, Karl, 83, 87, 212, 281–82 materialism, 68, 99, 167, 173 material security: of modern life, 201–2, 204–5, 253 McCartney, Paul, 161 McCoy, Travie, 161 Mead, Rebecca, 198 Menedemus, 63 mercenariness, 142–44 Mill, John Stuart, 69–71, 74 Minnelli, Liza, 168–69, 177 Les Misérables, 63 miserliness, 143 Mittal, Lakshmi, 197 monasteries, 22, 31, 35, 45, 56 monastic orders, 53.

A Little History of Economics
by Niall Kishtainy
Published 15 Jan 2017

All economics is about behaviour, of course, but behavioural economics was new because it built its theories around the quirks in people’s actual decision-making, rather than simply assuming that they were completely rational. One quirk is that people weigh up gains and losses differently. Rationally, a gain of $50 should exactly offset a loss of $50. However, people seem to hate losses more than they love gains. When he was still a student, the behavioural economist Richard Thaler (b. 1945) noticed ‘loss aversion’ in one of his own economics professors! The professor, a wine lover, was willing to pay a high price for a bottle of a certain wine to add to his collection. But he really hated giving one up: even if you offered him three times what he’d paid, he wouldn’t sell a bottle to you. Thaler and Kahneman did an experiment on a group of people to see what was going on.

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(i), (ii) Kerala (India) (i) Keynes, John Maynard (i), (ii), (iii), (iv), (v), (vi) Keynesian theory (i), (ii), (iii) Klemperer, Paul (i) Krugman, Paul (i), (ii) Kydland, Finn (i), (ii) labour (i) in ancient Greece (i) and market clearing (i) women as unpaid (i) labour theory of value (i), (ii) laissez-faire (i) landowners (i), (ii), (iii) Lange, Oskar (i) law of demand (i), (ii) leakage of spending (i) Lehman Brothers (i) leisure class (i) leisured, women as (i) Lenin, Vladimir Ilyich (i), (ii) Lerner, Abba (i) Lewis, Arthur (i) Lincoln, Abraham (i) List, Friedrich (i) loss aversion (i) Lucas, Robert (i), (ii) MacKay, Charles (i) Macmillan, Harold (i) macro/microeconomics (i) Malaysia, and speculators (i) Malthus, Thomas (i), (ii), (iii) Malynes, Gerard de (i), (ii) manufacturing (i), (ii) division of labour (i) see also Industrial Revolution margin (i) marginal costs (i), (ii) marginal principle (i), (ii), (iii) marginal revenue (i) marginal utility (i), (ii) market, the (i) market clearing (i) market design (i) market failure (i), (ii), (iii), (iv) ‘Market for Lemons, The’ (Akerlof) (i) market power (i) markets, currency (i), (ii) Marshall, Alfred (i), (ii), (iii), (iv), (v) Marx, Karl (i), (ii), (iii), (iv), (v), (vi), (vii) Marxism (i) mathematics (i), (ii), (iii) means of production (i) mercantilism (i), (ii) Mesopotamia (i) Mexico, pegged currency (i) micro/macroeconomics (i) Microsoft (i) Midas fallacy (i) minimum wage (i) Minsky, Hyman (i) Minsky moment (i), (ii) Mirabeau, Marquis de (i), (ii), (iii) Mises, Ludwig von (i), (ii), (iii), (iv) mixed economies (i), (ii) Mobutu Sese Seko (i) model villages (i) models (economic) (i), (ii), (iii), (iv) modern and traditional economies (i), (ii) monetarism (i) monetary policy (i), (ii) money (i), (ii), (iii), (iv), (v), (vi) see also coins; currency money illusion (i) money wages (i) moneylending see usury monopolies (i), (ii) monopolistic competition (i), (ii) monopoly, theory of (i) monopoly capitalism (i), (ii), (iii) monopsony (i) moral hazard (i), (ii) multiplier (i) Mun, Thomas (i), (ii), (iii) Muth, John (i) Nash, John (i), (ii) Nash equilibrium (i) national income (i), (ii), (iii), (iv), (v) National System of Political Economy (List) (i) Nelson, Julie (i) neoclassical economics (i) net product (i) Neumann, John von (i) New Christianity, The (Saint-Simon) (i) new classical economics (i) New Harmony (Indiana) (i) New Lanark (Scotland) (i) Nkrumah, Kwame (i), (ii) non-rival good (i) Nordhaus, William (i), (ii) normative economics (i), (ii) Obstfeld, Maurice (i) Occupy movement (i) oligopolies (i) opportunity cost (i), (ii) organ transplant (i) output per person (i) Owen, Robert (i) paper money (i), (ii) Pareto, Vilfredo (i) pareto efficiency (i), (ii) pareto improvement (i) Park Chung-hee (i) partial equilibrium (i) pegged exchange rate (i) perfect competition (i), (ii), (iii), (iv), (v) perfect information (i) periphery (i) phalansteries (i) Phillips, Bill (i) Phillips curve (i), (ii), (iii), (iv), (v), (vi), (vii) physiocracy (i), (ii) Pigou, Arthur Cecil (i), (ii), (iii) Piketty, Thomas (i), (ii), (iii) Plato (i), (ii), (iii) policy discretion (i) Ponzi, Charles (i) Ponzi finance (i) population and food supply (i), (ii), (iii) of women (i) positive economics (i) poverty (i), (ii), (iii), (iv), (v) in Cuba (i) Sen on (i) and utopian thinkers (i) Prebisch, Raúl (i) predicting (i) Prescott, Edward (i), (ii) price wars (i), (ii) primary products (i) prisoners’ dilemma (i) private costs and benefits (i) privatisation (i) productivity (i), (ii), (iii) profit (i), (ii), (iii), (iv) and capitalism (i), (ii) proletariat (i), (ii) property (private) (i), (ii), (iii), (iv), (v) and communism (i), (ii), (iii), (iv) protection (i), (ii), (iii) provisioning (i) public choice theory (i) public goods (i) quantity theory of money (i) Quesnay, François (i) Quincey, Thomas de (i), (ii) racism (i) Rand, Ayn (i) RAND Corporation (i), (ii) rate of return (i), (ii) rational economic man (i), (ii), (iii), (iv), (v) rational expectations (i), (ii), (iii), (iv), (v) real wages (i), (ii), (iii) recession (i) and governments (i), (ii), (iii) Great Recession (i) Keynes on (i), (ii) Mexican (i) redistribution of wealth (i) reference points (i) relative poverty (i) rent on land (i), (ii), (iii) rents/rent-seeking (i) resources (i), (ii) revolution (i), (ii), (iii), (iv) Cuban (i) French (i), (ii), (iii), (iv) Russian (i), (ii) Ricardo, David (i), (ii), (iii) risk aversion (i) Road to Serfdom, The (Hayek) (i) robber barons (i) Robbins, Lionel (i) Robinson, Joan (i) Roman Empire (i) Romer, Paul (i) Rosenstein-Rodan, Paul (i) Roth, Alvin (i), (ii) rule by nature (i) rules of the game (i) Sachs, Jeffrey (i) Saint-Simon, Henri de (i) Samuelson, Paul (i), (ii) savings (i), (ii) and Say’s Law (i) Say’s Law (i) scarcity (i), (ii), (iii), (iv), (v), (vi) Schumpeter, Joseph (i), (ii) sealed bid auction (i) second price auction (i) Second World War (i) securitisation (i) self-fulfilling crises (i) self-interest (i) Sen, Amartya (i), (ii) missing women (i), (ii), (iii) services (i) shading bids (i), (ii) shares (i), (ii), (iii), (iv), (v), (vi) see also stock market Shiller, Robert (i), (ii) signalling (i) in auctions (i) Smith, Adam (i), (ii), (iii), (iv), (v) social costs and benefits (i) Social Insurance and Allied Services (Beveridge) (i) social security (i), (ii) socialism (i), (ii), (iii), (iv), (v) socialist commonwealth (i) Socrates (i) Solow, Robert (i) Soros, George (i), (ii), (iii) South Africa, war with Britain (i) South Korea, and the big push (i) Soviet Union and America (i) and communism (i), (ii) speculation (i) speculative lending (i) Spence, Michael (i) spending government (fiscal policy) (i), (ii), (iii), (iv), (v), (vi), (vii) and recessions (i), (ii) and Say’s Law (i) see also investment stagflation (i), (ii) Stalin, Joseph (i) standard economics (i), (ii), (iii), (iv) Standard Oil (i) Stiglitz, Joseph (i) stock (i) stock market (i), (ii), (iii), (iv), (v) stockbrokers (i) Strassmann, Diana (i), (ii) strategic interaction (i), (ii) strikes (i) subprime loans (i) subsidies (i), (ii) subsistence (i) sumptuary laws (i) supply curve (i) supply and demand (i), (ii), (iii), (iv) and currencies (i) and equilibrium (i), (ii) in recession (i), (ii), (iii) supply-side economics (i) surplus value (i), (ii) Swan, Trevor (i) tariff (i) taxes/taxation (i) and budget deficit (i) carbon (i) and carbon emissions (i) and France (i) and public goods (i) redistribution of wealth (i) and rent-seeking (i) technology as endogenous/exogenous (i) and growth (i) and living standards (i) terms of trade (i) Thailand (i) Thaler, Richard (i) theory (i) Theory of the Leisure Class, The (Veblen) (i) Theory of Monopolistic Competition (Chamberlain) (i) Thompson, William Hale ‘Big Bill’ (i) threat (i) time inconsistency (i), (ii) time intensity (i) Tocqueville, Alexis de (i) totalitarianism (i) trade (i), (ii), (iii) and dependency theory (i) free (i), (ii), (iii) trading permit, carbon (i) traditional and modern economies (i), (ii) transplant, organ (i) Treatise of the Canker of England’s Common Wealth, A (Malynes) (i) Tversky, Amos (i), (ii) underdeveloped countries (i) unemployment in Britain (i) and the government (i) and the Great Depression (i) and information economics (i) and Keynes (i) and market clearing (i) and recession (i) unions (i), (ii) United States of America and free trade (i) and growth of government (i) industrialisation (i) and Latin America (i) Microsoft (i) recession (i), (ii) and the Soviet Union (i) and Standard Oil (i) stock market (i) wealth in (i) women in the labour force (i) unpaid labour, and women (i) usury (i), (ii), (iii) utility (i), (ii), (iii), (iv) utopian thinkers (i), (ii) Vanderbilt, Cornelius (i), (ii) Veblen, Thorstein (i), (ii), (iii) velocity of circulation (i), (ii) Vickrey, William (i) wage, minimum (i) Walras, Léon (i) Waring, Marilyn (i) wealth (i) and Aristotle (i), (ii) and Christianity (i) Piketty on (i) and Plato (i) Smith on (i) Wealth of Nations, The (Smith) (i), (ii) welfare benefits (i), (ii), (iii), (iv) welfare economics (i) Who Pays for the Kids?

The Better Angels of Our Nature: Why Violence Has Declined
by Steven Pinker
Published 24 Sep 2012

(Of course, paying a self-imposed cost would be worthwhile only if the prize is especially valuable to him, or if he had reason to believe that he could prevail over his opponent if the contest escalated.) In the case of a war of attrition, one can imagine a leader who has a changing willingness to suffer a cost over time, increasing as the conflict proceeds and his resolve toughens. His motto would be: “We fight on so that our boys shall not have died in vain.” This mindset, known as loss aversion, the sunk-cost fallacy, and throwing good money after bad, is patently irrational, but it is surprisingly pervasive in human decision-making.65 People stay in an abusive marriage because of the years they have already put into it, or sit through a bad movie because they have already paid for the ticket, or try to reverse a gambling loss by doubling their next bet, or pour money into a boondoggle because they’ve already poured so much money into it.

…

Though psychologists don’t fully understand why people are suckers for sunk costs, a common explanation is that it signals a public commitment. The person is announcing: “When I make a decision, I’m not so weak, stupid, or indecisive that I can be easily talked out of it.” In a contest of resolve like an attrition game, loss aversion could serve as a costly and hence credible signal that the contestant is not about to concede, preempting his opponent’s strategy of outlasting him just one more round. I already mentioned some evidence from Richardson’s dataset which suggests that combatants do fight longer when a war is more lethal: small wars show a higher probability of coming to an end with each succeeding year than do large wars.66 The magnitude numbers in the Correlates of War Dataset also show signs of escalating commitment: wars that are longer in duration are not just costlier in fatalities; they are costlier than one would expect from their durations alone.67 If we pop back from the statistics of war to the conduct of actual wars, we can see the mechanism at work.

…

I already mentioned some evidence from Richardson’s dataset which suggests that combatants do fight longer when a war is more lethal: small wars show a higher probability of coming to an end with each succeeding year than do large wars.66 The magnitude numbers in the Correlates of War Dataset also show signs of escalating commitment: wars that are longer in duration are not just costlier in fatalities; they are costlier than one would expect from their durations alone.67 If we pop back from the statistics of war to the conduct of actual wars, we can see the mechanism at work. Many of the bloodiest wars in history owe their destructiveness to leaders on one or both sides pursuing a blatantly irrational loss-aversion strategy. Hitler fought the last months of World War II with a maniacal fury well past the point when defeat was all but certain, as did Japan. Lyndon Johnson’s repeated escalations of the Vietnam War inspired a protest song that has served as a summary of people’s understanding of that destructive war: “We were waist-deep in the Big Muddy; The big fool said to push on.”

Good Economics for Hard Times: Better Answers to Our Biggest Problems
by Abhijit V. Banerjee and Esther Duflo
Published 12 Nov 2019

The status quo, the outcome of letting things be, serves as a natural benchmark. Any loss relative to that benchmark is particularly painful. This concept was named loss aversion by Daniel Kahneman and Amos Tversky, two psychologists who have been incredibly influential in economics. (Kahneman won the Nobel Prize in economics in 2002 and Tversky would probably have as well, but for his untimely demise.) Since their original work, a vast literature has demonstrated the existence of loss aversion and its ability to explain many apparently strange behaviors. For example, most people pay a huge premium on their home insurance plans to get a low deductible.67 This allows them to avoid that painful moment when, after some accident has damaged their house, they have to pay a large sum out of pocket (the high deductible).

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By comparison, the fact that they may be paying a lot extra now (to get the policy with the low deductible) is painless because they will never discover if it was a mistake. The same logic also explains why gullible buyers often end up with outrageously expensive “extended warranties.” In essence, loss aversion makes us extremely worried about any risk, even small, that is a consequence of our active choice. Migration, unless everyone else is doing it, is one of these active choices, and a big one; it is easy to imagine many will be chary of trying. Finally, failure in migration is something people take personally.

Barometer of Fear: An Insider's Account of Rogue Trading and the Greatest Banking Scandal in History
by Alexis Stenfors
Published 14 May 2017

Even if the odds are exactly even, the frustration of losing a thousand dollars feels more powerful than the joy of winning a thousand. As a trader, I always felt more like a loser following a day when I had lost $10,000 or $100,000 than I felt like a ‘winner’ when I had made the same amount. The fact that losses loom larger than gains is called ‘loss aversion’. Nobody likes losing, of course, but a potential loss (or ‘negative’ reward) changes the way we approach risk taking. Another bias that complicates standard economics and finance theory is called ‘mental accounting’.3 In a nutshell, it refers to the way in which we often segregate different gambles into different accounts, and in so doing use very different criteria to assess how we utilise the various accounts.

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INDEX ABN Amro Bank, 59 Accenture, ‘rogue trading’ definition, 249 Accept, Breaker album, 110–11 ACI (Association Cambiste Internationale/Forex), 174, 181; ‘Dealing Certificates’, 216; Model Code, 227 actual funding rates, public knowledge, 97 Adoboli, Kweku, 250 Agius, Marcus, 77, 284 Almunia, Joaquín, 221 American Psycho, 239–40, 250 Aragon, 25 arbitarge, 31; opportunities, 27 Aros, 25 Asian financial crisis 1997, 260 ATM queues, image of, 109 average opinions, expectation of, 102 Bäckström, Urban, 117 Bailey, Andrew, 280 ‘banging the close’, 209 Bank of America, 2, 11, 153, 164, 188, 191, 223; Merrill Lynch rescue/takeover, 49, 67, 161–3, 193; rescue of, 10 Bank of England, 38, 55, 222; Exchange Joint Standing Committee, 179; inflation target, 39 Bank of Japan, 33, 81, 175–6 Bank of Tokyo-Mitsubishi, 153, 223 banking, competitive deregulation, 114; incentive structures literature, 252; post 2008 reforms, 254; risk taking essence, 281; staff ‘cost centres’, 95; see also, central banks; financial markets; money markets banks: access to money indicators, 96; cash hoarding, 45; change attempts post-scandals, 283; credit departments, 253; derivatives main users, 121; Eurodollar market made, 117, 125; fines, 236; LIBOR hiding, 105; LIBOR perceptions, 79; LIBOR quotes, 99; markets abuse, 14; profit maximizing, 80; public trust need, 284; reputational damage, 168; ‘special’ sector, 173; risk management systems, 46 Banque pour l’Europe du Nord, 113 Barclays Bank, 59, 98, 105, 153, 192–3, 210, 220, 223; Capital securities unit, 98; interest rate derivatives traders, 77; 2012 fines, 76; US dollar LIBOR trial, 139 Basel Accord 1988, 137; perverse effect, 138 BBA, banking lobby, 180, 183; BBAIRS creation, 118; big banks dominated, 107; -LIBOR trademark, 181 Bear Stearns, 49, 105, 272 ‘beating the market’, 267 Becker, Gary, 254 behavioural finance, study of, 196, 200–1, 255; ‘disruption effect’ concept, 258 benchmarks: financial instruments, 122; manipulation of, 14; manipulation criminalised, 282 Berlin Radio Show, 111 bid-offer spreads, 42–3, 62, 112, 132, 139, 146, 219, 223, 228–9; collusive practices, 223; FX market, 192; prices tight, 201; round figures, 218; secretly agreed, 220 BIS (Bank for International Settlements), 130 blame, individualised, 236; shifting, 68 Bloomberg, 50, 86, 88, 98, 151, 195, 283; indicative prices, 62 BNP Paribas, 193, 223; investment funds freeze redemptions, 50 Böll, Heinrich, 235 bonds selling, 21; trading desks, 215 bonuses, 164, 273; curbing partial solution, 280; stricter rules on, 280 ‘book’, traders, 26 Borough Market, London, 7, 101, 245 borrowing rates, low-balling, 99 bribes, forms of, 91 brokers, 143; best guess, 88; false information transmission, 89; role of, 141; traders pressure on, 90; -traders relation, 86–7, 89; use of, 132 Buffett, Warren, 15, 251 bulls/bears, early experiences formed, 31 Bush, George W., 45 buy and sell orders, 208 ‘call-outs’, 24; symptom assessing, 25 ‘Can do More’, 144 Canada: dollar, 33; Foreign Exchange Committee, 179 Canary Wharf, London, 6 Cantor Fitzgerald, London office, 264 capital controls, abolishment, 133 Carr Futures, World Trade Centre office, 264 cash markets, importance loss, 139 cash squeezes, year-end, 44 cash-settled derivatives; benchmark need, 122–3; made market, 133 cassettes, history of, 110–11 CDOs (collateralised debt obligations), 11 central bank, 151; -banks unique relationship, 173; foreign exchange interventions, 233; inflation rate target, 70; LIBOR key variable, 53, 151; LIBOR use, 152; money pumping, 50; power, 174; power overestimated, 49, 54; price stability goal, 51; repos, 175; tips, 176; transparency, 40, 166–7; unexpected interest rate moves, 41; weakening of, 114 Channel 4 News, 11 Chase Manhattan, 131 Chemical Bank (JPMorgan Chase), 30 CIBOR (Copenhagen Interbank Offered Rate), 28, 78–9 Citibank, 29, 30, 58, 101, 153, 155, 182, 188, 193, 220, 223; benchmark manipulation fine, 160; ‘Scandi’ desk, 33; Tokyo dealing room, 196 CME (Chicago Mercantile Exchange), 123, 1288; Eurodollar futures, 126 collateral types, central banks lowering, 50 competition law, UK and EU, 222 complex structured products, valuation inability, 50 compliance departments banks, 253; post-scandals increase, 283 Cooke, Mr Justice, 282 copycat behaviour, market making, 202–3 Cosmopolis, 250 counterparties, confirmations, 18 Countrywide, 49 CPI, Inflation index, 149 credit: default swap market, 99; officers, 95; rating agencies, 96; risk, 137; risk measure for, 55 Crédit Agricole Indosuez, 37, 44, 58–9, 134, 155 Crédit Suisse, 153, 193, 221, 223; First Boston, 127 creditworthiness: ‘image problem’, 51; judgments on, 225; signals, 98, 99 cross-currency basis swap, LIBOR-indexed, 62 CRSs, 129 Darin, Roger, 115 dealing relationships, informal reciprocal, 227 dealing rooms, internal monitoring increase, 283 deceptive behaviour, LIBOR banks, 105; quotes post-crisis pressure, 106 Del Missier, Jerry, 77 Den Danske bank, 178 derivatives, ‘abstract’, 123–4; benchmark use, 150; borrowing and lending idea, 138; concrete type, 121; growing market, 79; interest rates, 30; LIBOR-indexed, 28, 71, 80, 104, 129; new instruments, 18; textbook explanation, 119–20; trade tickets, 141’usefulness’ of, 131 derivatives market: benchmark need, 119; LIBOR importance, 37; Scandanavia, 27 Deutsche Bank, 153, 193, 223; LIBOR controls deceptions, 183; LIBOR fine, 83 Diamond, Bob, 77 Dillon Read, 49 ‘discount windows’ lowering, 50 ‘dishonesty’, 249 Donohue, Craig, 128 dot-com bubble, 104 downgrades, credit rating agencies, 96 Dresdner Bank, 17, 155, 197 Duffy, Terry, 128 Easton Ellis, Bret, American Psycho, 236 economic data releases, examples of, 38 efficient market hypothesis, 195, 200–1; unrealistic assumption, 196 ‘emerging markets’, trading desks, 37 ERM (European Exchange Rate Mechanism) crisis, 31–2 Ermotti, Sergio, 213 EURIBOR (Euro Interbank Offered Rate), 14, 76–8, 126, 130; derivatives, 145; new unpredictability, 62; pre-Euro, 148 euro, the: Eurozone crisis, 109; launch of, 36 eurocurrency market, 113; central bank weakening, 111; deregulated, 114; Eurodollars, see below; fast growth of, 112; LIBOR derivatives replaced, 134 Eurodollar market, 113, 133, 152; advantages, 112; banks made, 117, 125; contracts standard maturity dates, 126; financial deregulation prompt, 116; futures, see below; gradual reduction of, 136; history of, 111; LIBOR rate making, 117, 129; rapid growth of, 115 Eurodollar futures, 125, 128, 265; bets on, 146; rationale for, 129; success of, 127 Euromoney, 135 European Banking Federation, 180 European Central Bank (ECB), 50, 109, 145 European Commission, 221 Euroyen LIBOR futures contract, 127 ‘Events’ central bank meetings, 40 excessive lending, inflationary fears, 114 exclusivity, self-perception, 269 expectations, games of, 103; overpriced stock, 104 ‘expert judgments’, banks LIBOR quotes, 278 Fama, Eugene, 195 ‘fat fingers’ errors, 253 FBI, USA, 192–3 FCA (Financial Conduct Authority), 183–4, 188, 219, 282; Fair and Effective Markets Review, 222; prohibited individuals list, 285 fear, rumours of, 266 Federal Reserve, see USA FIBOR (Frankfurt Interbank Offered Rate), 19, 127 financial crisis, Asia 1997, 36 financial crisis 2007–8; decent culture erosion explanation, 279; familiar analysis of, 114; financial market illuminating, 275; -LIBOR implications, 52, 111; money markets freeze, 109 financial markets: cartels, 222; deregulation 115–16; instruments liquidity, 43; misconceptions, 236; self-regulated, 113, 171; see also, money markets Finers Stephens Innocent, 3 Finland: USSR collapse impact, 20; USSR Winter War, 65 ‘firm policy’, interbank spread choosing, 229 fixed exchange rates, sustainability, 32 flat switch, 92–5 flow traders, 143 Forex, 1995 exam, 223; reciprocity endorsed, 227 FRAs (forward rate agreements), 28, 75, 91, 129–30; growth of, 148 Friday dress policy, 135 FSA (Financial Services Authority), UK, 1–2, 67, 77, 98, 105, 124, 163, 180, 243; prohibition orders, 4; suspension, 5 ‘Full Amount’ call, weakness indicator, 143 funding costs:, averages, 104; LIBOR signalling, 97; -market liquidity relation, 44 futures contracts: agricultural, 120; cash-settled, 125; transparent exchanges, 63 FX (foreign exchange) market, 172, 196, 245; bank price influence, 212; big banks domination/market concentration, 193, 195, 210, 212, 223, 234; ‘clear the decks’, 210; ‘community’, 190; ethical problem, 213; global banks 2014 fines, 188; interbank spread survey, 228; interest rate markets joining, 31; Japanese banks borrowing, 33; London ‘banging the close’; 209; non-public information grey zone, 224; order books, 7; reciprocity, 224; scale of significance, 126, 192, 232; spot market desk, 214, 217; standardised norms, 194; swap market, see below; ‘The Cartel’, 220; traders, see below; turnover scale, 212 FX swap market, 134, 137, 145, 146; interest rate speculation, 133; Japanese traders, 34; lower credit risk, 137, 144; 9/11 trading, 265; spot-prices, 31, 227 FX traders, 191; club mentality, 269; desks, 30; respect among, 269; secret code us, 219; ‘techniques’, 204; varied backgrounds, 216 Gelboim, Ellen, 153 gentlemen’s agreements, 141 ‘getting married to your position’, trading attitude, 257–8 global merchandise exports, growth, 112 Goldman Sachs, 49, 140, 193, 223, 272 Goodhart, Charles, 173 Greece, 2015 ATM queues, 109 Greenspan, Alan, 15, 51, 173–4 Greenwald, Bruce, 225 guilt, feelings of, 78, 169, 243, 259 Häyhä, Simo (‘White Death’), 65 ‘Hambros’, 194 Harley, Dean, 231 Hayes, Tom, 8, 13, 72, 92–3, 115, 238; prison sentence, 12 HBOS, 183 headhunters, 160 HELIBOR (Helsinki Interbank Offered Rate), 28 Hester, Stephen, 284 Hintz, Brad, 10 HSBC, bank, 27, 153, 155, 188, 193, 208, 213, 223; FCA fine, 219; FX trading, 116, 187; Group Management Training College, 187; Stockholm, 31 Hull, John, 150 Hunger Games series, 255 Hyogo Bank default, 33 ICAP, 86, 101, 175; LIBOR fine, 85 ICMA (International Capital Market Association), 174 IKB bank, 50; rollover problems, 49 illiquidity, temporary, 43 Indonesia, financial crisis, 36 Industrial Bank of Japan, 34 ‘industry’, financial, 154–5 information: LIBOR delays problem, 49, 54; big banks superior, 210 instincts, 226 interbank money market, 38; central bank influence, 39; efficiency estimate change, 109; lending fall, 111; LIBOR, see below interest rate(s): benchmarks, 14; central banks forecasts, 166; changes impact of, 38; derivatives, 17, 174; hedging, 128; movement, 42; short-term, 28, 133; swaps sizes, 142 International Code of Conduct and Practice for the, 216 International Monetary Market (IMM), 72; contracts conventions, 126; LIBOR fixings, 73–4 investment banks, risk takers, 272 Ireland, Financial Regulator, 4, 168, 281 IRS, interest rate swap, 129–30; short-term, 140 ISDA (International Swaps and Derivatives Association), 174; fix, 14 Japan: bank sector/system: crisis, 47, 81; dollars difficulty period, 34; fear premium, 36; Financial Services Agency, 101; FX market concentration, 193; FX ‘premium’, 35–6; safe perception change, 33; unique derivatives market, 36; yen market, 8, 45 JP Morgan/JP Morgan Chase, 92, 105, 153, 178, 188, 192–3, 220–3 Kahneman, Daniel, 255 Kerviel, Jérôme, 250 Keynes, J.M., General Theory of Employment, 102 Kipling, Rudyard, 127 KLIBOR (Kuala Lumpur), 37 Knight, Angela, 107 Lapavitsas, Costas, 6–7 layering, 204 Leeson, Nick, 250 ‘legacy issues’, 236 Lehman Brothers, 2, 10, 48–9, 59, 105, 162, 272; bankruptcy filing, 160; collapse of aftermath, 96 Lewis, Ken, 164 LIBOR, 19, 28, 76–7, 104, 127, 130, 147, 209, 234, 265; anti-competitive process, 186; banking lobby regulated, 180–1; ‘barometer of fear’, 96; benchmark significance, 192, 225; central banks perfection assumption, 49; controls deception, 184; crisis-induced ‘stickiness’, 106; crucial price, 13; daily individual quotes, 97; derivatives, see below; ‘Eurodollar futures’ origin, 126; FCA regulated, 282; ‘fear’ index, 15; fixing panels, see below; future direction of, 38; inaccuracy possibilities, 74; interbank money market gauge, 39; jurisdiction issue, 115; manipulation, 7, 12, 14, 78; manipulation impossibility assumption, 81; market-determined perception, 88, 149; mechanism, 104; minute change importance, 73; new unpredictability, 62; 1980s invention, 111; objective process ‘evidence’, 148; perception of, 119; players as referees, 80; post 2007 interest, 53; pre-2013 unregulated, 118; predicting difficulty, 70; regulatory oversight lack, 179; retail credit impact, 277; sanctioned secrecy, 181–2; savings and borrowings dominance, 107; scandal breaking, 81; state measure use, 151; three-months, 71; ‘too big to fail’, 279; use of limited post-scandal, 278 LIBOR derivatives market, 8, 45, 137–8, 232; autonomous development of, 111; banks made, 125; ‘community’, 190; -FX connected, 189; imaginary money market, 148; increased abstraction of, 144–6 LIBOR panel banks, 74–5, 79, 98, 118, 172, 282; -LIBOR implications, 52 big banks dominated, 173, 179–80; fixing process, 75; membership criteria, 184–5; punishment idea, 108; post-scandal membership, 186 LIBOR scandal, 77, 152, 167, 245; correctness attempts, 277; post- definition unchanged, 278; breaking of, 81; Wall Street Journal on, 238 LIBOR-OIS spread(s), 51, 54–5, 99, 151 LIFFE, 126–7 liquidity: and credit crunch 2008, 2; credit issues, 45; informal norms need, 284; provision ‘duty’ 229; risk, 42–3, 55, 70 Lloyds Bank, 153, 183; LIBOR fine, 83 long/short positions, 26 Lukes, Steven, 186 makers, price, 24 Malaysia, financial crisis, 36 Mankell, Henning, 235 ‘marked to market’ trading books, 62 market, the financial: ‘colour’ 202; ‘conventions’, 228–33; ‘courtroom’, 171; interbank spread choosing ‘image’, 229; liquidity risk, 42–3; making, see below; perfections of, 15; relationships dependent, 225–6; risks limits management failure, 281 market makers/making, 24, 72, 117, 201, 206, 217, 226–7, 257; ‘ability’, 185; cash-settled derivatives, 133; failure to manage, 281; NIBOR IRS, 132; profession of, 200; two-way price quoting, 228; visibility of, 202 Martin Brokers, 85 Mathew, Jonathan, 139 McAdams, Richard, 231 McDermott, Tracey, 282 Meitan Tradition, 100, 175 Merita Bank, 56 Merrill Lynch, 2–3, 8–9, 12, 46, 49, 59–60, 62, 64, 69, 92–3, 96, 140, 153, 155, 160–1, 164, 188, 272, 285; Bank of America takeover, 67; bonuses, 10, 162–3; financial centre, 48; International Bank Limited Dublin, 4; mismarking, 68; risk taking encouraged, 281; silence rule, 242 Midland Montagu (Midland Bank Stockholm Branch), 20, 22–3, 27, 29; Stockholm, 22, 29 ‘Millenium bug’ fears, LIBOR impact, 44 mismarking, 9 mistakes, fear of, 26 Mollenkamp, Carrick, 98 ‘monetary transmission mechanism’, 39 money market(s): decentralised, 224; freeze, 110; international basis, 112; ‘risk premium’, 42; stable illusion-making, 106; -state link, 224 Moody’s, 96 morals, 66; morality, 69 Morgan Stanley, 49, 193, 223, 272 mortgage bonds, 21 NASDAQ stock exchange, transparency, 220 New York 2001 attacks, 263 New York Times, 4, 9, 11, 163, 241, 243 NIBOR (Norwegian Interbank Offered Rate), 28, 72, 130–1; fixing dates, 76; inaccurate fixing, 74; IRS market, 132; new unpredictability, 62; one month IRS market, 136 nicknames, use of, 25–6 Nordbanken, nationalised, 27 Nordic bank branches, 30 Norges Bank, NIBOR use, 152 Norinchukin Bank, 153 Northern Rock, Newcastle queues, 109 Norway, banking system, 131 ‘objective’ fact, LIBOR, 149 ‘off-balance-sheet’, trading, 137–8 official interest rate, predicting, 38 OIS (overnight index swap), 51; see also LIBOR-OIS one month IRS market, 136 OPEC (Organization of the Petroleum Exporting Countries), US dollar surpluses, 113 options desk, FX, 214 ‘over-the-counter’ trades, 63 derivatives, 129, 134; interest rate options, 130; markets, 227 Philippines, financial crisis, 37 Philips, cassette launch, 111 PIBOR (Paris Interbank Offered Rate), 19, 127 post scandals, reforms, 282 price(s), as interactions, 200; brokers indications role, 87; ‘resolution hypothesis’, 218 primary dealers, 175, 178 privacy, individual rights to, 167 Rabobank, LIBOR fine, 83, 153, 282 RBC, bank, 223 RBS, bank, 92, 153, 185, 188, 192, 220–1, 223, 284; LIBOR scandal fine, 83 reciprocity: -and trust, 226, 284; informal agreements, 228 regret, fear of, 258 regulatory arbitrage: Eurodollar market prompting, 118; platform for, 114 ‘reputation’, 185 respect, among traders, 267 Reuters, 19, 79, 151; Dealing, 41, 195, 260; Dealing 2000–2, 29, 34, 194; indicative prices, 62; screen price, 53 risk, 135; buzz of, 261–2; limits breaking, 274; ‘loss aversion’, 255; managers, 253; organizational limits, 250; pressures for, 63 risk taking: addictive, 262; enjoyment of, 260; fear control, 263; increase, 73; individualistic, 262; reward anticipation, 254; reward interpretation, 259; supervision need, 253 risk takers, 270; respect among, 268–9 Robert, Alain, 260 ‘rogue traders’, 1, 237; ‘bad apples’ narrative, 237, 240, 246, 279; fame, 252; fascination with, 246; losses, 259; ranking list, 250; risk list, 251; scandals, 258; stigma, 247 rogue trading, 274; definitions, 249; labelling, 248; risk link, 250 Royal Bank of Canada, 153 RP Martins, 153 rules of the game, loyalty to, 25 ‘run-throughs’, 87–9, 226–7 Russia, financial crisis, 36 Ryan, Ian, 3, 9, 68 Sanford C.

The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing
by Michael J. Mauboussin
Published 14 Jul 2012

You have a gain if the stock rises above $30 and a loss if it drops below that price. Rather than viewing the value of the stock in the context of a larger portfolio, the natural tendency is to consider each stock relative to its reference point. Loss aversion is another feature of prospect theory. We suffer roughly two times more from a loss than we enjoy a gain of the same size. The combination of the reference point and loss aversion leads investors to hold on to losing stocks and sell winners, because it is painful to take losses.34 Because good decisions can have bad outcomes, not everyone has a temperament that is well suited to making decisions about activities that involve luck.

The End of Ownership: Personal Property in the Digital Economy
by Aaron Perzanowski and Jason Schultz
Published 4 Nov 2016

When presented the opportunity to sell or trade their mugs to other participants, mug owners demanded nearly twice as much compensation as nonowners were willing to pay.34 Subjectively, they valued the mugs they owned well above the market rate. What explains these vastly different assessments of the value of an otherwise ordinary mug? Some have suggested that the endowment effect is the result of loss aversion—the idea that people are more motivated by the fear or regret associated with loss of an item than the enjoyment of gaining it. But more recent research shows that we place greater value on the things we own because we own them.35 The association between an item and its owner means that we value things we own far more than things we simply use.

…

Taylor Swift, “For Taylor Swift, the Future of Music Is a Love Story,” Wall Street Journal, July 7, 2014, http://www.wsj.com/articles/for-taylor-swift-the-future-of-music-is-a-love-story-1404763219, accessed June 15, 2015. 34. Daniel Kahneman, Jack L. Knetsch, and Richard H. Thaler, “Experimental Tests of the Endowment Effect and the Coase Theorem,” Journal of Political Economy 98, no. 6 (1990): 1325–1348. 35. Carey K. Morewedge et al., “Bad Riddance or Good Rubbish? Ownership and Not Loss Aversion Causes the Endowment Effect,” Journal of Experimental Psychology 45, no. 4 (July 2009): 947–951. 36. Yannick Ferreira De Sousa and Alistair Munro, “Truck, Barter, and Exchange versus the Endowment Effect: Virtual Field Experiments in an Online Game Environment,” Journal of Economic Psychology 33, no. 3 (June 2012): 482–493.

The Price of Everything: And the Hidden Logic of Value
by Eduardo Porter
Published 4 Jan 2011

In the 1980s a new discipline called Prospect Theory—also known as behavioral economics—deployed the tools of psychology to analyze economic behavior. It found all sorts of peculiar behaviors that don’t fit economics’ standard understanding of what makes us happy. For instance, losing something reduces our happiness more than winning the same thing increases it—a quirk known as loss aversion. We are unable to distinguish between choices that have slightly different odds of making us happy. We extrapolate from a few experiences to arrive at broad, mostly wrong conclusions. We herd, imitating successful behaviors around us. Still, it remains generally true that we pursue what we think makes us happy—and though some of our choices may not make us happy, some will.

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electricity elephant-seal cows Elías, Julio Jorge e-mail, spam and Emergency Highway Energy Conservation Act (1974) Empire State Stem Cell Board encyclopedias, free energy engagement rings engineers England environment see also climate change; pollution Environmental Protection Agency (EPA) Epson ESP printers Essay on the Principle of Population, An (Malthus) Ethiopia Ethnographic Atlas (Murdock) eToys Eurobarometer surveys Europe Catholic Church in decline of polygamy in happiness in lack of sprawl in U.S. compared with work hours in see also Western Europe European Climate Exchange European Union evangelical Christianity executive pay ExxonMobil faith benefits of cheap cost of Fallaci, Oriana families changes to culture and income of of 9/11 victims size of Fanning, Shawn (the Napster) Federal Communications Commission Federal Food, Drug, and Cosmetic Act, Delaney Clause to (1958) Federal Reserve Federal Trade Commission (FTC) “Feeding the Illusion of Growth and Happiness” (Easterlin) Feinberg, Kenneth fertility decline in female file sharing film financial crises financial services fines fire departments fishing floors Florence foeticide food culture and faith and preparation of price increases for surpluses of Food and Agriculture Organization Food Quality Protection Act (1996) Ford Ford, Henry Foreign Corrupt Practices Act Fourier, Charles France happiness in work hours in Frank, Robert Free (Anderson) Freedom Communications free lunch, use of term free rider problem free things broadcast TV and movies music and Napstering the world and profiting from ideas freeware Freud, Sigmund fuel see also gas Fundamentalist Church of Jesus Christ of Latter-day Saints future ethics of mispricing nature and price of Gabaix, Xavier Gallup polls Gandhi garbage gas price of General Motors (GM) General Social Survey General Theory of Employment, Interest and Money, The (Keynes) genetics, genes Germany happiness in Germany, Nazi Gershom ben Judah Ghosts I-IV (album) gifts Glass-Steagall Act (1933) GlaxoSmithKline globalization global warming Goa God Goldin, Claudia goods Google Google News Gore, Al Gorton, Mark government hostility toward intervention of resource allocation of Great Britain bubbles in gas prices in happiness in politics in Great Depression Greece, ancient green revolution (1960s and 1970s) Greenspan, Alan gross national happiness (GNH) index Haiti Hammurabi Hanna, Mark happiness faith and genetics and life-cycle curve of loss aversion and money and problems with defining of right-left gap in U.S. trade-off and Hare Krishna Society Harvard University Haryana health health care health insurance Health Ministry, New Zealand Healthway Heinrich, Armin Hindus, Hinduism HIV homeland security, U.S. Homeland Security Department, U.S.

Time Paradox
by Philip G. Zimbardo and John Boyd
Published 1 Jan 2008

Kahneman, “The Framing of Decisions and the Psychology of Choice,” Science 211: 453–58 (1981); and A. Tversky and D. Kahneman, “Loss Aversion in Riskless Choice: A Reference-Dependent Model,” Quarterly Journal of Economics 106: 1039–61 (1991). 35. D. Kahneman, J. L. Knetsch, and R. H. Thaler, “Experimental Tests of the Endowment Effect and the Coase Theorem,” Journal of Political Economy 98: 1325–48 (1990); and D. Kahneman, J. Kentsch, and D. Thaler, “The Endowment Effect, Loss Aversion, and Status Quo Bias,” Journal of Economic Perspectives 5: 193–206 (1991). 36. L. van Boven, D. Dunning, and G. F. Loewenstein, “Egocentric Empathy Gaps Between Owners and Buyers: Misperceptions of the Endowment Effect,” Journal of Personality and Social Psychology 79: 66–76 (2000); and Z.

Nervous States: Democracy and the Decline of Reason
by William Davies
Published 26 Feb 2019

In more prosaic contexts, this is an insight that has been confirmed by behavioral economists. Experiments show that, all else being equal, people place greater value on not losing that which they already have, than on gaining something of equivalent value. As these behavioral economists would say, we are fundamentally “loss-averse” creatures. Where victory is enjoyed and then quickly taken for granted, the experience of loss shapes our identity, forging a melancholic sense of nostalgia. Paradoxically, this melancholic sense of having lost can have its own mobilizing effect, if it can be triggered in the right way. Clausewitz wondered whether “through the loss of a great battle, forces are not perhaps roused into existence, which otherwise would never have come to life.”23 The pain of defeat produces a feeling of victimhood through which national cohesion starts to emerge.

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Kennedy International Airport, New York, x, xiii, 41 Johns Hopkins University, 176 Jones, Alexander, 131 Kant, Immanuel, 128, 130 Kemelmacher-Shlizerman, Ira, 188 Kennedy Jr., Robert, 23 Kepler, Johannes, 35 Keynes, John Maynard, 165 King Jr., Martin Luther, 21, 224 knowledge economy, 84, 85, 88, 151–2, 217 known knowns, 132, 138 Koch, Charles and David, 154, 164, 174 Korean War (1950–53), 178 Kraepelin, Emil, 139 Kurzweil, Ray, 183–4 Labour Party, 5, 6, 65, 80, 81, 221 Lagarde, Christine, 64 Le Bon, Gustave, 8–12, 13, 15, 16, 20, 24, 25, 38 Le Pen, Marine, 27, 79, 87, 92, 101–2 Leadbeater, Charles, 84 Leeds, West Yorkshire, 85 Leicester, Leicestershire, 85 Leviathan (Hobbes), 34, 39, 45 liberal elites, 20, 58, 88, 89, 161 libertarianism, 15, 151, 154, 158, 164, 173, 196, 209, 226 Liberty Fund, 158 Libya, 143 lie-detection technology, 136 life expectancy, 62, 68–71, 72, 92, 100–101, 115, 224 Lindemann, Frederick Alexander, 1st Viscount Cherwell, 138 Lloyds Bank, 29 London, England bills of mortality, 68–71, 75, 79–80, 81, 89, 127 Blitz (1940–41), 119, 143, 180 EU referendum (2016), 85 Great Fire (1666), 67 Grenfell Tower fire (2017), 10 and gross domestic product (GDP), 77, 78 housing crisis, 84 insurance sector, 59 knowledge economy, 84 life expectancy, 100 newspapers, early, 48 Oxford Circus terror scare (2017), ix–x, xiii, 41 plagues, 67–71, 75, 79–80, 81, 89, 127 Unite for Europe march (2017), 23 London School of Economics (LSE), 160 loss aversion, 145 Louis XIV, King of France, 73, 127 Louisiana, United States, 151, 221 Ludwig von Mises Institute, 154 MacLean, Nancy, 158 Macron, Emmanuel, 33 mainstream media, 197 “Make America Great Again,” 76, 145 Manchester, England, 85 Mann, Geoff, 214 maps, 182 March For Our Lives (2018), 21 March for Science (2017), 23–5, 27, 28, 210, 211 marketing, 14, 139–41, 143, 148, 169 Mars, 175, 226 Marxism, 163 Massachusetts Institute of Technology (MIT), 179 Mayer, Jane, 158 McCarthy, Joseph, 137 McGill Pain Questionnaire, 104 McKibben, William “Bill,” 213 Megaface, 188–9 memes, 15, 194 Menger, Carl, 154 mental illness, 103, 107–17, 139 mercenaries, 126 Mercer, Robert, 174, 175 Mexico, 145 Million-Man March (1995), 4 mind-reading technology, 136 see also telepathy Mirowski, Philip, 158 von Mises, Ludwig, 154–63, 166, 172, 173 Missing Migrants Project, 225 mobilization, 5, 7, 126–31 and Corbyn, 81 and elections, 81, 124 and experts, 27–8 and Internet, 15 and Le Bon’s crowd psychology, 11, 12, 16, 20 and loss, 145 and Napoleonic Wars, xv, 127–30, 141, 144 and Occupy movement, 5 and populism, 16, 22, 60 and violence, opposition to, 21 Moniteur Universel, Le, 142 monopoly on violence, 42 Mont Pelerin Society, 163, 164 moral emotion, 21 morphine, 105 multiculturalism, 84 Murs, Oliver “Olly,” ix Musk, Elon, 175, 176, 178, 183, 226 Nanchang, Jiangxi, 13 Napoleonic Wars (1803–15), 126–30 chappe system, 129, 182 and conscription, 87, 126–7, 129 and disruption, 170–71, 173, 174, 175, 226 and great leader ideal, 146–8 and intelligence, 134 and mobilization, xv, 126–30, 141, 144 and nationalism, 87, 128, 129, 144, 183, 211 and propaganda, 142 Russia, invasion of (1812), 128, 133 Spain, invasion of (1808), 128 National Aeronautics and Space Administration (NASA), 23, 175 National Audit Office (NAO), 29–30 national citizenship, 71 National Defense Research Committee, 180 National Health Service (NHS), 30, 93 National Park Service, 4 National Security Agency (NSA), 152 national sovereignty, 34, 53 nationalism, 87, 141, 210–12 and conservatism, 144 and disempowerment, 118–19 and elites, 22–3, 60–61, 145 ethnic, 15 and health, 92, 211–12, 224 and imagined communities, 87 and inequality, 78 and loss, 145 and markets, 167 and promises, 221 and resentment, 145, 197, 198 and war, 7, 20–21, 118–19, 143–6, 210–11 nativism, 61 natural philosophy, 35–6 nature, 86 see also environment Nazi Germany (1933–45), 137, 138, 154 Netherlands, 48, 56, 129 Neurable, 176 neural networking, 216 Neuralink, 176 neurasthenia, 139 Neurath, Otto, 153–4, 157, 160 neurochemistry, 108, 111, 112 neuroimaging, 176–8, 181 Nevada, United States, 194 new atheism, 209 New Orleans, Louisiana, 151 New Right, 164 New York, United States and climate change, 205 and gross domestic product (GDP), 78 housing crisis, 84 JFK Airport terror scare (2016), x, xiii, 41 knowledge economy, 84 September 11 attacks (2001), 17, 18 New York Times, 3, 27, 85 newspapers, 48, 71 Newton, Isaac, 35 Nietzsche, Friedrich, 217 Nixon, Robert, 206 no-platforming, 22, 208 Nobel Prize, 158–9 non-combatants, 43, 143, 204 non-violence, 224 North Atlantic Treaty Organization (NATO), 123, 145, 214 North Carolina, United States, 84 Northern Ireland, 43, 85 Northern League, 61 Northern Rock, 29 Norwich, Norfolk, 85 nostalgia, xiv, 143, 145, 210, 223 “Not in my name,” 27 nuclear weapons, 132, 135, 137, 180, 183, 192, 196, 204 nudge techniques, 13 Obama, Barack, 3, 24, 76, 77, 79, 158, 172 Obamacare, 172 objectivity, xiv, 13, 75, 136, 223 and crowd-based politics, 5, 7, 24–5 and death, 94 and Descartes, 37 and experts, trust in, 28, 32, 33, 51, 53, 64, 86, 89 and Hayek, 163, 164, 170 and markets, 169, 170 and photography, 8 and Scientific Revolution, 48, 49 and statistics, 72, 74, 75, 82, 88 and telepathic communication, 179 and war, 58, 125, 134, 135, 136, 146 Occupy movement, 5, 10, 24, 61 Oedipus complex, 109 Office for National Statistics, 63, 133 Ohio, United States, 116 oil crisis (1973), 166 “On Computable Numbers” (Turing), 181 On War (Clausewitz), 130 Open Society and Its Enemies, The (Popper), 171 opiates, 105, 116, 172–3 opinion polling, 65, 80–81, 191 Orbán, Viktor, 87, 146 Organisation for Economic Co-operation and Development (OECD), 72 Oxford, Oxfordshire, 85 Oxford Circus terror scare (2017), ix–x, xiii, 41 Oxford University, 56, 151 OxyContin, 105, 116 pacifism, 8, 20, 44, 151 pain, 102–19, 172–3, 224 see also chronic pain painkillers, 104, 105, 116, 172–3 Palantir, 151, 152, 175, 190 parabiosis, 149 Paris climate accord (2015), 205, 207 Paris Commune (1871), 8 Parkland attack (2018), 21 Patriot Act (2001), 137 Paul, Ronald, 154 PayPal, 149 Peace of Westphalia (1648), 34, 53 peer reviewing, 48, 139, 195, 208 penicillin, 94 Pentagon, 130, 132, 135, 136, 214, 216 pesticides, 205 Petty, William, 55–9, 67, 73, 85, 167 pharmacology, 142 Pielke Jr., Roger, 24, 25 Piketty, Thomas, 74 Pinker, Stephen, 207 plagues, 56, 67–71, 75, 79–80, 81, 89, 95 pleasure principle, 70, 109, 110, 224 pneumonia, 37, 67 Podemos, 5, 202 Poland, 20, 34, 60 Polanyi, Michael, 163 political anatomy, 57 Political Arithmetick (Petty), 58, 59 political correctness, 20, 27, 145 Popper, Karl, 163, 171 populism xvii, 211–12, 214, 220, 225–6 and central banks, 33 and crowd-based politics, 12 and democracy, 202 and elites/experts, 26, 33, 50, 152, 197, 210, 215 and empathy, 118 and health, 99, 101–2, 224–5 and immediate action, 216 in Kansas (1880s), 220 and markets, 167 and private companies, 174 and promises, 221 and resentment, 145 and statistics, 90 and unemployment, 88 and war, 148, 212 Porter, Michael, 84 post-traumatic stress disorder (PTSD), 111–14, 117, 209 post-truth, 167, 224 Potsdam Conference (1945), 138 power vs. violence, 19, 219 predictive policing, 151 presidential election, US (2016), xiv and climate change, 214 and data, 190 and education, 85 and free trade, 79 and health, 92, 99 and immigration, 79, 145 and inequality, 76–7 and Internet, 190, 197, 199 “Make America Great Again,” 76, 145 and opinion polling, 65, 80 and promises, 221 and relative deprivation, 88 and Russia, 199 and statistics, 63 and Yellen, 33 prisoners of war, 43 promises, 25, 31, 39–42, 45–7, 51, 52, 217–18, 221–2 Propaganda (Bernays), 14–15 propaganda, 8, 14–16, 83, 124–5, 141, 142, 143 property rights, 158, 167 Protestantism, 34, 35, 45, 215 Prussia (1525–1947), 8, 127–30, 133–4, 135, 142 psychiatry, 107, 139 psychoanalysis, 107, 139 Psychology of Crowds, The (Le Bon), 9–12, 13, 15, 16, 20, 24, 25 psychosomatic, 103 public-spending cuts, 100–101 punishment, 90, 92–3, 94, 95, 108 Purdue, 105 Putin, Vladimir, 145, 183 al-Qaeda, 136 quality of life, 74, 104 quantitative easing, 31–2, 222 quants, 190 radical statistics, 74 RAND Corporation, 183 RBS, 29 Reagan, Ronald, 15, 77, 154, 160, 163, 166 real-time knowledge, xvi, 112, 131, 134, 153, 154, 165–70 Reason Foundation, 158 Red Vienna, 154, 155 Rees-Mogg, Jacob, 33, 61 refugee crisis (2015–), 60, 225 relative deprivation, 88 representative democracy, 7, 12, 14–15, 25–8, 61, 202 Republican Party, 77, 79, 85, 154, 160, 163, 166, 172 research and development (R&D), 133 Research Triangle, North Carolina, 84 resentment, 5, 226 of elites/experts, 32, 52, 61, 86, 88–9, 161, 186, 201 and nationalism/populism, 5, 144–6, 148, 197, 198 and pain, 94 Ridley, Matt, 209 right to remain silent, 44 Road to Serfdom, The (Hayek), 160, 166 Robinson, Tommy, ix Roosevelt, Franklin Delano, 52 Royal Exchange, 67 Royal Society, 48–52, 56, 68, 86, 133, 137, 186, 208, 218 Rumsfeld, Donald, 132 Russian Empire (1721–1917), 128, 133 Russian Federation (1991–) and artificial intelligence, 183 Gerasimov Doctrine, 43, 123, 125, 126 and information war, 196 life expectancy, 100, 115 and national humiliation, 145 Skripal poisoning (2018), 43 and social media, 15, 18, 199 troll farms, 199 Russian Revolution (1917), 155 Russian SFSR (1917–91), 132, 133, 135–8, 155, 177, 180, 182–3 safe spaces, 22, 208 Sands, Robert “Bobby,” 43 Saxony, 90 scarlet fever, 67 Scarry, Elaine, 102–3 scenting, 135, 180 Schneier, Bruce, 185 Schumpeter, Joseph, 156–7, 162 Scientific Revolution, 48–52, 62, 66, 95, 204, 207, 218 scientist, coining of term, 133 SCL, 175 Scotland, 64, 85, 172 search engines, xvi Second World War, see World War II securitization of loans, 218 seismology, 135 self-employment, 82 self-esteem, 88–90, 175, 212 self-harm, 44, 114–15, 117, 146, 225 self-help, 107 self-interest, 26, 41, 44, 61, 114, 141, 146 Semi-Automatic Ground Environment (SAGE), 180, 182, 200 sentiment analysis, xiii, 12–13, 140, 188 September 11 attacks (2001), 17, 18 shell shock, 109–10 Shrecker, Ted, 226 Silicon Fen, Cambridgeshire, 84 Silicon Valley, California, xvi, 219 and data, 55, 151, 185–93, 199–201 and disruption, 149–51, 175, 226 and entrepreneurship, 149–51 and fascism, 203 and immortality, 149, 183–4, 224, 226 and monopolies, 174, 220 and singularity, 183–4 and telepathy, 176–8, 181, 185, 186, 221 and weaponization, 18, 219 singularity, 184 Siri, 187 Skripal poisoning (2018), 43 slavery, 59, 224 smallpox, 67 smart cities, 190, 199 smartphone addiction, 112, 186–7 snowflakes, 22, 113 social indicators, 74 social justice warriors (SJWs), 131 social media and crowd psychology, 6 emotional artificial intelligence, 12–13, 140–41 and engagement, 7 filter bubbles, 66 and propaganda, 15, 18, 81, 124 and PTSD, 113 and sentiment analysis, 12 trolls, 18, 20–22, 27, 40, 123, 146, 148, 194–8, 199, 209 weaponization of, 18, 19, 22, 194–5 socialism, 8, 20, 154–6, 158, 160 calculation debate, 154–6, 158, 160 Socialism (Mises), 160 Society for Freedom in Science, 163 South Africa, 103 sovereignty, 34, 53 Soviet Russia (1917–91), 132, 133, 135–8, 177, 180, 182–3 Spain, 5, 34, 84, 128, 202 speed of knowledge, xvi, 112, 124, 131, 134, 136, 153, 154, 165–70 Spicer, Sean, 3, 5 spy planes, 136, 152 Stalin, Joseph, 138 Stanford University, 179 statactivism, 74 statistics, 62–91, 161, 186 status, 88–90 Stoermer, Eugene, 206 strong man leaders, 16 suicide, 100, 101, 115 suicide bombing, 44, 146 superbugs, 205 surveillance, 185–93, 219 Sweden, 34 Switzerland, 164 Sydenham, Thomas, 96 Syriza, 5 tacit knowledge, 162 talking cure, 107 taxation, 158 Tea Party, 32, 50, 61, 221 technocracy, 53–8, 59, 60, 61, 78, 87, 89, 90, 211 teenage girls, 113, 114 telepathy, 39, 176–9, 181, 185, 186 terrorism, 17–18, 151, 185 Charlottesville attack (2017), 20 emergency powers, 42 JFK Airport terror scare (2016), x, xiii, 41 Oxford Circus terror scare (2017), ix–x, xiii, 41 September 11 attacks (2001), 17, 18 suicide bombing, 44, 146 vehicle-ramming attacks, 17 war on terror, 131, 136, 196 Thames Valley, England, 85 Thatcher, Margaret, 154, 160, 163, 166 Thiel, Peter, 26, 149–51, 153, 156, 174, 190 Thirty Years War (1618–48), 34, 45, 53, 126 Tokyo, Japan, x torture, 92–3 total wars, 129, 142–3 Treaty of Westphalia (1648), 34, 53 trends, xvi, 168 trigger warnings, 22, 113 trolls, 18, 20–22, 27, 40, 123, 146, 148, 194–8, 199, 209 Trump, Donald, xiv and Bannon, 21, 60–61 and climate change, 207 and education, 85 election campaign (2016), see under presidential election, US and free trade, 79 and health, 92, 99 and immigration, 145 inauguration (2017), 3–5, 6, 9, 10 and inequality, 76–7 “Make America Great Again,” 76, 145 and March for Science (2017), 23, 24, 210 and media, 27 and opinion polling, 65, 80 and Paris climate accord, 207 and promises, 221 and relative deprivation, 88 and statistics, 63 and Yellen, 33 Tsipras, Alexis, 5 Turing, Alan, 181, 183 Twitter and Corbyn’s rallies, 6 and JFK Airport terror scare (2016), x and Oxford Circus terror scare (2017), ix–x and Russia, 18 and sentiment analysis, 188 and trends, xvi and trolls, 194, 195 Uber, 49, 185, 186, 187, 188, 191, 192 UK Independence Party, 65, 92, 202 underemployment, 82 unemployment, 61, 62, 72, 78, 81–3, 87, 88, 203 United Kingdom austerity, 100 Bank of England, 32, 33, 64 Blitz (1940–41), 119, 143, 180 Brexit (2016–), see under Brexit Cameron government (2010–16), 33, 73, 100 Center for Policy Studies, 164 Civil Service, 33 climate-gate (2009), 195 Corbyn’s rallies, 5, 6 Dunkirk evacuation (1940), 119 education, 85 financial crisis (2007–9), 29–32, 100 first past the post, 13 general election (2015), 80, 81 general election (2017), 6, 65, 80, 81, 221 Grenfell Tower fire (2017), 10 gross domestic product (GDP), 77, 79 immigration, 63, 65 Irish hunger strike (1981), 43 life expectancy, 100 National Audit Office (NAO), 29 National Health Service (NHS), 30, 93 Office for National Statistics, 63, 133 and opiates, 105 Oxford Circus terror scare (2017), ix–x, xiii, 41 and pain, 102, 105 Palantir, 151 Potsdam Conference (1945), 138 quantitative easing, 31–2 Royal Society, 138 Scottish independence referendum (2014), 64 Skripal poisoning (2018), 43 Society for Freedom in Science, 163 Thatcher government (1979–90), 154, 160, 163, 166 and torture, 92 Treasury, 61, 64 unemployment, 83 Unite for Europe march (2017), 23 World War II (1939–45), 114, 119, 138, 143, 180 see also England United Nations, 72, 222 United States Bayh–Dole Act (1980), 152 Black Lives Matter, 10, 225 BP oil spill (2010), 89 Bush Jr. administration (2001–9), 77, 136 Bush Sr administration (1989–93), 77 Bureau of Labor, 74 Central Intelligence Agency (CIA), 3, 136, 151, 199 Charlottesville attack (2017), 20 Civil War (1861–5), 105, 142 and climate change, 207, 214 Clinton administration (1993–2001), 77 Cold War, see Cold War Defense Advanced Research Projects Agency (DARPA), 176, 178 Defense Intelligence Agency, 177 drug abuse, 43, 100, 105, 115–16, 131, 172–3 education, 85 Federal Bureau of Investigation (FBI), 137 Federal Reserve, 33 Fifth Amendment (1789), 44 financial crisis (2007–9), 31–2, 82, 158 first past the post, 13 Government Accountability Office, 29 gross domestic product (GDP), 75–7, 82 health, 92, 99–100, 101, 103, 105, 107, 115–16, 158, 172–3 Heritage Foundation, 164, 214 Iraq War (2003–11), 74, 132 JFK Airport terror scare (2016), x, xiii, 41 Kansas populists (1880s), 220 libertarianism, 15, 151, 154, 158, 164, 173 life expectancy, 100, 101 March For Our Lives (2018), 21 March for Science (2017), 23–5, 27, 28, 210 McCarthyism (1947–56), 137 Million-Man March (1995), 4 National Aeronautics and Space Administration (NASA), 23, 175 National Defense Research Committee, 180 National Park Service, 4 National Security Agency (NSA), 152 Obama administration (2009–17), 3, 24, 76, 77, 79, 158 Occupy Wall Street (2011), 5, 10, 61 and opiates, 105, 172–3 and pain, 103, 105, 107, 172–3 Palantir, 151, 152, 175, 190 Paris climate accord (2015), 205, 207 Parkland attack (2018), 21 Patriot Act (2001), 137 Pentagon, 130, 132, 135, 136, 214, 216 presidential election (2016), see under presidential election, US psychiatry, 107, 111 quantitative easing, 31–2 Reagan administration (1981–9), 15, 77, 154, 160, 163, 166 Rumsfeld’s “unknown unknowns” speech (2002), 132 Semi-Automatic Ground Environment (SAGE), 180, 182, 200 September 11 attacks (2001), 17, 18 Tea Party, 32, 50, 61, 221 and torture, 93 Trump administration (2017–), see under Trump, Donald unemployment, 83 Vietnam War (1955–75), 111, 130, 136, 138, 143, 205 World War I (1914–18), 137 World War II (1939–45), 137, 180 universal basic income, 221 universities, 151–2, 164, 169–70 University of Cambridge, 84, 151 University of Chicago, 160 University of East Anglia, 195 University of Oxford, 56, 151 University of Vienna, 160 University of Washington, 188 unknown knowns, 132, 133, 136, 138, 141, 192, 212 unknown unknowns, 132, 133, 138 “Use of Knowledge in Society, The” (Hayek), 161 V2 flying bomb, 137 vaccines, 23, 95 de Vauban, Sébastien Le Prestre, Marquis de Vauban, 73 vehicle-ramming attacks, 17 Vesalius, Andreas, 96 Vienna, Austria, 153–5, 159 Vietnam War (1955–75), 111, 130, 136, 138, 143, 205 violence vs. power, 19, 219 viral marketing, 12 virtual reality, 183 virtue signaling, 194 voice recognition, 187 Vote Leave, 50, 93 Wainright, Joel, 214 Wales, 77, 90 Wall Street, New York, 33, 190 War College, Berlin, 128 “War Economy” (Neurath), 153–4 war on drugs, 43, 131 war on terror, 131, 136, 196 Watts, Jay, 115 weaponization, 18–20, 22, 26, 75, 118, 123, 194, 219, 223 weapons of mass destruction, 132 wearable technology, 173 weather control, 204 “What Is An Emotion?”

Software Engineering at Google: Lessons Learned From Programming Over Time
by Titus Winters , Tom Manshreck and Hyrum Wright
Published 17 Mar 2020

Software pervades so many aspects of society and culture, it is wise for us to be aware of both the good and the bad that we enable when making product and technical decisions. We discuss this much more in “Engineering for Equity.” In addition to the above costs (or our estimate of them), there are biases: status quo bias, loss aversion, etc. When we evaluate cost, we need to keep all of the above in mind: the health of an organization isn’t just whether there is money in the bank, it’s also whether its members are feeling valued and productive. In highly creative and lucrative fields like software engineering, financial cost is usually not the limiting factor: personnel cost usually is.

Money Moments: Simple Steps to Financial Well-Being
by Jason Butler
Published 22 Nov 2017

This risk, in the form of a wide range of potential return outcomes, is actually the source of their higher expected return over the long-term compared to cash deposits and fixed income securities. You therefore need to get used to seeing your capital fall in value on a regular basis if you want to earn a higher return. But this is easier said than done, mainly because people prefer avoiding losses to acquiring gains – a phenomenon known as loss aversion.29 Research shows that people give twice the weight to the pain of loss than they do the pleasure of gain. This means we seek risk when pursuing gains but become risk adverse in relation to losses, and are more likely to act if threatened with loss than promised gain. As long as you have a big enough cash reserve, a long enough time horizon and have a good spread of global companies, all you need to control is your emotions when investment markets take a tumble.

Irrational Exuberance: With a New Preface by the Author
by Robert J. Shiller
Published 15 Feb 2000

French and Richard Roll, “Stock Return Variances: The Arrival of Information and the Reaction of Traders,” Journal of Financial Economics, 17 (1986): 5–26; see also Richard Roll, “Orange Juice and Weather,” American Economic Review, 74 (1984): 861–80. 36. See Shlomo Benartzi and Richard H. Thaler, “Myopic Loss Aversion and the Equity Premium Puzzle,” Quarterly Journal of Economics, 110(1) (1995): 73–92. 37. Data are from the National Gambling Impact Study Commission, Final Report, Washington D.C., 1999, http://www.ngisc.gov/reports/exsum_1-7.pdf. 38. See also William N. Thompson, Legalized Gambling: A Reference Handbook (Santa Barbara, Calif.: ABC-CLIO, 1994), pp. 52–53. 39.

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“Regret in Decision Making under Uncertainty.” Operations Research, 30(5) (1982): 961–81. Benartzi, Shlomo. “Why Do Employees Invest Their Retirement Savings in Company Stock?” Unpublished paper, Anderson School, University of California, Los Angeles, 1999. Benartzi, Shlomo, and Richard H. Thaler. “Myopic Loss Aversion and the Equity Premium Puzzle.” Quarterly Journal of Economics, 110(1) (1995): 73–92. ———. “Naive Diversification Strategies in Defined Contribution Plans.” Unpublished paper, University of Chicago, 1998. RE F E RE N CE S 271 Bikhchandani, S. D., David Hirshleifer, and Ivo Welch. “A Theory of Fashion, Social Custom and Cultural Change.”

Predictably Irrational, Revised and Expanded Edition: The Hidden Forces That Shape Our Decisions
by Dan Ariely
Published 19 Feb 2007

Jack Knetsch, “The Endowment Effect and Evidence of Nonreversible Indifference Curves,” American Economic Review, Vol. 79 (1989), 1277–1284. Daniel Kahneman, Jack Knetsch, and Richard Thaler, “Experimental Tests of the Endowment Effect and the Coase Theorem,” Journal of Political Economy (1990). Daniel Kahneman, Jack Knetsch, and Richard H. Thaler, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias,” Journal of Economic Perspectives, Vol. 5 (1991), 193–206. Chapter 8: Keeping Doors Open BASED ON Jiwoong Shin and Dan Ariely, “Keeping Doors Open: The Effect of Unavailability on Incentives to Keep Options Viable,” Management Science (2004). RELATED READINGS Sheena Iyengar and Mark Lepper, “When Choice Is De-motivating: Can One Desire Too Much of a Good Thing?”

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, time spent on line for, 61 “Ikea effect,” 135 immediate gratification: e-mail and, 255–59 unpleasant medical treatments and, 261–64 imprinting, 25, 34, 43 see also anchoring indecision, 151–53 individualism, 68 thinking about money and, 74, 75 ingredients, exotic-sounding, 164–65 innovation, increased globalization and, 316–18 insurance fraud, 196, 223 insurance industry, 296 punitive finance practices of, 299-301 spreading cost of, 304 interest-only mortgages, 287–88 interferon, 260–64 internal mammary artery ligation, 173–74, 191 inventiveness, 68 IRA (Irish Republican Army), 156–57 Iran, lack of trust in, 214–15 irrational behaviors, xxix–xxx opportunities for improvement and, 240–44 systematic and predictable nature of, xxx, 239 see also specific topics IRS (Internal Revenue Service), 196 J Japan, savings rate in, 109 jealousy, comparisons and, 15–19 Jerome, Jerome K., 273–74 job performance. 320–24 public scrutiny and, 322 relationship between compensation and, 320–21, 322–24 Jobst suit, 192–94 Johnston, David Cay, 204 JP Morgan Chase, 280 judgment and decision making (JDM), xxviii see also behavioral economics “Just say no” campaign, 100, 101 K Kahneman, Daniel, 19, 129 Keeney, Ralph, 264 knee surgery, arthroscopic, 174–76 Knetsch, Jack, 129 Knight-McDowell, Victoria, 277 Koran, 215 L “Lake Wobegone Effect,” 268–69 Latin America, lack of trust in, 214 Lay, Kenneth, 219 learned helplessness, 312–16 experiments on, 312–14 in financial meltdown, 314–16 recovering from, 315–16 Leaves of Grass (Whitman), 40–41 Lee, Leonard, 21, 157–59, 161, 337 legal profession: attempts at improving ethics of, 213–14 decline of ethics and values in, 209–10 Lehman Brothers, 280, 310 leisure, blurring of partition between work and, 80, 81 Leland, John, 122–23 Leo III, Pope, 188 Leonardo da Vinci, 274 Levav, Jonathan, 231–37, 337 Levitt, Steven, xvi Li, Jian, 166–68 Lincoln, Abraham, 177 Linux, 81 List, John, xvi loans: punitive finance practices and, 300–301, 304 see also mortgages lobbyists, congressional restrictions on, 205 Loewenstein, George, 21, 26, 30–31, 39, 89,, 320–21, 337–38 Logic of Life, The (Harford), 291–92 Lorenz, Konrad, 25, 43 loss: aversion to, 134, 137, 138, 148–49 fear of, 54–55 Lost World, The (Crichton), 317–18 loyalty: in business-customer relations, 78–79 of employees to their companies, 80–84 M Macbeth (Shakespeare), 188 Madoff, Bernard, 291 Maier, Steve, 312-13 major, college students’ choice of, 141–42 manufacturer’s suggested retail price (MSRP), 30, 45 marketing: high price tag and, 24–25 hype of, related to satisfaction derived from product, 186–87, 190–91 relativity and, 1–6, 9–10 “trial” promotions and, 136–37 zero cost and, 49–50 market norms, 67–88 companies’ relations with their customers and, 78–80 companies’ relations with their employees and, 80–84, 252–54 doing away with, 86–88 education and, 85 mere mention of money and, 73–75 mixing signals of social norms and, 69, 73–74, 75–77, 79, 214, 250–52 reducing emphasis on, 88 social norms kept separate from, 67–69, 75–76, 77–78 willingness to risk life and, 84 working for gifts and, 72–74 working under social norms vs., 69–72 Maryland Judicial Task Force, 210 Mazar, Nina, 196–97, 206, 219–20, 224, 320–21, 338 McClure, Sam, 166–68 Mead, Nicole, 74–75 medical benefits, recent cuts in, 82 medical care, see health care medical profession: conflicts of interest and, 293, 295 decline of ethics and values in, 210 salaries of, as practicing physicians vs.

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution
by Gregory Zuckerman
Published 5 Nov 2019

In the 1970s, Israeli psychologists Amos Tversky and Daniel Kahneman had explored how individuals make decisions, demonstrating how prone most are to act irrationally. Later, economist Richard Thaler used psychological insights to explain anomalies in investor behavior, spurring the growth of the field of behavioral economics, which explored the cognitive biases of individuals and investors. Among those identified: loss aversion, or how investors generally feel the pain from losses twice as much as the pleasure from gains; anchoring, the way judgment is skewed by an initial piece of information or experience; and the endowment effect, how investors assign excessive value to what they already own in their portfolios. Kahneman and Thaler would win Nobel Prizes for their work.

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(Kelly formula), 91–92, 96, 127 Kempe, Julia, 272 Kennedy, John F., 31, 78 Kepler Financial Management, 133–34, 157, 166–67 kernel methods, 84–86, 96 Kirtland Air Force Base, 170–71 Klein, Naomi, 321 Koch, Charles, 278 Koch, David, 278 Kochen, Simon, 69–70, 71, 103 Kononenko, Alexey, 236–37, 241–43, 262–63, 270–71 Kostant, Bertram, 18, 20 Kovner, Bruce, 140 Kurz, Christopher, 121–22 Kushner, Jared, 281, 292 Lackman, Abe, 286 Laufer, Henry, xi, 101 background of, 140–41 Long Island Sound estate of, 227–28 at Renaissance, 109, 141–44, 149–50, 201, 229–31, 233 at Stony Brook, 77, 78, 84–85, 141–42 trading models, 77, 107–18, 142–43, 149–50, 156, 168, 189, 197, 229–30, 253, 258 Laufer, Marsha Zlatin, 141–42 Law of Vibration, 123 Lawrence School, 13 Leave.EU, 280–81 L’eggs, 162 Lehman Brothers, 173, 264, 309 Leibler, Dick, 26, 30–31, 32 Leinweber, David, 204 Leo, Leonard, 290 Let’s Make a Deal (TV show), 211 leverage, 188 Lewinsky, Monica, 208 Lieberman, Louis, 46 Limroy, 50–51, 53, 54, 55, 58, 98, 346 linear regression, 83–84 liquidity, 229 Lo, Andrew, 123, 124 locals, 110 Loma Prieta earthquake of 1989, 107 Long-Term Capital Management (LTCM), 209–11, 212–13, 226, 256 Lord Jim, The (yacht), 60 loss aversion, 152 Lott, John R., Jr., 207 Lourie, Robert, 11, 228, 257 Lux, Hal, 218 Lynch, Carolyn, 162 Lynch, Peter, xvi, 3, 161–63 McCain, John, 304 McCarthy, David, 154 McCarthy, Eugene, 74 McGrayne, Sharon, 202 machine learning, 4–5, 47–48, 144, 205, 215, 315 McNulty, Bill, 295 Macrae, Kenny, 267 macro investors, 164 “macroscopic variables,” 29 Madoff, Bernard, 146n, 198 Magellan Fund, 161–63, 333 Magerman, David, xi background of, 182–84 computer hacking of, 191–93, 213 confrontational behavior of, 235, 270 education of, 183–85 at IBM, 177, 181, 185, 191–92 Mercers and, 195, 213–14, 232, 277, 291–99, 318 at Penn, 270 philanthropic activity of, 270, 318 presidential election of 2016 and Trump, 290–94 Magerman, David, at Renaissance Brown and, 181–82, 191–95, 241, 294, 296, 297, 299, 318 computer bug, 194–95, 213 departures, 262–63, 269–70 firing, 317–18 Kononenko and, 237, 241–43, 262–63, 270–71 lawsuit and financial settlement, 318–19 misgivings of, 269–70 recruitment of, 181–82, 186–87 return to, 270–71 Simons and, 181–82, 186–87, 234–35, 237, 296–99 tech bubble, 215–17 trading system, 186–87, 191–95, 213–17, 234–36 Magerman, Debra, 291, 292 Magerman, Melvin, 182–83, 184 Mahlmann, Karsten, 114 Malloy, Martin, 259 management fees, 115n, 248 Man AHL, 313 Mandelbrot, Benoit, 127 Man for All Markets, A (Thorp), 128 Manhattan Fund, 123 market neutral, 166–67, 211, 255 Markov chains, 46–48, 81 Markov model, xx, 29, 174 Markowitz, Harry, 30 Massachusetts Institute of Technology (MIT), 9, 14–16, 17, 20–21, 89–91, 325–26 Mathematical Sciences Research Institute, 236–37 Math for America, 269, 296–99, 321 Matrix, The (movie), 307 Mattone, Vinny, 210–11 Mayer, Jane, 280 Mayer, Jimmy, 15, 16–17, 21, 38–39, 50 Mazur, Barry, 15 Medallion Fund basket options, 225–27 fees, 145–46, 235–36, 271, 315–16 financial crisis and, 257–61, 263–64 GAM Investments, 153–54 launch of, 98 move into stock investing, 157–58 returns, xvi, 140, 145–46, 151, 153, 156, 157, 215, 217–18, 223–24, 225, 247–48, 255, 271, 315–16, 319, 331–32 returns comparison, 333 Sharpe ratio, 218, 223–24, 245 size limit, 246–47 trading models, 107–9, 113, 138–40, 142–43, 156–57, 168, 197–205, 271–74 Media Research Center, 304 Mercer, Diana, 179, 186, 214, 228, 288 Mercer, Heather Sue, 207, 214, 228 Mercer, Jennifer “Jenji,” 179, 186, 228 Mercer, Rebekah, xi, 228 Bannon and Breitbart News, 278–83, 288–90, 294–95, 301–2 emergence as right-wing donor, 277–79, 301–2 Magerman and, 214, 291, 293, 298, 299 political blowback and, 301–2, 303–5 presidential election of 2016 and Trump, xviii, 279–86, 288–90, 294–95 at Renaissance, 214 Mercer, Robert, xi background of, 169–70 education of, 169–70 emergence as right-wing donor, xviii, 276–86, 325–26 at IBM, 4–5, 169, 171–81, 187–88, 202 interest in computers, 170–71 at Kirtland Air Force Base, 170–71 libertarian views of, 171, 207–8, 232, 235, 275–77 presidential election of 2016 and Trump, xviii, 279–87, 291–95, 299–300, 302 Stony Brook Harbor estate (Owl’s Nest), 228, 275, 288–89, 295 Mercer, Robert, at Renaissance client presentations, 251 as co-CEO, xviiin, 231, 290, 301 equity stake, 201 financial crisis and, 257–61 Magerman and, 195, 213–14, 232, 277, 291–99, 318 management, 230–31, 232–33, 237, 241–43, 254–55, 289–90 political blowback and, 291–305 recruitment of, 169, 179–80 resignation of, 301–2, 319 statistical-arbitrage trading system, 4–5, 187–91, 193–95, 197–99, 205–8, 213–14, 221–22, 223, 229–32, 255, 272 tech bubble, 215–17 Mercer, Thomas, 169, 179 Mercer, Virginia, 169 Mercer Family Foundation, 276 Meriwether, John, 209–11, 212 Merrill Lynch, 19–20, 54, 96 Merton, Robert C., 209 Mexico–United States border wall, 290–91 Microsoft, 38, 59 Milken, Michael, 105–6, 129 Millennium Management, 238, 252–54 minimal varieties, 26–28, 38 “Minimal Varieties in Riemannian Manifolds” (Simons), 28 Mirochnikoff, Sylvain, 278 Mississippi, 13–14 Mnuchin, Steve, 282 Monemetrics Ax at, 34, 51–52, 72–73 Baum at, 45, 49–60, 63–65 founding and naming of, 44–45 Hullender at, 54–59, 74 name change to Renaissance, 61.

The Glass Half-Empty: Debunking the Myth of Progress in the Twenty-First Century
by Rodrigo Aguilera
Published 10 Mar 2020

Eastern Europe, arguably the region in the world that has more brazenly embraced far-right populism, can be understood in the context of a search for national identity after decades (arguably centuries) of foreign domination, coupled with a post-2009 crisis of legitimacy in the EU and a refugee crisis in 2015–2016 that hardened the region’s already highly xenophobic attitudes.42 In the end, it must be reemphasized that there is no single, dominant explanation for democracy’s discontents any more than a single indicator like higher life satisfaction can provide any validity to the progress narrative. One could be inclined to see the growing problem of mental health (which the World Health Organization predicts will be the leading global burden of disease in 203043) and “diseases of despair” like drug and alcohol abuse and suicide to argue the opposite; and under the logic of loss aversion, it would follow that it would be more preferable not to be mentally ill or despairing than it would to be highly satisfied with life. While we cannot quantify the causes of the disenchantment with liberal democracy, we must recognize that the reasons are rooted in its failure to address the ever-increasing expectations of its citizens, whether these be economic or socio-psychological.

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US political philosopher John Rawls famously described the original position as a thought experiment of how such a just society should be conceived.49 In this original position outside a “veil of ignorance”, no citizen would know what life they would be born into within society. You could just as easily end up being Jeff Bezos as you could one of his warehouse workers. Under the logic of loss aversion, it would appear that it is preferable to avoid being the warehouse worker than hoping to be Bezos, which suggests that people would prefer to live under more egalitarian societies. Being the poorest Swede is far better than being the poorest American or Briton, even if the possibility of amassing stupefying levels of wealth is much higher in the latter two countries.

The New Trading for a Living: Psychology, Discipline, Trading Tools and Systems, Risk Control, Trade Management
by Alexander Elder
Published 28 Sep 2014

The irrational behavior increases when people feel under pressure. According to Dr. Shapiro, at the racetrack, “bets on long shots increase in the last two races of the day.” Prof. Daniel Kahneman writes in his book Thinking, Fast and Slow: “The sure loss is very aversive, and this drives you to take the risk … Considerable loss aversion exists even when the amount at risk is minuscule relative to your wealth … losses loom larger than corresponding gains.” He adds: “Animals, including people, fight harder to prevent losses than to achieve gains” and spells it out: “People who face very bad options take desperate gambles, accepting a high probability of making things worse in exchange for a small hope of avoiding a large loss.

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See also Timeframes; individual indicators Intrinsic value (options) Inverse ETFs Inversions (futures) Investing (long-term trading) Investors Intelligence Iron Triangle of risk control, the Isolation in trading J Japanese candlesticks Japanese Candlestick Charting Techniques (Steve Nison) K Kahneman, Daniel Kangaroo tails (fingers) Kaufman, Josh Keelan, Brian Key demands for trades Keynes, John Maynard L Lag, of moving averages Lane, George Large speculators Larsen, Max Leaders: of crowds and fear of uncertainty gurus dead followers of magic method market cycle loyalty to Learning trading skills LeBon, Gustave Letter writers (financial) Leverage, in forex Leveraged ETFs Leveraged inverse ETFs Life, taking charge of Limits, on futures Limit orders Liquidity Long-term price cycles Long-term timeframe Long-term trading (investing) Look-back windows (New High–New Low Index) Losers: AA principles for denial by emotional responses of and emotional trading fantasies of autopilot myth brain myth cult of personality undercapitalization myth pain and regret felt by and self-control vs. controlling markets self-destructive and volume of trading wishful thinking by Losers Anonymous Losses: in account as a whole businessman's risks vs. on CFDs cutting of former institutional traders inability to manage on options per share, limiting psychological effect of 6% Rule to limit 2% Rule to limit Loss aversion Lovvorn, Kerry Low-priced stocks, indictors based on volume of “Low” volume M MAs, see Moving averages MACD, see Moving Average Convergence-Divergence MACD-Histogram combined with channels divergences in Impulse system and market psychology peaks and valleys seasons of semiautomatic divergence scanner slope of time windows of trading rules in Triple Screen system MACD Lines crossover of Signal lines and MACD line in divergences and market psychology trading rules Mackay, Charles MacMillan, Lawrence Magic method gurus Managing trades forecasting vs.

Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist
by Kate Raworth
Published 22 Mar 2017

His findings, augmented by those of psychologists Daniel Kahneman and Amos Tversky in the 1970s, gave birth to the field now known as behavioural economics, which studies the many kinds of ‘cognitive bias’ that systematically cause humans to deviate from the ideal model of rationality. Examples abound. We (the WEIRD ones, at least) typically exhibit: availability bias – making decisions on the basis of more recent and more accessible information; loss aversion – the strong preference to avoid a loss rather than to make an equivalent gain; selective cognition – taking on board facts and arguments that fit with our existing frames; and risk bias – underestimating the likelihood of extreme events, while overestimating our ability to cope with them. There are many more.

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Page numbers in italics denote illustrations A Aalborg, Denmark, 290 Abbott, Anthony ‘Tony’, 31 ABCD group, 148 Abramovitz, Moses, 262 absolute decoupling, 260–61 Acemoglu, Daron, 86 advertising, 58, 106–7, 112, 281 Agbodjinou, Sénamé, 231 agriculture, 5, 46, 72–3, 148, 155, 178, 181, 183 Alaska, 9 Alaska Permanent Fund, 194 Alperovitz, Gar, 177 alternative enterprise designs, 190–91 altruism, 100, 104 Amazon, 192, 196, 276 Amazon rainforest, 105–6, 253 American Economic Association, 3 American Enterprise Institute, 67 American Tobacco Corporation, 107 Andes, 54 animal spirits, 110 Anthropocene epoch, 48, 253 anthropocentrism, 115 Apertuso, 230 Apple, 85, 192 Archer Daniels Midland (ADM), 148 Arendt, Hannah, 115–16 Argentina, 55, 274 Aristotle, 32, 272 Arrow, Kenneth, 134 Articles of Association and Memoranda, 233 Arusha, Tanzania, 202 Asia Wage Floor Alliance, 177 Asian financial crisis (1997), 90 Asknature.org, 232 Athens, 57 austerity, 163 Australia, 31, 103, 177, 180, 211, 224–6, 255, 260 Austria, 263, 274 availability bias, 112 AXIOM, 230 Axtell, Robert, 150 Ayres, Robert, 263 B B Corp, 241 Babylon, 13 Baker, Josephine, 157 balancing feedback loops, 138–41, 155, 271 Ballmer, Steve, 231 Bangla Pesa, 185–6, 293 Bangladesh, 10, 226 Bank for International Settlements, 256 Bank of America, 149 Bank of England, 145, 147, 256 banking, see under finance Barnes, Peter, 201 Barroso, José Manuel, 41 Bartlett, Albert Allen ‘Al’, 247 basic income, 177, 194, 199–201 basic personal values, 107–9 Basle, Switzerland, 80 Bauwens, Michel, 197 Beckerman, Wilfred, 258 Beckham, David, 171 Beech-Nut Packing Company, 107 behavioural economics, 11, 111–14 behavioural psychology, 103, 128 Beinhocker, Eric, 158 Belgium, 236, 252 Bentham, Jeremy, 98 Benyus, Janine, 116, 218, 223–4, 227, 232, 237, 241 Berger, John, 12, 281 Berlin Wall, 141 Bermuda, 277 Bernanke, Ben, 146 Bernays, Edward, 107, 112, 281–3 Bhopal gas disaster (1984), 9 Bible, 19, 114, 151 Big Bang (1986), 87 billionaires, 171, 200, 289 biodiversity, 10, 46, 48–9, 52, 85, 115, 155, 208, 210, 242, 299 as common pool resource, 201 and land conversion, 49 and inequality, 172 and reforesting, 50 biomass, 73, 118, 210, 212, 221 biomimicry, 116, 218, 227, 229 bioplastic, 224, 293 Birmingham, West Midlands, 10 Black, Fischer, 100–101 Blair, Anthony ‘Tony’, 171 Blockchain, 187, 192 blood donation, 104, 118 Body Shop, The, 232–4 Bogotá, Colombia, 119 Bolivia, 54 Boston, Massachusetts, 3 Bowen, Alex, 261 Bowles, Sam, 104 Box, George, 22 Boyce, James, 209 Brasselberg, Jacob, 187 Brazil, 124, 226, 281, 290 bread riots, 89 Brisbane, Australia, 31 Brown, Gordon, 146 Brynjolfsson, Erik, 193, 194, 258 Buddhism, 54 buen vivir, 54 Bullitt Center, Seattle, 217 Bunge, 148 Burkina Faso, 89 Burmark, Lynell, 13 business, 36, 43, 68, 88–9 automation, 191–5, 237, 258, 278 boom and bust, 246 and circular economy, 212, 215–19, 220, 224, 227–30, 232–4, 292 and complementary currencies, 184–5, 292 and core economy, 80 and creative destruction, 142 and feedback loops, 148 and finance, 183, 184 and green growth, 261, 265, 269 and households, 63, 68 living metrics, 241 and market, 68, 88 micro-businesses, 9 and neoliberalism, 67, 87 ownership, 190–91 and political funding, 91–2, 171–2 and taxation, 23, 276–7 workers’ rights, 88, 91, 269 butterfly economy, 220–42 C C–ROADS (Climate Rapid Overview and Decision Support), 153 C40 network, 280 calculating man, 98 California, United States, 213, 224, 293 Cambodia, 254 Cameron, David, 41 Canada, 196, 255, 260, 281, 282 cancer, 124, 159, 196 Capital Institute, 236 carbon emissions, 49–50, 59, 75 and decoupling, 260, 266 and forests, 50, 52 and inequality, 58 reduction of, 184, 201, 213, 216–18, 223–7, 239–41, 260, 266 stock–flow dynamics, 152–4 taxation, 201, 213 Cargill, 148 Carney, Mark, 256 Caterpillar, 228 Catholic Church, 15, 19 Cato Institute, 67 Celts, 54 central banks, 6, 87, 145, 146, 147, 183, 184, 256 Chang, Ha-Joon, 82, 86, 90 Chaplin, Charlie, 157 Chiapas, Mexico, 121–2 Chicago Board Options Exchange (CBOE), 100–101 Chicago School, 34, 99 Chile, 7, 42 China, 1, 7, 48, 154, 289–90 automation, 193 billionaires, 200, 289 greenhouse gas emissions, 153 inequality, 164 Lake Erhai doughnut analysis, 56 open-source design, 196 poverty reduction, 151, 198 renewable energy, 239 tiered pricing, 213 Chinese Development Bank, 239 chrematistics, 32, 273 Christianity, 15, 19, 114, 151 cigarettes, 107, 124 circular economy, 220–42, 257 Circular Flow diagram, 19–20, 28, 62–7, 64, 70, 78, 87, 91, 92, 93, 262 Citigroup, 149 Citizen Reaction Study, 102 civil rights movement, 77 Cleveland, Ohio, 190 climate change, 1, 3, 5, 29, 41, 45–53, 63, 74, 75–6, 91, 141, 144, 201 circular economy, 239, 241–2 dynamics of, 152–5 and G20, 31 and GDP growth, 255, 256, 260, 280 and heuristics, 114 and human rights, 10 and values, 126 climate positive cities, 239 closed systems, 74 coffee, 221 cognitive bias, 112–14 Colander, David, 137 Colombia, 119 common-pool resources, 82–3, 181, 201–2 commons, 69, 82–4, 287 collaborative, 78, 83, 191, 195, 196, 264, 292 cultural, 83 digital, 82, 83, 192, 197, 281 and distribution, 164, 180, 181–2, 205, 267 Embedded Economy, 71, 73, 77–8, 82–4, 85, 92 knowledge, 197, 201–2, 204, 229, 231, 292 commons and money creation, see complementary currencies natural, 82, 83, 180, 181–2, 201, 265 and regeneration, 229, 242, 267, 292 and state, 85, 93, 197, 237 and systems, 160 tragedy of, 28, 62, 69, 82, 181 triumph of, 83 and values, 106, 108 Commons Trusts, 201 complementary currencies, 158, 182–8, 236, 292 complex systems, 28, 129–62 complexity science, 136–7 Consumer Reaction Study, 102 consumerism, 58, 102, 121, 280–84 cooking, 45, 80, 186 Coote, Anna, 278 Copenhagen, Denmark, 124 Copernicus, Nicolaus, 14–15 copyright, 195, 197, 204 core economy, 79–80 Corporate To Do List, 215–19 Costa Rica, 172 Council of Economic Advisers, US, 6, 37 Cox, Jo, 117 cradle to cradle, 224 creative destruction, 142 Cree, 282 Crompton, Tom, 125–6 cross-border flows, 89–90 crowdsourcing, 204 cuckoos, 32, 35, 36, 38, 40, 54, 60, 159, 244, 256, 271 currencies, 182–8, 236, 274, 292 D da Vinci, Leonardo, 13, 94–5 Dallas, Texas, 120 Daly, Herman, 74, 143, 271 Danish Nudging Network, 124 Darwin, Charles, 14 Debreu, Gerard, 134 debt, 37, 146–7, 172–3, 182–5, 247, 255, 269 decoupling, 193, 210, 258–62, 273 defeat device software, 216 deforestation, 49–50, 74, 208, 210 degenerative linear economy, 211–19, 222–3, 237 degrowth, 244 DeMartino, George, 161 democracy, 77, 171–2, 258 demurrage, 274 Denmark, 180, 275, 290 deregulation, 82, 87, 269 derivatives, 100–101, 149 Devas, Charles Stanton, 97 Dey, Suchitra, 178 Diamond, Jared, 154 diarrhoea, 5 differential calculus, 131, 132 digital revolution, 191–2, 264 diversify–select–amplify, 158 double spiral, 54 Doughnut model, 10–11, 11, 23–5, 44, 51 and aspiration, 58–9, 280–84 big picture, 28, 42, 61–93 distribution, 29, 52, 57, 58, 76, 93, 158, 163–205 ecological ceiling, 10, 11, 44, 45, 46, 49, 51, 218, 254, 295, 298 goal, 25–8, 31–60 and governance, 57, 59 growth agnosticism, 29–30, 243–85 human nature, 28–9, 94–128 and population, 57–8 regeneration, 29, 158, 206–42 social foundation, 10, 11, 44, 45, 49, 51, 58, 77, 174, 200, 254, 295–6 systems, 28, 129–62 and technology, 57, 59 Douglas, Margaret, 78–9 Dreyfus, Louis, 148 ‘Dumb and Dumber in Macroeconomics’ (Solow), 135 Durban, South Africa, 214 E Earning by Learning, 120 Earth-system science, 44–53, 115, 216, 288, 298 Easter Island, 154 Easterlin, Richard, 265–6 eBay, 105, 192 eco-literacy, 115 ecological ceiling, 10, 11, 44, 45, 46, 49, 51, 218, 254, 295, 298 Ecological Performance Standards, 241 Econ 101 course, 8, 77 Economics (Lewis), 114 Economics (Samuelson), 19–20, 63–7, 70, 74, 78, 86, 91, 92, 93, 262 Economy for the Common Good, 241 ecosystem services, 7, 116, 269 Ecuador, 54 education, 9, 43, 45, 50–52, 85, 169–70, 176, 200, 249, 279 economic, 8, 11, 18, 22, 24, 36, 287–93 environmental, 115, 239–40 girls’, 57, 124, 178, 198 online, 83, 197, 264, 290 pricing, 118–19 efficient market hypothesis, 28, 62, 68, 87 Egypt, 48, 89 Eisenstein, Charles, 116 electricity, 9, 45, 236, 240 and Bangla Pesa, 186 cars, 231 Ethereum, 187–8 and MONIAC, 75, 262 pricing, 118, 213 see also renewable energy Elizabeth II, Queen of the United Kingdom, 145 Ellen MacArthur Foundation, 220 Embedded Economy, 71–93, 263 business, 88–9 commons, 82–4 Earth, 72–6 economy, 77–8 finance, 86–8 household, 78–81 market, 81–2 power, 91–92 society, 76–7 state, 84–6 trade, 89–90 employment, 36, 37, 51, 142, 176 automation, 191–5, 237, 258, 278 labour ownership, 188–91 workers’ rights, 88, 90, 269 Empty World, 74 Engels, Friedrich, 88 environment and circular economy, 220–42, 257 conservation, 121–2 and degenerative linear economy, 211–19, 222–3 degradation, 5, 9, 10, 29, 44–53, 74, 154, 172, 196, 206–42 education on, 115, 239–40 externalities, 152 fair share, 216–17 and finance, 234–7 generosity, 218–19, 223–7 green growth, 41, 210, 243–85 nudging, 123–5 taxation and quotas, 213–14, 215 zero impact, 217–18, 238, 241 Environmental Dashboard, 240–41 environmental economics, 7, 11, 114–16 Environmental Kuznets Curve, 207–11, 241 environmental space, 54 Epstein, Joshua, 150 equilibrium theory, 134–62 Ethereum, 187–8 ethics, 160–62 Ethiopia, 9, 226, 254 Etsy, 105 Euclid, 13, 15 European Central Bank, 145, 275 European Commission, 41 European Union (EU), 92, 153, 210, 222, 255, 258 Evergreen Cooperatives, 190 Evergreen Direct Investing (EDI), 273 exogenous shocks, 141 exponential growth, 39, 246–85 externalities, 143, 152, 213 Exxon Valdez oil spill (1989), 9 F Facebook, 192 fair share, 216–17 Fama, Eugene, 68, 87 fascism, 234, 277 Federal Reserve, US, 87, 145, 146, 271, 282 feedback loops, 138–41, 143, 148, 155, 250, 271 feminist economics, 11, 78–81, 160 Ferguson, Thomas, 91–2 finance animal spirits, 110 bank runs, 139 Black–Scholes model, 100–101 boom and bust, 28–9, 110, 144–7 and Circular Flow, 63–4, 87 and complex systems, 134, 138, 139, 140, 141, 145–7 cross-border flows, 89 deregulation, 87 derivatives, 100–101, 149 and distribution, 169, 170, 173, 182–4, 198–9, 201 and efficient market hypothesis, 63, 68 and Embedded Economy, 71, 86–8 and financial-instability hypothesis, 87, 146 and GDP growth, 38 and media, 7–8 mobile banking, 199–200 and money creation, 87, 182–5 and regeneration, 227, 229, 234–7 in service to life, 159, 234–7 stakeholder finance, 190 and sustainability, 216, 235–6, 239 financial crisis (2008), 1–4, 5, 40, 63, 86, 141, 144, 278, 290 and efficient market hypothesis, 87 and equilibrium theory, 134, 145 and financial-instability hypothesis, 87 and inequality, 90, 170, 172, 175 and money creation, 182 and worker’s rights, 278 financial flows, 89 Financial Times, 183, 266, 289 financial-instability hypothesis, 87, 146 First Green Bank, 236 First World War (1914–18), 166, 170 Fisher, Irving, 183 fluid values, 102, 106–9 food, 3, 43, 45, 50, 54, 58, 59, 89, 198 food banks, 165 food price crisis (2007–8), 89, 90, 180 Ford, 277–8 foreign direct investment, 89 forest conservation, 121–2 fossil fuels, 59, 73, 75, 92, 212, 260, 263 Foundations of Economic Analysis (Samuelson), 17–18 Foxconn, 193 framing, 22–3 France, 43, 165, 196, 238, 254, 256, 281, 290 Frank, Robert, 100 free market, 33, 37, 67, 68, 70, 81–2, 86, 90 free open-source hardware (FOSH), 196–7 free open-source software (FOSS), 196 free trade, 70, 90 Freeman, Ralph, 18–19 freshwater cycle, 48–9 Freud, Sigmund, 107, 281 Friedman, Benjamin, 258 Friedman, Milton, 34, 62, 66–9, 84–5, 88, 99, 183, 232 Friends of the Earth, 54 Full World, 75 Fuller, Buckminster, 4 Fullerton, John, 234–6, 273 G G20, 31, 56, 276, 279–80 G77, 55 Gal, Orit, 141 Gandhi, Mohandas, 42, 293 Gangnam Style, 145 Gardens of Democracy, The (Liu & Hanauer), 158 gender equality, 45, 51–2, 57, 78–9, 85, 88, 118–19, 124, 171, 198 generosity, 218–19, 223–9 geometry, 13, 15 George, Henry, 149, 179 Georgescu-Roegen, Nicholas, 252 geothermal energy, 221 Gerhardt, Sue, 283 Germany, 2, 41, 100, 118, 165, 189, 211, 213, 254, 256, 260, 274 Gessel, Silvio, 274 Ghent, Belgium, 236 Gift Relationship, The (Titmuss), 118–19 Gigerenzer, Gerd, 112–14 Gintis, Herb, 104 GiveDirectly, 200 Glass–Steagall Act (1933), 87 Glennon, Roger, 214 Global Alliance for Tax Justice, 277 global material footprints, 210–11 Global Village Construction Set, 196 globalisation, 89 Goerner, Sally, 175–6 Goffmann, Erving, 22 Going for Growth, 255 golden rule, 91 Goldman Sachs, 149, 170 Gómez-Baggethun, Erik, 122 Goodall, Chris, 211 Goodwin, Neva, 79 Goody, Jade, 124 Google, 192 Gore, Albert ‘Al’, 172 Gorgons, 244, 256, 257, 266 graffiti, 15, 25, 287 Great Acceleration, 46, 253–4 Great Depression (1929–39), 37, 70, 170, 173, 183, 275, 277, 278 Great Moderation, 146 Greece, Ancient, 4, 13, 32, 48, 54, 56–7, 160, 244 green growth, 41, 210, 243–85 Greenham, Tony, 185 greenhouse gas emissions, 31, 46, 50, 75–6, 141, 152–4 and decoupling, 260, 266 and Environmental Kuznets Curve, 208, 210 and forests, 50, 52 and G20, 31 and inequality, 58 reduction of, 184, 201–2, 213, 216–18, 223–7, 239–41, 256, 259–60, 266, 298 stock–flow dynamics, 152–4 and taxation, 201, 213 Greenland, 141, 154 Greenpeace, 9 Greenspan, Alan, 87 Greenwich, London, 290 Grenoble, France, 281 Griffiths, Brian, 170 gross domestic product (GDP), 25, 31–2, 35–43, 57, 60, 84, 164 as cuckoo, 32, 35, 36, 38, 40, 54, 60, 159, 244, 256, 271 and Environmental Kuznets Curve, 207–11 and exponential growth, 39, 53, 246–85 and growth agnosticism, 29–30, 240, 243–85 and inequality, 173 and Kuznets Curve, 167, 173, 188–9 gross national product (GNP), 36–40 Gross World Product, 248 Grossman, Gene, 207–8, 210 ‘grow now, clean up later’, 207 Guatemala, 196 H Haifa, Israel, 120 Haldane, Andrew, 146 Han Dynasty, 154 Hanauer, Nick, 158 Hansen, Pelle, 124 Happy Planet Index, 280 Hardin, Garrett, 69, 83, 181 Harvard University, 2, 271, 290 von Hayek, Friedrich, 7–8, 62, 66, 67, 143, 156, 158 healthcare, 43, 50, 57, 85, 123, 125, 170, 176, 200, 269, 279 Heilbroner, Robert, 53 Henry VIII, King of England and Ireland, 180 Hepburn, Cameron, 261 Herbert Simon, 111 heuristics, 113–14, 118, 123 high-income countries growth, 30, 244–5, 254–72, 282 inequality, 165, 168, 169, 171 labour, 177, 188–9, 278 overseas development assistance (ODA), 198–9 resource intensive lifestyles, 46, 210–11 trade, 90 Hippocrates, 160 History of Economic Analysis (Schumpeter), 21 HIV/AIDS, 123 Holocene epoch, 46–8, 75, 115, 253 Homo economicus, 94–103, 109, 127–8 Homo sapiens, 38, 104, 130 Hong Kong, 180 household, 78 housing, 45, 59, 176, 182–3, 269 Howe, Geoffrey, 67 Hudson, Michael, 183 Human Development Index, 9, 279 human nature, 28 human rights, 10, 25, 45, 49, 50, 95, 214, 233 humanistic economics, 42 hydropower, 118, 260, 263 I Illinois, United States, 179–80 Imago Mundi, 13 immigration, 82, 199, 236, 266 In Defense of Economic Growth (Beckerman), 258 Inclusive Wealth Index, 280 income, 51, 79–80, 82, 88, 176–8, 188–91, 194, 199–201 India, 2, 9, 10, 42, 124, 164, 178, 196, 206–7, 242, 290 Indonesia, 90, 105–6, 164, 168, 200 Indus Valley civilisation, 48 inequality, 1, 5, 25, 41, 63, 81, 88, 91, 148–52, 209 and consumerism, 111 and democracy, 171 and digital revolution, 191–5 and distribution, 163–205 and environmental degradation, 172 and GDP growth, 173 and greenhouse gas emissions, 58 and intellectual property, 195–8 and Kuznets Curve, 29, 166–70, 173–4 and labour ownership, 188–91 and land ownership, 178–82 and money creation, 182–8 and social welfare, 171 Success to the Successful, 148, 149, 151, 166 inflation, 36, 248, 256, 275 insect pollination services, 7 Institute of Economic Affairs, 67 institutional economics, 11 intellectual property rights, 195–8, 204 interest, 36, 177, 182, 184, 275–6 Intergovernmental Panel on Climate Change, 25 International Monetary Fund (IMF), 170, 172, 173, 183, 255, 258, 271 Internet, 83–4, 89, 105, 192, 202, 264 Ireland, 277 Iroquois Onondaga Nation, 116 Israel, 100, 103, 120 Italy, 165, 196, 254 J Jackson, Tim, 58 Jakubowski, Marcin, 196 Jalisco, Mexico, 217 Japan, 168, 180, 211, 222, 254, 256, 263, 275 Jevons, William Stanley, 16, 97–8, 131, 132, 137, 142 John Lewis Partnership, 190 Johnson, Lyndon Baines, 37 Johnson, Mark, 38 Johnson, Todd, 191 JPMorgan Chase, 149, 234 K Kahneman, Daniel, 111 Kamkwamba, William, 202, 204 Kasser, Tim, 125–6 Keen, Steve, 146, 147 Kelly, Marjorie, 190–91, 233 Kennedy, John Fitzgerald, 37, 250 Kennedy, Paul, 279 Kenya, 118, 123, 180, 185–6, 199–200, 226, 292 Keynes, John Maynard, 7–8, 22, 66, 69, 134, 184, 251, 277–8, 284, 288 Kick It Over movement, 3, 289 Kingston, London, 290 Knight, Frank, 66, 99 knowledge commons, 202–4, 229, 292 Kokstad, South Africa, 56 Kondratieff waves, 246 Korzybski, Alfred, 22 Krueger, Alan, 207–8, 210 Kuhn, Thomas, 22 Kumhof, Michael, 172 Kuwait, 255 Kuznets, Simon, 29, 36, 39–40, 166–70, 173, 174, 175, 204, 207 KwaZulu Natal, South Africa, 56 L labour ownership, 188–91 Lake Erhai, Yunnan, 56 Lakoff, George, 23, 38, 276 Lamelara, Indonesia, 105–6 land conversion, 49, 52, 299 land ownership, 178–82 land-value tax, 73, 149, 180 Landesa, 178 Landlord’s Game, The, 149 law of demand, 16 laws of motion, 13, 16–17, 34, 129, 131 Lehman Brothers, 141 Leopold, Aldo, 115 Lesotho, 118, 199 leverage points, 159 Lewis, Fay, 178 Lewis, Justin, 102 Lewis, William Arthur, 114, 167 Lietaer, Bernard, 175, 236 Limits to Growth, 40, 154, 258 Linux, 231 Liu, Eric, 158 living metrics, 240–42 living purpose, 233–4 Lomé, Togo, 231 London School of Economics (LSE), 2, 34, 65, 290 London Underground, 12 loss aversion, 112 low-income countries, 90, 164–5, 168, 173, 180, 199, 201, 209, 226, 254, 259 Lucas, Robert, 171 Lula da Silva, Luiz Inácio, 124 Luxembourg, 277 Lyle, John Tillman, 214 Lyons, Oren, 116 M M–PESA, 199–200 MacDonald, Tim, 273 Machiguenga, 105–6 MacKenzie, Donald, 101 macroeconomics, 36, 62–6, 76, 80, 134–5, 145, 147, 150, 244, 280 Magie, Elizabeth, 149, 153 Malala effect, 124 malaria, 5 Malawi, 118, 202, 204 Malaysia, 168 Mali, Taylor, 243 Malthus, Thomas, 252 Mamsera Rural Cooperative, 190 Manhattan, New York, 9, 41 Mani, Muthukumara, 206 Manitoba, 282 Mankiw, Gregory, 2, 34 Mannheim, Karl, 22 Maoris, 54 market, 81–2 and business, 88 circular flow, 64 and commons, 83, 93, 181, 200–201 efficiency of, 28, 62, 68, 87, 148, 181 and equilibrium theory, 131–5, 137, 143–7, 155, 156 free market, 33, 37, 67–70, 90, 208 and households, 63, 69, 78, 79 and maxi-max rule, 161 and pricing, 117–23, 131, 160 and rational economic man, 96, 100–101, 103, 104 and reciprocity, 105, 106 reflexivity of, 144–7 and society, 69–70 and state, 84–6, 200, 281 Marshall, Alfred, 17, 98, 133, 165, 253, 282 Marx, Karl, 88, 142, 165, 272 Massachusetts Institute of Technology (MIT), 17–20, 152–5 massive open online courses (MOOCs), 290 Matthew Effect, 151 Max-Neef, Manfred, 42 maxi-max rule, 161 maximum wage, 177 Maya civilisation, 48, 154 Mazzucato, Mariana, 85, 195, 238 McAfee, Andrew, 194, 258 McDonough, William, 217 Meadows, Donella, 40, 141, 159, 271, 292 Medusa, 244, 257, 266 Merkel, Angela, 41 Messerli, Elspeth, 187 Metaphors We Live By (Lakoff & Johnson), 38 Mexico, 121–2, 217 Michaels, Flora S., 6 micro-businesses, 9, 173, 178 microeconomics, 132–4 microgrids, 187–8 Micronesia, 153 Microsoft, 231 middle class, 6, 46, 58 middle-income countries, 90, 164, 168, 173, 180, 226, 254 migration, 82, 89–90, 166, 195, 199, 236, 266, 286 Milanovic, Branko, 171 Mill, John Stuart, 33–4, 73, 97, 250, 251, 283, 284, 288 Millo, Yuval, 101 minimum wage, 82, 88, 176 Minsky, Hyman, 87, 146 Mises, Ludwig von, 66 mission zero, 217 mobile banking, 199–200 mobile phones, 222 Model T revolution, 277–8 Moldova, 199 Mombasa, Kenya, 185–6 Mona Lisa (da Vinci), 94 money creation, 87, 164, 177, 182–8, 205 MONIAC (Monetary National Income Analogue Computer), 64–5, 75, 142, 262 Monoculture (Michaels), 6 Monopoly, 149 Mont Pelerin Society, 67, 93 Moral Consequences of Economic Growth, The (Friedman), 258 moral vacancy, 41 Morgan, Mary, 99 Morogoro, Tanzania, 121 Moyo, Dambisa, 258 Muirhead, Sam, 230, 231 MultiCapital Scorecard, 241 Murphy, David, 264 Murphy, Richard, 185 musical tastes, 110 Myriad Genetics, 196 N national basic income, 177 Native Americans, 115, 116, 282 natural capital, 7, 116, 269 Natural Economic Order, The (Gessel), 274 Nedbank, 216 negative externalities, 213 negative interest rates, 275–6 neoclassical economics, 134, 135 neoliberalism, 7, 62–3, 67–70, 81, 83, 84, 88, 93, 143, 170, 176 Nepal, 181, 199 Nestlé, 217 Netherlands, 211, 235, 224, 226, 238, 277 networks, 110–11, 117, 118, 123, 124–6, 174–6 neuroscience, 12–13 New Deal, 37 New Economics Foundation, 278, 283 New Year’s Day, 124 New York, United States, 9, 41, 55 Newlight Technologies, 224, 226, 293 Newton, Isaac, 13, 15–17, 32–3, 95, 97, 129, 131, 135–7, 142, 145, 162 Nicaragua, 196 Nigeria, 164 nitrogen, 49, 52, 212–13, 216, 218, 221, 226, 298 ‘no pain, no gain’, 163, 167, 173, 204, 209 Nobel Prize, 6–7, 43, 83, 101, 167 Norway, 281 nudging, 112, 113, 114, 123–6 O Obama, Barack, 41, 92 Oberlin, Ohio, 239, 240–41 Occupy movement, 40, 91 ocean acidification, 45, 46, 52, 155, 242, 298 Ohio, United States, 190, 239 Okun, Arthur, 37 onwards and upwards, 53 Open Building Institute, 196 Open Source Circular Economy (OSCE), 229–32 open systems, 74 open-source design, 158, 196–8, 265 open-source licensing, 204 Organisation for Economic Co-operation and Development (OECD), 38, 210, 255–6, 258 Origin of Species, The (Darwin), 14 Ormerod, Paul, 110, 111 Orr, David, 239 Ostrom, Elinor, 83, 84, 158, 160, 181–2 Ostry, Jonathan, 173 OSVehicle, 231 overseas development assistance (ODA), 198–200 ownership of wealth, 177–82 Oxfam, 9, 44 Oxford University, 1, 36 ozone layer, 9, 50, 115 P Pachamama, 54, 55 Pakistan, 124 Pareto, Vilfredo, 165–6, 175 Paris, France, 290 Park 20|20, Netherlands, 224, 226 Parker Brothers, 149 Patagonia, 56 patents, 195–6, 197, 204 patient capital, 235 Paypal, 192 Pearce, Joshua, 197, 203–4 peer-to-peer networks, 187, 192, 198, 203, 292 People’s QE, 184–5 Perseus, 244 Persia, 13 Peru, 2, 105–6 Phillips, Adam, 283 Phillips, William ‘Bill’, 64–6, 75, 142, 262 phosphorus, 49, 52, 212–13, 218, 298 Physiocrats, 73 Pickett, Kate, 171 pictures, 12–25 Piketty, Thomas, 169 Playfair, William, 16 Poincaré, Henri, 109, 127–8 Polanyi, Karl, 82, 272 political economy, 33–4, 42 political funding, 91–2, 171–2 political voice, 43, 45, 51–2, 77, 117 pollution, 29, 45, 52, 85, 143, 155, 206–17, 226, 238, 242, 254, 298 population, 5, 46, 57, 155, 199, 250, 252, 254 Portugal, 211 post-growth society, 250 poverty, 5, 9, 37, 41, 50, 88, 118, 148, 151 emotional, 283 and inequality, 164–5, 168–9, 178 and overseas development assistance (ODA), 198–200 and taxation, 277 power, 91–92 pre-analytic vision, 21–2 prescription medicines, 123 price-takers, 132 prices, 81, 118–23, 131, 160 Principles of Economics (Mankiw), 34 Principles of Economics (Marshall), 17, 98 Principles of Political Economy (Mill), 288 ProComposto, 226 Propaganda (Bernays), 107 public relations, 107, 281 public spending v. investment, 276 public–private patents, 195 Putnam, Robert, 76–7 Q quantitative easing (QE), 184–5 Quebec, 281 Quesnay, François, 16, 73 R Rabot, Ghent, 236 Rancière, Romain, 172 rating and review systems, 105 rational economic man, 94–103, 109, 111, 112, 126, 282 Reagan, Ronald, 67 reciprocity, 103–6, 117, 118, 123 reflexivity of markets, 144 reinforcing feedback loops, 138–41, 148, 250, 271 relative decoupling, 259 renewable energy biomass energy, 118, 221 and circular economy, 221, 224, 226, 235, 238–9, 274 and commons, 83, 85, 185, 187–8, 192, 203, 264 geothermal energy, 221 and green growth, 257, 260, 263, 264, 267 hydropower, 118, 260, 263 pricing, 118 solar energy, see solar energy wave energy, 221 wind energy, 75, 118, 196, 202–3, 221, 233, 239, 260, 263 rentier sector, 180, 183, 184 reregulation, 82, 87, 269 resource flows, 175 resource-intensive lifestyles, 46 Rethinking Economics, 289 Reynebeau, Guy, 237 Ricardo, David, 67, 68, 73, 89, 250 Richardson, Katherine, 53 Rifkin, Jeremy, 83, 264–5 Rise and Fall of the Great Powers, The (Kennedy), 279 risk, 112, 113–14 Robbins, Lionel, 34 Robinson, James, 86 Robinson, Joan, 142 robots, 191–5, 237, 258, 278 Rockefeller Foundation, 135 Rockford, Illinois, 179–80 Rockström, Johan, 48, 55 Roddick, Anita, 232–4 Rogoff, Kenneth, 271, 280 Roman Catholic Church, 15, 19 Rombo, Tanzania, 190 Rome, Ancient, 13, 48, 154 Romney, Mitt, 92 Roosevelt, Franklin Delano, 37 rooted membership, 190 Rostow, Walt, 248–50, 254, 257, 267–70, 284 Ruddick, Will, 185 rule of thumb, 113–14 Ruskin, John, 42, 223 Russia, 200 rust belt, 90, 239 S S curve, 251–6 Sainsbury’s, 56 Samuelson, Paul, 17–21, 24–5, 38, 62–7, 70, 74, 84, 91, 92, 93, 262, 290–91 Sandel, Michael, 41, 120–21 Sanergy, 226 sanitation, 5, 51, 59 Santa Fe, California, 213 Santinagar, West Bengal, 178 São Paolo, Brazil, 281 Sarkozy, Nicolas, 43 Saumweder, Philipp, 226 Scharmer, Otto, 115 Scholes, Myron, 100–101 Schumacher, Ernst Friedrich, 42, 142 Schumpeter, Joseph, 21 Schwartz, Shalom, 107–9 Schwarzenegger, Arnold, 163, 167, 204 ‘Science and Complexity’ (Weaver), 136 Scotland, 57 Seaman, David, 187 Seattle, Washington, 217 second machine age, 258 Second World War (1939–45), 18, 37, 70, 170 secular stagnation, 256 self-interest, 28, 68, 96–7, 99–100, 102–3 Selfish Society, The (Gerhardt), 283 Sen, Amartya, 43 Shakespeare, William, 61–3, 67, 93 shale gas, 264, 269 Shang Dynasty, 48 shareholders, 82, 88, 189, 191, 227, 234, 273, 292 sharing economy, 264 Sheraton Hotel, Boston, 3 Siegen, Germany, 290 Silicon Valley, 231 Simon, Julian, 70 Sinclair, Upton, 255 Sismondi, Jean, 42 slavery, 33, 77, 161 Slovenia, 177 Small Is Beautiful (Schumacher), 42 smart phones, 85 Smith, Adam, 33, 57, 67, 68, 73, 78–9, 81, 96–7, 103–4, 128, 133, 160, 181, 250 social capital, 76–7, 122, 125, 172 social contract, 120, 125 social foundation, 10, 11, 44, 45, 49, 51, 58, 77, 174, 200, 254, 295–6 social media, 83, 281 Social Progress Index, 280 social pyramid, 166 society, 76–7 solar energy, 59, 75, 111, 118, 187–8, 190 circular economy, 221, 222, 223, 224, 226–7, 239 commons, 203 zero-energy buildings, 217 zero-marginal-cost revolution, 84 Solow, Robert, 135, 150, 262–3 Soros, George, 144 South Africa, 56, 177, 214, 216 South Korea, 90, 168 South Sea Bubble (1720), 145 Soviet Union (1922–91), 37, 67, 161, 279 Spain, 211, 238, 256 Spirit Level, The (Wilkinson & Pickett), 171 Sraffa, Piero, 148 St Gallen, Switzerland, 186 Stages of Economic Growth, The (Rostow), 248–50, 254 stakeholder finance, 190 Standish, Russell, 147 state, 28, 33, 69–70, 78, 82, 160, 176, 180, 182–4, 188 and commons, 85, 93, 197, 237 and market, 84–6, 200, 281 partner state, 197, 237–9 and robots, 195 stationary state, 250 Steffen, Will, 46, 48 Sterman, John, 66, 143, 152–4 Steuart, James, 33 Stiglitz, Joseph, 43, 111, 196 stocks and flows, 138–41, 143, 144, 152 sub-prime mortgages, 141 Success to the Successful, 148, 149, 151, 166 Sugarscape, 150–51 Summers, Larry, 256 Sumner, Andy, 165 Sundrop Farms, 224–6 Sunstein, Cass, 112 supply and demand, 28, 132–6, 143, 253 supply chains, 10 Sweden, 6, 255, 275, 281 swishing, 264 Switzerland, 42, 66, 80, 131, 186–7, 275 T Tableau économique (Quesnay), 16 tabula rasa, 20, 25, 63, 291 takarangi, 54 Tanzania, 121, 190, 202 tar sands, 264, 269 taxation, 78, 111, 165, 170, 176, 177, 237–8, 276–9 annual wealth tax, 200 environment, 213–14, 215 global carbon tax, 201 global financial transactions tax, 201, 235 land-value tax, 73, 149, 180 non-renewable resources, 193, 237–8, 278–9 People’s QE, 185 tax relief v. tax justice, 23, 276–7 TED (Technology, Entertainment, Design), 202, 258 Tempest, The (Shakespeare), 61, 63, 93 Texas, United States, 120 Thailand, 90, 200 Thaler, Richard, 112 Thatcher, Margaret, 67, 69, 76 Theory of Moral Sentiments (Smith), 96 Thompson, Edward Palmer, 180 3D printing, 83–4, 192, 198, 231, 264 thriving-in-balance, 54–7, 62 tiered pricing, 213–14 Tigray, Ethiopia, 226 time banking, 186 Titmuss, Richard, 118–19 Toffler, Alvin, 12, 80 Togo, 231, 292 Torekes, 236–7 Torras, Mariano, 209 Torvalds, Linus, 231 trade, 62, 68–9, 70, 89–90 trade unions, 82, 176, 189 trademarks, 195, 204 Transatlantic Trade and Investment Partnership (TTIP), 92 transport, 59 trickle-down economics, 111, 170 Triodos, 235 Turkey, 200 Tversky, Amos, 111 Twain, Mark, 178–9 U Uganda, 118, 125 Ulanowicz, Robert, 175 Ultimatum Game, 105, 117 unemployment, 36, 37, 276, 277–9 United Kingdom Big Bang (1986), 87 blood donation, 118 carbon dioxide emissions, 260 free trade, 90 global material footprints, 211 money creation, 182 MONIAC (Monetary National Income Analogue Computer), 64–5, 75, 142, 262 New Economics Foundation, 278, 283 poverty, 165, 166 prescription medicines, 123 wages, 188 United Nations, 55, 198, 204, 255, 258, 279 G77 bloc, 55 Human Development Index, 9, 279 Sustainable Development Goals, 24, 45 United States American Economic Association meeting (2015), 3 blood donation, 118 carbon dioxide emissions, 260 Congress, 36 Council of Economic Advisers, 6, 37 Earning by Learning, 120 Econ 101 course, 8, 77 Exxon Valdez oil spill (1989), 9 Federal Reserve, 87, 145, 146, 271, 282 free trade, 90 Glass–Steagall Act (1933), 87 greenhouse gas emissions, 153 global material footprint, 211 gross national product (GNP), 36–40 inequality, 170, 171 land-value tax, 73, 149, 180 political funding, 91–2, 171 poverty, 165, 166 productivity and employment, 193 rust belt, 90, 239 Transatlantic Trade and Investment Partnership (TTIP), 92 wages, 188 universal basic income, 200 University of Berkeley, 116 University of Denver, 160 urbanisation, 58–9 utility, 35, 98, 133 V values, 6, 23, 34, 35, 42, 117, 118, 121, 123–6 altruism, 100, 104 anthropocentric, 115 extrinsic, 115 fluid, 28, 102, 106–9 and networks, 110–11, 117, 118, 123, 124–6 and nudging, 112, 113, 114, 123–6 and pricing, 81, 120–23 Veblen, Thorstein, 82, 109, 111, 142 Venice, 195 verbal framing, 23 Verhulst, Pierre, 252 Victor, Peter, 270 Viner, Jacob, 34 virtuous cycles, 138, 148 visual framing, 23 Vitruvian Man, 13–14 Volkswagen, 215–16 W Wacharia, John, 186 Wall Street, 149, 234, 273 Wallich, Henry, 282 Walras, Léon, 131, 132, 133–4, 137 Ward, Barbara, 53 Warr, Benjamin, 263 water, 5, 9, 45, 46, 51, 54, 59, 79, 213–14 wave energy, 221 Ways of Seeing (Berger), 12, 281 Wealth of Nations, The (Smith), 74, 78, 96, 104 wealth ownership, 177–82 Weaver, Warren, 135–6 weightless economy, 261–2 WEIRD (Western, educated, industrialised, rich, democratic), 103–5, 110, 112, 115, 117, 282 West Bengal, India, 124, 178 West, Darrell, 171–2 wetlands, 7 whale hunting, 106 Wiedmann, Tommy, 210 Wikipedia, 82, 223 Wilkinson, Richard, 171 win–win trade, 62, 68, 89 wind energy, 75, 118, 196, 202–3, 221, 233, 239, 260, 263 Wizard of Oz, The, 241 Woelab, 231, 293 Wolf, Martin, 183, 266 women’s rights, 33, 57, 107, 160, 201 and core economy, 69, 79–81 education, 57, 124, 178, 198 and land ownership, 178 see also gender equality workers’ rights, 88, 91, 269 World 3 model, 154–5 World Bank, 6, 41, 119, 164, 168, 171, 206, 255, 258 World No Tobacco Day, 124 World Trade Organization, 6, 89 worldview, 22, 54, 115 X xenophobia, 266, 277, 286 Xenophon, 4, 32, 56–7, 160 Y Yandle, Bruce, 208 Yang, Yuan, 1–3, 289–90 yin yang, 54 Yousafzai, Malala, 124 YouTube, 192 Yunnan, China, 56 Z Zambia, 10 Zanzibar, 9 Zara, 276 Zeitvorsoge, 186–7 zero environmental impact, 217–18, 238, 241 zero-hour contracts, 88 zero-humans-required production, 192 zero-interest loans, 183 zero-marginal-cost revolution, 84, 191, 264 zero-waste manufacturing, 227 Zinn, Howard, 77 PICTURE ACKNOWLEDGEMENTS Illustrations are reproduced by kind permission of: archive.org

The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives
by Peter H. Diamandis and Steven Kotler
Published 28 Jan 2020

If solitary minds working in collectivist organizations—aka, business, culture, and society—produced converging exponential technologies—aka, the fastest innovation accelerant the world has yet seen—imagine what a hive-minded planet—aka, a kinder, gentler Borg—might be capable of creating. Put differently: How fast is our future if we’re all thinking together? And if you’ve come out the other side of all this thinking or feeling a little unsettled, there’s actually a technical term for this as well: loss aversion. One of our most potent cognitive biases, loss aversion is the evolutionarily programmed suspicion that if I take away whatever you have today, whatever I replace it with tomorrow will be a whole lot worse. This is why people stay stuck in ruts, it’s among the main reasons companies have such difficulty innovating, and why cultural change is so molasses slow.

Financial Freedom: A Proven Path to All the Money You Will Ever Need
by Grant Sabatier
Published 5 Feb 2019

I know people who are so afraid to invest that they just keep their money in a savings account making 1 percent or less, effectively losing money, since inflation rises 2 to 3 percent each year. Sure, you can lose money investing, but losing to inflation is a guaranteed loss! And if you invest intelligently, in most cases the upside is much greater than the downside over the long term. But we humans naturally fear losing more than we enjoy winning (a concept aptly known as loss aversion), which is why some people either don’t invest at all or get stuck on an emotional roller coaster—always chasing that next big gain or freaking out and making a rash decision on a decline. But investing isn’t gambling and there are ways to minimize the risks, as you’ll see in the section of this book on investing.

…

advertising, 20, 128–29, 174–75 side hustles and, 196, 200, 204 Amazon, 112, 153, 161, 181, 188 investing in, 228, 230–31, 256–57 assessments, 167, 270–73, 277, 285 assets, 21–22, 111, 147, 190, 309 allocation of, 40, 217–24, 235–38, 261 investing and, 2, 22, 211–12, 217–25, 235–38, 254, 261–62, 267–68, 276, 279, 288–89, 307 net worth and, 76–79, 82–83, 91 automation, 160, 306, 312 daily habits and, 89–90, 92 enterprise mindset and, 103, 112 investing and, 216–17, 258–59, 262, 302, 307, 312 banks, bank accounts, 9–10, 12, 40, 44, 51, 89–90, 95, 131, 190, 301 investing and, 214, 216, 258, 264–65, 268–69, 271, 273, 279, 283, 285 net worth and, 77–79 and paying down debt, 84–85 bartering, 58, 133, 145–47, 154 blended funds, 229, 238 blogs, blogging, 11, 34, 68–69 living richer life and, 313–15 side hustles and, 61, 109, 181, 187–88, 191–92, 220 bonds, 22 domestic, 237–38 international, 237–38 investing in, 114, 119, 211–12, 214–15, 217–24, 226, 228–29, 235–38, 254, 258, 260–62, 289–90 municipal (muni), 237 past performance of, 222–23 Trinity study and, 40–41 brokerage accounts, 95, 118 investing in, 79, 218, 228, 241–42, 251–52, 262 budgets, 11, 13, 140–41, 143, 147, 152, 154, 171, 281, 286 Buffett, Warren, 103, 255 cars, 58–59, 194, 313 expenses and, 21, 28, 75, 127–31, 133, 136–37, 141, 147–48 net worth and, 77, 79, 81–82 savings and, 141, 147, 154 your number and, 37, 52, 54 cash flow, 90, 147, 208, 306–7 investing and, 214, 263, 266–68, 276–78, 280–82, 286–89 certificates of deposit (CDs), 43, 214, 260 Chicago, Ill., 61, 132, 159, 175, 179, 205 housing and real estate in, 64–65, 67, 146, 271, 274, 279 clothing, 77, 122–23, 159, 180, 188 expenses and, 52, 58–59, 62, 65, 120, 127, 133, 136, 142–43 compounding, 59, 85, 176, 305 expenses and, 59, 75, 138 investing and, 22–23, 33, 35, 43–44, 46–49, 72–75, 84, 94, 96, 100, 108,114, 143, 169, 210, 237, 246–47, 258, 265, 285, 289–91, 296 job hacking and, 162, 169 money growth and, 2, 21–23, 33, 35, 76 retiring and, 21–24, 39, 43–44, 46–49, 61, 74 side hustles and, 108, 111, 185 your number and, 55–56, 59, 72–74 consulting, 21, 107, 123, 192, 194 cost of living, 63–65, 278, 312 credit, credit scores, 21, 85, 151, 190, 268–69, 271, 273–74, 307 credit cards, 7, 16, 51, 120, 130, 306 daily habits and, 89–90 net worth and, 77, 81–82 and paying down debt, 84–85 of Sabatier, 151–52, 214 side hustles and, 190, 192 transportation and, 148, 150–52 cryptocurrencies, 211, 256 daily habits, 17, 216, 305 expenses and, 54, 88–90, 135 riches and, 88–92 of Sabatier, 11, 90–91, 135 debts, debt, 15–16, 51, 57, 130, 307–8 investing and, 276–77 net worth and, 76–82, 91 paying down, 83–86, 88–89, 91–92 of Sabatier, 7–8, 84 your number and, 13, 36, 51 dependent care FSAs, 250–51 digital marketing, 20, 109, 124, 175, 181–82, 191, 193–94, 207 dividends, 22, 25, 46, 111, 220, 232, 235, 238, 252, 255, 264, 292, 297 dog walking, 104, 110, 133, 179, 181, 183–84, 188–89, 197–99, 201–2, 208 domain name flipping, 20, 109, 181–82 donations, 2–3, 52, 62, 65, 307, 316 down payments: low, 268–69, 271–72, 274 real estate investing and, 147, 263, 265–66, 268–74, 279, 285 education, 6, 12, 133, 181–82 expenses and, 55–56, 130, 147 investing and, 251, 278 living richer life and, 313–15 net worth and, 77, 81–82 and paying down debt, 84–85 side hustles and, 193–96, 209 student loans and, 8, 55–56, 77, 81–82, 84–85, 130, 147, 313 emergency funds, 43, 214, 273 emotions, 38, 86–89, 91–92, 207, 285, 316 employment, 1, 3, 5–11, 63, 77, 298 benefits of, 9, 14, 105–7, 156–61, 173, 177, 302 bonuses and, 159, 161, 163, 169, 259, 262 enterprise mindset and, 104–10, 119 equity in, 105, 160–61 full-time, 104–11, 113, 119, 122, 155–80, 183, 187, 191, 193–96, 203, 208, 250, 278, 290, 300 future-optimization framework and, 301–3 incomes and, 122–25, 139, 153, 179, 193, 203 investing and, 16, 21, 26, 86, 92, 156, 158, 214, 224, 226–28, 239–40, 245–46, 250, 259, 262, 271, 278, 287–88, 290 job hacking and, 14, 105–7, 155–78 lifestyle and, 57–58, 60–61 living richer life and, 312–15, 317 long-term strategy for, 174–77 and meanings of financial freedom, 15, 18 part-time, 36, 39, 61, 108, 297 promotions and, 58, 105, 161 raises and, 11, 14, 105, 107, 159, 162–73, 177–78, 300–302, 315, 317 and relationship between time and money, 32–34 retiring and, 19–21, 26, 29–31, 34–35, 39, 47, 60–61, 68 of Sabatier, 5–10, 33, 35–36, 58, 104, 124, 148, 151, 162, 175–76 Sabatier’s parents and, 28–29 savings and, 20–21, 26, 30, 95, 313, 315 short-term strategies for, 156–73 side hustles and, 104, 108–10, 179–80, 183, 187, 191–96, 203, 208, 300 and thinking about money before buying, 128–29, 131 transportation and, 147–48 unemployment and, 5, 7–8 working remotely and, 11, 14, 106–7, 156, 158–60, 165–66, 177, 187, 315 your market value and, 163–67, 169–71, 173–74, 177–78 your number and, 13, 36 and your value to your company, 163–64, 167–70, 173, 177–78 Enron, 231 enterprise mindset, 103–19, 155, 300 employment and, 104–10, 119 entrepreneurship and, 104, 108, 110–13, 119 investing and, 103–4, 106, 113–19, 180 side hustles and, 104, 108–13, 119, 180 entertainment, 53, 62, 65, 142–43, 189 entrepreneurship, 2, 10, 20 enterprise mindset and, 104, 108, 110–13, 119 side hustles and, 104, 110–13, 119, 189–90, 194, 200, 207 equity, 309 job hacking and, 105–6, 160–61 real estate and, 266, 268, 270, 273, 276–78 exchange traded funds (ETFs), 222, 226, 229, 232, 234, 237, 243, 258 expenses, 3, 11, 16, 31–32, 35–40, 96–103, 124–54, 300 affordability and, 17–18, 21, 25, 57, 63, 67, 105, 126–31, 139 budgeting and, 13, 140, 143, 147, 152 building wealth and, 93–94, 118 comparing price percentages and, 131–32, 134, 139 for convenience, 133–35, 139 daily habits and, 54, 88–90, 135 enterprise mindset and, 103, 105, 111, 118 future-optimization framework and, 306–8 and future value of money, 136–37, 139 happiness and, 125–27, 138–39 housing and, 3, 9–10, 21, 25, 52, 54, 58–61, 63–67, 127, 130–31,133–34, 140–47, 154 incomes and, 21, 50, 124, 127–33, 135, 142–44 inflation and, 24–25, 45 investing and, 47, 51, 59, 128, 131, 133, 136–37, 141–44, 214, 263, 267, 271–72, 276, 280–82, 284–85, 287, 289–92, 295, 297, 313 job hacking and, 105, 156, 168 lifestyle and, 17–18, 21, 57–63 living richer life and, 313, 315–17 net worth and, 83, 130 onetime future, 55–56 and paying down debt, 84–85 and per-use costs of items, 135–36, 139 purchasing power and, 24–27, 45, 127 recurring, 50, 54–55, 135, 138 retiring and, 21, 24–28, 39–40, 42–43, 45, 47–56, 60–61, 69–70, 74 of Sabatier, 7, 17–18, 38, 42–43, 58, 61–62, 132–36, 138, 307–8, 311–12 savings and, 42–43, 74, 96–101, 120–21, 126–28, 130, 132–33, 137–38, 140–44, 153–54 side hustles and, 14, 69–70, 188–90, 192, 199, 207, 209 and thinking about money before buying, 120–21, 125–39 trading and, 132–33, 139 value and, 120, 126–27 your number and, 13, 36–38, 50–57, 59–60, 62–66, 68–70, 75, 135, 137, 276 Facebook, 58, 189, 196, 304 investing in, 228, 256–57 Fidelity, 228, 232–33, 252 fiduciaries, 212 financial advisors, 11–12, 50, 87, 131 fees of, 225–28, 260, 296, 309 investing and, 210–12, 225–28, 235, 260, 296 financial freedom: levels of, 16–17 meanings of, 15–18, 36 529 plans, 251 flexible spending accounts (FSAs), 156–57, 177, 250–51 food, 2, 30, 38, 83, 180 expenses and, 25, 52, 54–55, 58, 61–62, 65–66, 132–36, 140–43, 151–54 inflation and, 25, 35 of Sabatier, 5–7, 132, 135, 153 savings and, 142–43, 152–54 your number and, 52, 54–55 foreclosures, 283, 286 four fund investments, 235–36 401(k) accounts: employer contributions to, 21, 26, 86, 92, 156, 158, 239–40, 245–46 fees of, 224, 226–29 investing in, 79, 216, 218, 223–24, 226–29, 236–47, 249–51, 254,258–59, 262, 293–95, 298–99, 301–2 job hacking and, 105, 156–58 retiring and, 20–21, 26–28, 45 savings rates and, 95, 118 403(b) accounts, 224, 228–29, 236–37, 241–46, 249, 254, 262, 293, 295, 298 457(b) accounts, 241–45, 251, 254, 293, 298 frugality, 28, 35, 38, 314 future-optimization framework, 300–310 chilling and hustling in, 309–10 executing consistently in, 305–8, 310 focusing intensely and learning to say no in, 303–5, 310 just getting started in, 301–3, 310 principles of, 301–9 sharing and asking for help in, 308–10 geographic arbitrage, 67 Glassdoor, 21, 165, 177 gold, investing in, 229, 237, 262 Google, 9, 107, 149, 161, 166, 175 investing and, 233, 237, 284 side hustles and, 182, 189, 200 Graham, Benjamin, 255 Hamptons, N.Y., 63 health, healthcare, 1–2, 8, 19, 25, 33, 64, 297, 316 expenses and, 53, 58, 65, 142 insurance and, 53, 105, 156–57, 247 investing and, 234, 241, 243, 246–47, 250–52 job hacking and, 156–57, 177 of Sabatier, 309–10 your number and, 53, 56 health savings accounts (HSAs), 156–57, 177, 241, 243, 246–47, 250–52, 293, 298 hobbies, 34, 53, 189, 191, 195–96 home inspectors, 284, 286 homes, housing, 2, 9–11, 30, 38, 179 affordability of, 49, 270–75 and cost of living, 63–65 expenses and, 3, 9–10, 21, 25, 52, 54, 58–61, 63–67, 127, 130–31, 133–34, 140–47, 154 hacking of, 145–47, 154, 264, 285 inflation and, 25, 35, 45, 67 investing and, 213, 263–64, 270–75, 279, 283–84 lifestyle and, 58–60, 62–63, 65–66 living rent-free and, 144–47 living richer life and, 313–15 location and, 63–67 net worth and, 76, 79, 83, 91 renting vs. buying of, 266–68, 302 retiring and, 45, 49, 52, 54, 56, 60, 144 of Sabatier, 5–7, 9–10, 61, 63–64, 84, 143–44, 147, 274–75 savings and, 63, 67, 141–47, 154 your number and, 52, 54, 66–67 see also real estate house-sitting, 58, 63, 145–47, 154 incomes, 1–3, 7–17, 26–39, 86–87, 90–119, 148, 300 building wealth and, 93–94, 118 compounding and, 94, 111, 108 daily habits and, 90, 92 employment and, 122–25, 139, 153, 179, 193, 203 enterprise mindset and, 103–19 expenses and, 21, 50, 124, 127–33, 135, 142–44 food and, 152–53 future-optimization framework and, 303, 306–10 housing and, 143–44, 146–47 investing and, 8, 16, 30, 43, 82–83, 91, 104, 114–19, 179, 181, 210–16, 218, 220, 224–25, 230, 233–35, 237, 239–41, 243–54, 258–60, 262, 270–71, 275–80, 285, 287–94, 296–98, 314 job hacking and, 14, 105–6, 155–57, 159–60, 162–80 lifestyle and, 57, 59–60 living richer life and, 312–15, 317–18 net worth and, 77, 83, 91 passive, 14–15, 34, 39, 43, 74, 109, 111–14, 119, 188–89, 192, 209–10,260, 278, 289, 298, 306–7 real hourly rates of, 121–25, 129, 134–35, 139, 307 recurring, 68–70, 75, 109 and relationship between time and money, 32–34 rental, 43, 68–69, 83, 111, 147, 189, 266, 271, 276–79, 288–89, 306 retiring and, 20–21, 24, 26–28, 30, 35, 39, 42–43, 46, 50, 60–61, 69, 74 of Sabatier, 1–2, 7–10, 17, 20, 30, 38, 42–43, 103–4, 118, 124, 135, 175–76, 181, 183, 194, 202, 205, 214, 217, 220, 302, 307–9, 311 savings and, 20–21, 24, 26–28, 33, 94–101, 108, 115–18, 170, 258, 312–13, 315 side hustles and, 9, 14, 42, 68–70, 74, 95, 104, 108–13, 119, 122, 153, 175–76, 179–84, 187–209, 216, 220, 289–91, 298, 306 your number and, 36–39, 68–70, 74, 82–83, 91, 169, 180, 189 index funds, 24, 230–38 building your own, 236–38, 261 dividends of, 232, 235, 238 fees of, 225–26, 228, 232, 234–36, 238 international, 235–36 investing in, 215, 222–23, 225–26, 228, 232–38, 254–57, 261–62 large-cap, 236–38 mid-cap, 236–38 most popular, 232–33, 237 returns of, 215, 222–23 small-cap, 236–38 socially responsible, 234–35 individual retirement accounts (IRAs), 28, 95, 118 investing in, 223, 226, 228, 237, 241–43, 246–49, 254–55, 262, 293, 295–96, 298–99, 302 opening and maxing out of, 247–49 see also specific individual retirement accounts inflation: housing and, 25, 35, 45, 67 investing and, 22, 25, 35, 39–42, 46, 114, 138, 213–14, 219, 264, 290 lifestyle and, 57, 62 retiring and, 24–28, 40–42, 45–46, 94 savings and, 8–9, 24–28 and value of money, 25–27, 35, 43, 88 your number and, 36, 56 insurance, 313 expenses and, 52–53, 62, 65, 142, 148 healthcare and, 53, 105, 156–57, 247 housing and, 65, 273–74 investing and, 267, 273–74 job hacking and, 105, 156–57, 177 Intelligent Investor, The (Graham), 255 interest, 88–89, 131, 147, 313 compounding and, 22–23, 55, 59, 73–74, 169, 176 investing and, 85–86, 108, 115–17, 179, 214–15, 220–23, 239–40, 266, 268–76, 281 net worth and, 77–82 and paying down debt, 84–86, 89, 92 retiring and, 24, 26, 28, 45, 47–49, 51 savings and, 88, 143–44 your number and, 51, 55–56 Internal Revenue Service (IRS), 49n, 213, 248, 268, 293 internet, 30, 52, 103, 109, 123, 156, 181 investments, investing, 2–3, 8–12, 14, 30–31, 33–37, 90, 184, 210–302 automation and, 216–17, 258–59, 262, 302, 307, 312 compounding and, 22–23, 33, 35, 43–44, 46–49, 72–75, 84, 94, 96, 100, 108, 114, 169, 210, 237, 246–47, 258, 265, 285, 289–91, 296 determining target asset allocation in, 217–24, 261 dividends and, 22, 25, 46, 111, 220, 232, 235, 238, 252, 255, 264, 292, 297 emergency funds and, 43, 214 emotions and, 86–88, 285 enterprise mindset and, 103–4, 106, 113–19, 180 expenses and, 47, 51, 59, 128, 131, 133, 136–37, 141–44, 214, 263, 267, 271–72, 276, 280–82, 284–85, 287, 289–92, 295, 297, 313 fast-track strategy for, 210–62 fees of, 211–12, 224–38, 252, 260–61, 267, 296, 300, 307 figuring out how much money you have for, 216–17, 260–61 financial freedom levels and, 16–17 future-optimization framework and, 301–2, 306–8 gambling vs., 210–11 how to contribute to, 244–51 inflation and, 22, 25, 35, 39–42, 46, 114, 138, 213–14, 219, 264, 290 international, 216, 229–30, 235–36 job hacking and, 106, 156–58, 161–62, 168–69 lifestyle and, 57, 59–61, 259, 267, 278 living off them, 287–99, 312, 314 living richer life and, 312–17 long-term, 22, 213, 216, 230–32, 253–55, 260, 275–78, 286, 289 maxing out of, 211, 213, 239–51, 262 net worth and, 76–79, 82–83, 91, 108, 146, 223, 256, 268, 273, 276 and paying down debt, 84–86, 88, 92 rates of, 216–18, 259, 262, 302 real estate and, 2, 43, 119, 131, 146–47, 212–13, 237, 256, 260,262–86, 289, 300, 302 rebalancing of, 223–24, 236, 238, 261, 307 retiring and, 8–9, 21–28, 39–44, 46–51, 54–56, 60–61, 74–75, 179, 212, 215–22, 226, 229, 239, 242, 244–48, 251, 275, 287–92, 294–99 returns of, 22, 43–44, 46, 211, 213, 215, 222–23, 228, 239–40, 243, 245–46, 252, 254–55, 257–62, 264–66, 271–72, 274, 280, 285, 289–92, 294, 296, 300, 312–13 of Sabatier, 9–10, 30, 36, 95, 108, 114–18, 144, 181–83, 213–14,216–17, 220, 236, 256–57, 274–75, 285–86, 308 savings and, 95, 114–19, 143–44, 153–54, 213–17, 225, 239, 254, 258, 267–68, 273, 285, 287 selection of, 228–31, 254–55, 261–62 short-term, 213–16, 230, 260, 275, 286 side hustles and, 69–70, 95, 108, 113, 179–83, 187–88, 190, 192, 208, 216, 218, 220, 249–50, 259, 262, 264, 289–91, 298 taxes and, 46, 114, 211–15, 218, 224, 228, 232, 237, 239–55, 260, 262–68, 270–72, 275–77, 281–82, 285, 288, 291–300, 315 withdrawals from, 39–44, 46–49, 51, 54–56, 61, 66, 68–70, 239–45, 247–48, 251–54, 260, 262, 287–99 your number and, 37, 47, 49–51, 54–56, 66, 68–70, 72–74, 82–83, 91, 137, 179, 181, 210, 213, 216, 221, 259–60, 275–76, 287–89 see also under bonds; brokerage accounts; incomes; stocks, stock market iShares, 232, 234 Kelly, Brian (The Points Guy), 192 lawyers, law firms, 95, 144, 167, 193–94, 205, 224, 280, 313–14 website building for, 181–82, 194, 202 liabilities, 76–82, 91, 265 lifestyle, 28, 74, 158 expenses and, 17–18, 21, 57–63 housing and, 58–60, 62–63, 65–66 investing and, 57, 59–61, 259, 267, 278 of Sabatier, 9, 61–62 side hustles and, 111, 113, 191, 207–9 your number and, 15, 37, 59–60, 62 limited liability companies (LLCs), 179, 189–90, 309 load fees, 224 loans, 89, 151, 215 expenses and, 130–31, 147 investing and, 260, 263–64, 268–69, 271, 273, 279, 281, 285–86 net worth and, 77, 81–82 paying down, 84–85 preapproved, 281, 286 to students, 8, 55–56, 77, 81–82, 84–85, 130, 147, 313 Los Angeles, Calif., 148, 159, 271–72, 282 loss aversion, 88 Millennial Money, 11, 107, 146, 156, 164, 166, 168–69, 182 model portfolios, 229 Money Talk Cards, 305, 308 mopeds, 20, 109, 148, 182 mortgages, 52, 58, 66, 81–84, 131, 151 adjustable rate (ARMs), 268–70 fifteen–year, 269–70, 274 fixed rate (FRMs), 268–69, 274 investing and, 263–64, 266–77, 281, 283, 285–86 net worth and, 77, 81–82 preapproved, 281, 286 savings and, 146, 154 thirty-year, 269–70 mutual funds, 226, 229, 241, 243, 258 net worth, 13, 76–83, 122, 300, 312 calculation of, 76–77, 91 definition of, 82–83, 91 future-optimization framework and, 305–6, 308 investing and, 76–79, 82–83, 91, 108, 146, 223, 256, 268, 273, 276 negative, 82, 130–31 real estate and, 77–79, 83, 146 of Sabatier, 9–10, 82, 108, 114 savings and, 76, 79, 82, 146 side hustles and, 77, 108 and thinking about money before buying, 130–31, 137 your number and, 82–83, 91 New York City, 15, 150, 152, 166 housing and real estate in, 63–67, 271, 282, 315 1 percent rule, 281–82 passions, 2–3, 9, 33–35, 127 analysis of, 191–94, 209 in retirement, 297, 299 side hustles and, 11, 188, 191–96, 209 present value formula, 55–56 private mortgage insurance (PMI), 273–74 real estate: affordability of, 267, 270–75 and being prepared to walk away from deals, 285–86 buying and holding, 275, 286 case for, 264–66 criteria to follow for, 280–81, 285–86 finding properties and, 280–86 flipping of, 275–76, 286 with high rent appreciation potential, 281–82, 286 investing and, 2, 43, 119, 131, 146–47, 212–13, 237, 256, 260, 262–86, 289, 300, 302 net worth and, 77–79, 83, 146 refinancing of, 268, 270, 273 scaling in, 278–80, 286 test driving neighborhoods and, 284, 286 and thinking about money before buying, 127, 130–31, 133 see also homes, housing real estate agents, 182, 194, 283 real estate investment trusts (REITs), 237, 262 Realtors, 194, 279, 281–83, 286 recruiters, recruiting, 163, 165–66, 170, 173, 177, 192 retiring, retirement, 1, 8–12, 18–31, 34–36, 38–56, 308, 310 housing and, 45, 49, 52, 54, 60, 144 how much money actually needed for, 44–49 inflation and, 24–28, 40–42, 45–46, 94 investing and, 8–9, 21–28, 39–44, 46–51, 54–56, 60–61, 74–75, 179, 212, 215–22, 226, 229, 239, 242, 244–48, 251, 275, 287–92, 294–99 with less money at thirty than at sixty, 38–39, 47, 54 lifestyle and, 60–61, 74 living richer life and, 313, 315–16 and money for rest of your life, 42–44 and paying down debt, 84, 86 rewriting of, 28–31 of Sabatier, 8, 35, 45, 275 savings and, 8–9, 11, 20–22, 24–30, 38–40, 44–49, 51, 54–56, 60–61, 74, 94–96, 141, 144, 251 side hustles and, 43–44, 61, 69–70, 74, 179, 188, 192 time and, 19, 27, 45–47, 49n, 297–99 Trinity study and, 39–44 your number and, 36, 49–56, 68–70, 72–74 ride-sharing, 52, 109, 148 side hustles and, 183, 187, 190 risks, risk, 10, 16, 40, 49, 309 daily habits and, 89, 91–92 emotions and, 87–88 investing and, 14, 87, 106, 113–14, 211, 213, 215, 217–23, 229–30, 235, 237–38, 255–56, 260–61, 263–64, 269, 271, 279, 285, 289–91, 301, 317 side hustles and, 108, 112–13, 194, 202, 204 Robin, Vicki, 1–3, 32 robo-advisors, 212 Roth 401(k) accounts, 241, 243–45, 294, 298 Roth IRA accounts: conversion ladder for, 295, 298–99 investing in, 79, 95, 214, 218, 228, 241, 243–44, 247–49, 251, 258, 294–96, 298–99, 302 Rule of 72, 59, 75 Run the Trap, 191 S&P 500, 228, 232–38, 261 San Francisco, Calif., 64, 271, 282 savings, saving, 3, 5–11, 140–54 building wealth and, 93–94, 118 compounding and, 55, 59, 72, 94, 96, 143 daily habits and, 88–90 emotions and, 87–88 enterprise mindset and, 103, 107, 113–17 expenses and, 42–43, 74, 96–101, 120–21, 126–28, 130, 132–33, 137–38,140–44, 153–54 financial freedom levels and, 16–17 food and, 142–43, 152–54 future-optimization framework and, 301, 303, 305–6 housing and, 63, 67, 141–47, 154 inflation and, 8–9, 24–28 investing and, 95, 114–19, 143–44, 153–54, 213–17, 225, 239, 254, 258, 267–68, 273, 285, 287 job hacking and, 107, 159–60, 163, 170, 177 lifestyle and, 57–58, 60–61 living richer life and, 312–17 net worth and, 76, 79, 82, 146 rates of, 11, 13, 20–21, 24, 26–28, 33, 89, 94–101, 108, 114–19, 141,143, 213, 216–18, 256–58, 260, 300, 302–3, 305–6, 312–15, 317 retiring and, 8–9, 11, 20–22, 24–30, 38–40, 44–49, 51, 54–56, 60–61, 74, 94–96, 141, 144, 251 of Sabatier, 5–7, 9–11, 30, 45, 63, 95, 138, 143–44, 148, 151–53, 216–17, 256–57, 300, 302, 311–12 side hustles and, 113, 179 time and, 32–33, 94, 96–101, 118 transportation and, 141–43, 147–52, 154 your number and, 36–37, 39, 51, 54–56, 59, 68, 70–75, 94–95, 97–101, 118, 137, 141 see also health savings accounts saying no, 127, 208, 303–5, 310, 312 Charles Schwab, 232–33, 252 search engine optimization (SEO) projects, 175, 182 sequence-of-returns risk, 290–91 short sales, 283, 286 side hustles, 11, 14, 90, 153, 179–209, 300, 317 benefits of, 109–10, 185, 189 competitive analysis for, 200–203, 205, 209 enterprise mindset and, 104, 108–13, 119, 180 evaluation framework for, 190–209 in evenings, 186 figuring out what to charge for, 201–4, 209 future-optimization framework and, 301–3, 306 getting your first sale and, 204–6 hiring others for, 109, 111–13, 119, 206–7 in in-between moments, 187–89 incomes and, see under incomes investing and, 69–70, 95, 108, 113, 179–83, 187–88, 190, 192, 208, 216, 218, 220, 249–50, 259, 262, 264, 289–91, 298 job hacking and, 156, 160, 175 and knowing when to scale, 206–9 LLCs and, 179, 189–90 in mornings, 185 net worth and, 77, 108 retiring and, 43–44, 61, 69–70, 74, 179, 188, 192 of Sabatier, 9, 20, 58, 95, 104, 108–9, 175–76, 181–83, 185–86, 188, 194–95, 202–3, 205, 207–8 skills and, 109, 119, 175, 190–97, 201–2, 209 supply and demand for, 179, 182, 189, 194, 197–204, 206, 209 taxes and, 189–90, 249 time and, 108, 111, 180–87, 190, 199–200, 202–3, 207–9, 303 transportation and, 148, 187, 189, 192–93, 196, 208 on weekends, 186 and working for someone else vs. for yourself, 183–84, 207–9 your number and, 69–70, 179–81, 189, 209 your story in, 204–5 simplified employee pension individual retirement accounts (SEP IRAs), 79, 218, 228, 241, 243, 249–51 skills, 10, 51, 104, 133 analysis of, 191–94, 209 job hacking and, 14, 105, 156, 159, 164–66, 169, 174–78 learning new ones, 195–96, 209 retiring and, 34, 44 side hustles and, 109, 119, 175, 190–97, 201–2, 209 Social Security, 8, 26, 45n, 249, 269 expenses and, 53, 128, 141–42 Solo 401(k) accounts, 241, 243, 249–50 stocks, stock market, 160 buying individual, 254–57 compounding and, 22, 72 dividends paid by, 22, 25, 46, 111 emotions and, 86–87 international, 216, 229–30, 235–36 investing in, 2, 9, 17, 24–26, 34, 39–42, 46–47, 84, 86–87, 104, 114, 119, 162, 211–26, 228–38, 241–42, 244, 252–58, 260–62, 264–65, 273–79, 285, 287, 289–91, 297 past performance of, 222–23 and paying down debt, 84–86 retiring and, 39–44, 46–49 selection of, 228–29, 230–31, 255, 261 your number and, 36, 72 target date funds, 228–29, 238 taxes, 59, 64, 77, 83–84, 88, 102, 143 deductions and, 84, 128, 189–90, 240–42, 245–52, 254, 266–68, 276, 294,296–98 enterprise mindset and, 103, 114, 118 future-optimization framework and, 307–9 inflation and, 25, 45 investing and, 46, 114, 211–15, 218, 224, 228, 232, 235, 237, 239–55, 260, 262–68, 270–72, 275–77, 281–82, 285, 288, 291–99, 300, 315 job hacking and, 157–58 real hourly income rates and, 122–25, 129 retiring and, 19, 27, 45–47, 49n, 297–99 of Sabatier, 7–8 savings rates and, 95–96, 118 side hustles and, 189–90, 249 and thinking about money before buying, 120, 127–30 your number and, 52–53, 68–69 1031 exchanges, 264–65, 275 three fund investments, 235–36 time, 2, 10, 63, 153 compounding and, 22, 33, 305 daily habits and, 90–91 enterprise mindset and, 103, 105–6, 108, 111, 118–19, 300 expenses and, 32, 75, 129–30, 133–39 future-optimization framework and, 301, 303–5, 307–10 investing and, 33–34, 118, 133, 210, 212, 214–15, 224, 239, 283, 308 job hacking and, 105–6, 155–56, 160–64, 166–68, 170–73, 177–78 living richer life and, 312, 314–17 real hourly income rates and, 121–25, 129, 134–35, 139, 307 relationship between money and, 19, 32–35, 106, 111, 113, 121, 129, 133, 184, 188, 207–10, 303–4, 307–8, 310 retirement and, 19, 27, 45–47, 49n, 297–99 savings and, 32–33, 94, 96–101, 118 side hustles and, 108, 111, 180–87, 190, 199–200, 202–3, 207–9, 303 transportation and, 32–33, 148 Top Five Regrets of the Dying, The (Ware), 29–30 total stock market funds, see index funds trading, 132–33, 139 transportation, 32–35, 64, 91, 213, 297, 299, 313 expenses and, 25, 52, 54, 58–59, 61–62, 65–66, 75, 127, 140–43, 147–52, 154 housing and, 65–66, 145 job hacking and, 156–60 lifestyle and, 58–59, 61–62 real hourly income rates and, 122–25, 139 of Sabatier, 148, 151–52 savings and, 141–43, 147–52, 154 side hustles and, 148, 187, 189, 192–93, 196, 208 time and, 32–33, 148 travel-hacking and, 148–52, 154 your number and, 52, 54, 59 Trinity study, 39–44 vacations, 6, 9, 17, 28, 34, 52, 91, 105, 156, 187, 192, 278 expenses and, 130–31, 147 valuables, 52, 77–79, 83 value investing, 255 Vanguard, 212, 215, 234, 252 500 Index Fund Admiral Shares (VFIAX), 232–33 500 Index Fund Investor Shares (VFINX), 228, 232–33 Total Stock Market ETF (VTI), 222, 226, 232 Total Stock Market Index Fund (VTSAX), 228, 232–33, 257 Volkswagen (VW) campers, 8, 109, 182 Wang, Jim, 192 Ware, Bronnie, 29–30 wealth, 2–3, 16, 28, 38, 230 building of, 6, 11–12, 76, 86, 88, 93–94, 103, 118 daily habits and, 88–90 emotions and, 86, 88 job hacking and, 155, 162 net worth and, 76, 82 website building, 20, 58, 104, 175, 194–95, 201–3, 205, 207 for law firms, 181–82, 194, 202 We Need Diverse Books, 314–15 Your Money or Your Life (Robin and Dominguez), 2–3, 32 your number, 77, 307 breaking it down into smaller goals, 70–76 calculation and recalculation of, 13, 15, 44, 49–57, 66–69, 72, 75–76,82–83, 288–89, 300 definition of, 13, 82, 91 expenses and, 13, 36–38, 50–57, 59–60, 62–66, 68–70, 75, 135, 137, 276 housing and, 52, 54, 66–67 incomes and, 36–39, 68–70, 74, 82–83, 91, 169, 180, 189 investing and, 37, 47, 49–51, 54–56, 66, 68–70, 72–74, 82–83, 91, 137, 179, 181, 210, 213, 216, 221, 259–60, 275–76, 287–89 job hacking and, 155, 169, 174 lifestyle and, 15, 37, 59–60, 62 living richer life and, 312, 316 net worth and, 82–83, 91 of Sabatier, 12–13, 36 savings and, 36–37, 39, 51, 54–56, 59, 68, 70–75, 94–95, 97–101, 118, 137, 141 side hustles and, 69–70, 179–81, 189, 208 ABCDEFGHIJKLMNOPQRSTUVWXYZ ABOUT THE AUTHOR Grant Sabatier, called "The Millennial Millionaire" by CNBC, is the Founder of MillennialMoney.com, which has reached over 10 million readers.

Aftershock: The Next Economy and America's Future
by Robert B. Reich
Published 21 Sep 2010

Millar Publishing, 1790), pp. 261–63. 9 Almost 10 percent fewer people were killed: See National Highway Traffic Safety Administration Fatality Analysis Reporting System Encyclopedia (http://www-fars.nhtsa.dot.gov/Main/index.aspx). 10 deaths and serious injuries dropped: See U.S. Bureau of Labor Statistics, economic news release: “Workplace Injury and Illness Summary,” October 29, 2009. 4. THE PAIN OF ECONOMIC LOSS 1 Princeton psychologist Daniel Kahneman: See D. Kahneman, J. L. Knetch, and R. H. Thaler, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias,” Journal of Economic Perspectives, 5, no. 1 (Winter 1991): 193–206. 2 Societies whose living standards drop: Ibid. 3 Behavioral economist Christopher Ruhm: See C. J. Ruhm, Are Recessions Good for Your Health?, National Bureau of Economic Research, March 2006. 4 The stock market crash of 1929: See Leonardo Tondo and Ross J.

Radical Uncertainty: Decision-Making for an Unknowable Future
by Mervyn King and John Kay
Published 5 Mar 2020

Many more economists studied what became known as ‘behavioural economics’, which offers a list of widely observed ‘biases’ in people’s behaviour. These studies claim that we suffer from optimism and overconfidence, and we overestimate the likelihood of favourable outcomes. We are guilty of anchoring: attaching too much weight to the limited information we hold when we start to analyse a problem. We are victims of loss aversion: treating losses with a concern not given to equivalent gains. And so on. While Allais, Ellsberg and Simon regarded their observations as a rebuttal of the view of decision-making under uncertainty put forward by Friedman and Savage, the approach pioneered by Kahneman and Tversky adopted a markedly different stance.

…

And the irrational ‘biases’ identified by behavioural economists as being ‘in our nature’ are not, in any ordinary meaning of the term, irrational. They are traits that are advantageous outside the ‘small worlds’ of the casino and the psychology laboratory. And they have evolutionary origins. Loss aversion Evolution has fitted humans to deal with the many kinds of radical uncertainty encountered in large worlds. Different attitudes to uncertainty influence the chances of survival of individuals and groups. In some environments, such as business and sport, to play safe is to relinquish the possibility of success.

Economics Rules: The Rights and Wrongs of the Dismal Science
by Dani Rodrik
Published 12 Oct 2015

The postulate always had its critics from within economics, such as Herbert Simon, who argued for a limited form of rationality (called “bounded rationality”), and Richard Nelson, who proposed that firms move by trial and error rather than by optimization—not to mention Adam Smith himself, who may have been the first behavioral economist.11 But it was the work of psychologist Daniel Kahneman and his coauthors that had the greatest impact on mainstream economics.12 This contribution was recognized by a Nobel memorial prize in economics given to Kahneman in 2002, the first time that the prize was awarded to a noneconomist.# Kahneman and his colleagues’ experiments cataloged a long list of behavioral regularities that violated rationality, as the concept is used in economics. People value an object more when giving it up than they do when acquiring it (loss aversion), overgeneralize from small amounts of data (overconfidence), discount evidence that contradicts their beliefs (confirmation bias), yield to short-term temptations that they realize are bad for them (weak self-control), value fairness and reciprocity (bounded selfishness), and so on. These types of behavior have important implications in many areas of economics.

The Penguin and the Leviathan: How Cooperation Triumphs Over Self-Interest
by Yochai Benkler
Published 8 Aug 2011

Amos Tversky and Daniel Kahneman, the fathers of behavioral economics, explain that people will make different decisions depending on how a situation is presented. For example, when making a bet, people will risk different amounts depending on whether the bet is described as risking a loss or aiming for a gain (behavioral economists have found that people display what is often called “loss aversion”: they will reject bets framed as potential losses, but accept that same bet when it is framed as potential gains). Countless experiments have demonstrated equally powerful framing effects in a wide range of contexts. While “framing” is popularly known today through these kinds of “irrationalities,” the situation and its impact on what we want and what we can or should do is a long-standing component of social psychology.

Meet the Frugalwoods: Achieving Financial Independence Through Simple Living
by Elizabeth Willard Thames
Published 6 Mar 2018

Plus, all of our savings were stacked one on top of the other, which is how you create an extremely frugal lifestyle. We weren’t saving just $470 on seltzer per year, we were saving $470 plus the $3,456 on yoga plus $1,008 on haircuts . . . and on and on and on until we were saving thousands upon thousands of dollars every single year. Forever. There’s a theory in behavioral economics related to loss aversion positing that once we acclimate to a certain level of luxury or ownership in our lives—be it seltzer or expensive yoga classes—we find it nearly impossible to then live without this luxury. Giving these things up feels like deprivation because we’ve acclimated ourselves to their presence in our lives.

Think Twice: Harnessing the Power of Counterintuition
by Michael J. Mauboussin
Published 6 Nov 2012

Likewise, skillful people who have suffered a period of poor outcomes are often a good bet, since luck evens out over time.11 Picking the Main Mistakes We Professionals Make The primary audience for this book is investors and businesspeople, although the concepts are relevant for other professionals as well. This book is neither a survey of common mistakes nor an exposition of one big theme. For instance, most books focus either on the components of prospect theory (loss aversion, overconfidence, framing effects, anchoring, and the confirmation bias) or they dwell on one important idea.12 Rather, I have tried to select the concepts that I have found most useful, based on my experience in the investment industry and through my study of psychology and science. Each of the following chapters discusses a common decision mistake, illustrates why that mistake is consequential, and offers some thoughts on how to manage the problem.

The Self-Made Billionaire Effect: How Extreme Producers Create Massive Value
by John Sviokla and Mitch Cohen
Published 30 Dec 2014

The Nobel Prize winner Daniel Kahneman and his research partner Amos Tversky first proposed the subjective nature of risk in a 1979 paper in which they describe a series of experiments they conducted to come up with their famous Prospect Theory, a model for human decision making. At its core, Prospect Theory argues that individual perceptions of risk can be influenced by how an opportunity is framed, the context in which it is presented, personal experience, and other factors. Among other ideas, Prospect Theory first introduced the world to the concept of loss aversion, the now-accepted notion that people are more afraid of losing what they have than they are eager to gain something new.5 For most people, the subjective nature of risk causes them to overestimate the risk of failure and underestimate the risk of missing out on a gain. Producers, in contrast, have the ability to turn that tendency on its head.

Designing the Mind: The Principles of Psychitecture
by Designing The Mind and Ryan A Bush
Published 10 Jan 2021

Our present evaluations, even the ones we reflect deeply on, are much less coherent than we tend to think. We overvalue information which is readily available (availability bias), presented first (primacy bias), frequently (frequency bias), or recently (recency bias). And various expressions of loss aversion, such as the endowment effect cause us to demand more to give something up than we would pay to acquire it. Homo sapiens is a storytelling animal, that thinks in stories rather than in numbers or graphs, and believes that the universe itself works like a story, replete with heroes and villains, conflicts and resolutions, climaxes and happy endings

Spite: The Upside of Your Dark Side
by Simon McCarthy-Jones
Published 12 Apr 2021

Sitkin and his colleagues argue that companies should undertake stretch goals when they are already well positioned and on a winning streak. A company that attempts stretch goals when it is weak communicates fear and desperation. Unfortunately, this is when management may be most likely to attempt them. To make this point, Sitkin draws on the psychological literature on loss aversion and decision-making. Psychologists Daniel Kahneman and Amos Tversky famously showed that failure makes people more inclined to take risks to dig themselves out of a hole.53 As a result, struggling firms are more likely to take risky actions. The other implication of Kahneman and Tversky’s work is that successful firms are likely to be more risk averse, despite the fact that they are the ones with the resources and motivation to fruitfully take risks and achieve stretch goals.

Rapt: Attention and the Focused Life
by Winifred Gallagher
Published 9 Mar 2009

“If you focus too much on each issue separately, considering each loss and gain in isolation, you make mistakes.” If you’re pondering a choice that involves risk, you might focus too much on the threat of possible loss, thereby obscuring an even likelier potential benefit. Where this common scenario is concerned, research shows that we aren’t so much risk-averse as loss-averse, in that we’re generally much more sensitive to what we might have to give up than to what we might gain. Let’s say that you’re invited to toss a coin. The terms are that if it’s tails, you lose twenty dollars; heads, you win a certain amount. If you’re then asked how much your winnings would have to be to make you take the chance, you’re likely to suggest between forty and fifty dollars.

First Light: Switching on Stars at the Dawn of Time
by Emma Chapman
Published 23 Feb 2021

Philosophical Transactions of the Royal Society of London: 893–894. 26 Green, L. 2017. 15 Million Degrees. Penguin, London, UK. 27 Gamow, G. 1967. A Star Called the Sun. Pelican, London, UK. Chapter 2: Where is Population III? 1 https://abcnews.go.com/Business/beanie-babies-mania-ends-bankruptcy/story?id=19785126. 2 Kahneman, D. et al. 1991. Anomalies: the endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, vol. 5, no. 1: 193–206. 3 Davis, W. 2009. The Wayfinders: Why Ancient Wisdom Matters in the Modern World. House of Anansi Press Ltd, Canada. 4 Dempsey, F. 2009. Aboriginal sky lore of the constellation Orion in North America. Journal of the Royal Astronomical Society of Canada, vol. 103, no. 2: 65. 5 Sobel, D. 2017.

The Happiness Curve: Why Life Gets Better After 50
by Jonathan Rauch
Published 30 Apr 2018

Seligman, whose 2002 book Authentic Happiness: Using the New Positive Psychology to Realize Your Potential for Lasting Fulfillment (Atria Books) provides much insight, as well as the useful happiness formula which I adapted. The 1990 experiment by Kahneman, Knetsch, and Thaler on endowment bias is reported in “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias,” published in the Journal of Economic Perspectives 5:1 (1991). Tali Sharot and various collaborators have published extensively on optimism bias. Sharot lays out her findings concisely and readably in her ebook The Science of Optimism. Other Sharot work I consulted includes “The Optimism Bias” in Current Biology 21:23 (2011); “Neural Mechanisms Mediating Optimism Bias,” coauthored with Alison Riccardi, Candace Raio, and Elizabeth Phelps, in Nature 450 (2007); “Selectively Altering Belief Formation in the Human Brain,” coauthored with Ryota Kanai, David Marston, Christoph W.

Transport for Humans: Are We Nearly There Yet?
by Pete Dyson and Rory Sutherland
Published 15 Jan 2021

We’ve established that HS2 should make it fast and easy to get to the station, with travel time calculators based on anticipated congestion, recommended routes and back-up options. Why leave it to us? To minimize the chance of actually missing the train, and the perceived stress caused by even considering that this might happen, most people arrive early. When the stakes are high, we are loss averse. High-speed rail across the world mimics air travel by using pre-booked tickets and advance fares to handle yield management. We save money by buying a ticket that is valid on only one train, meaning we need ‘buffer time’ to get to the station, and most of the time this means we are left to hang around when we get there.

What Got You Here Won't Get You There: How Successful People Become Even More Successful
by Marshall Goldsmith and Mark Reiter
Published 9 Jan 2007

Apparently, he was making enough money so that an additional $500 a month didn’t make that much difference to him. It was no different when the entrepreneur increased the bonus to $3000. Still no improvement. Only when the boss resorted to deducting $3000 from the writer’s paycheck did he change his ways. Economists would call this “loss aversion”—the phenomenon that we hate losing something more than we enjoy gaining its equivalent. I would call this prejudice—a failure to understand what motivates an employee. The writer did indeed meet his deadlines for a few months, but he left the company within six months. Apparently, although the writer didn’t care about the rewards for good performance, he felt strongly about being punished for poor performance.

Psychopathy: An Introduction to Biological Findings and Their Implications
by Andrea L. Glenn and Adrian Raine
Published 7 Mar 2014

Mechelli, M. Wilke, K. R. Laurens, A. P. Jones, G. J. Barker, S. Hodgins, and E. Viding. 2009. “Size matters: Increased gray matter in boys with conduct problems and callous-unemotional traits.” Brain 132:843–52. De Martino, B., C. F. Camerer, and R. Adolphs. 2010. “Amygdala damage eliminates monetary loss aversion.” Proceedings of the National Academy of Sciences USA 107 (8):3788–92. de Oliveira-Souza, R., R. D. Hare, I. E. Bramati, G. J. Garrido, F. A. Ignácio, F. TovarMoll, and J. Moll. 2008. “Psychopathy as a disorder of the moral brain: Fronto-temporo-limbic grey matter reductions demonstrated by voxel-based morphometry.”

Life as a Passenger: How Driverless Cars Will Change the World
by David Kerrigan
Published 18 Jun 2017

If I take 30 steps exponentially, I get to a billion.”[368] Given that the negative impacts of driverless cars (e.g. job losses, first deaths of humans due to system errors) are likely to happen before the positives are fully realised, we face the very real chance that coping with the change will exceed our ability to prepare for it - especially when the outcome of a nonlinear change is negative, tendencies like loss aversion can kick in and people can react in highly emotional ways. Driverless cars are not the first transit technology to challenge our conceptions of time and space. The increased speed made possible by the first railroads totally changed the contemporary perception of distance between locations.

The New Economics: A Bigger Picture
by David Boyle and Andrew Simms
Published 14 Jun 2009

People are motivated to ‘do the right thing’: there are cases where money is actually demotivating because it undermines what drives people in the first place (you might stop looking after neighbours’ children if they insisted on paying you). People’s self-expectations influence how they behave: they want their actions to be in line with their values and their commitments. People are loss-averse and hang on to what they consider ‘theirs’. People are bad at computation when making decisions: they put too much weight on recent events and too little on far-off ones. They are bad at working out probabilities and worry too much about unlikely events, and they are strongly influenced by how the problem or information is presented to them.

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It)
by Salim Ismail and Yuri van Geest
Published 17 Oct 2014

Framing bias: Drawing different conclusions from the same information, depending on how or by whom that information is presented. Optimism bias: Tendency to be over-optimistic, overestimating favorable and pleasing outcomes. Planning fallacy bias: Tendency to overestimate benefits and underestimate costs and task-completion times. Sunk-cost or loss-aversion bias: Disutility of giving up an object is greater than the utility associated with acquiring it. Complete list of all cognitive biases: http://en.wikipedia.org/wiki/List_of_cognitive_biases Jacobstein is fond of pointing out that your neocortex has not had a major upgrade in 50,000 years.

The Future of Capitalism: Facing the New Anxieties
by Paul Collier
Published 4 Dec 2018

One is that, in order to be effective, any rewards need to be delivered almost immediately after the effort – within minutes, not months. As to the sorts of reward, esteem works better than money (once again, we are revealed to be more social animals than greedy ones). But rewards turn out not to be the best motivator. People are much more motivated to avoid losses than to acquire gains – the technical term is ‘loss aversion’ – so swift, esteem-related losses for low effort may pack the biggest punch. Yet this is not a message prominent in teacher training colleges. The issue of streaming is beset by ideological disputes, and is an issue desperately in need of pragmatism. A credible psychological theory is that children seek peer esteem, and are willing to put in some effort to get it (or avoid losing it).

Busy
by Tony Crabbe
Published 7 Jul 2015

They found that after each break or lunch (where food and drinks were served) there were spikes of higher parole levels. The boost in glucose levels increased the likelihood that the board would make the harder choice. So, take regular breaks, and have a small snack during each break to replace some of the glucose. Then, when your brain is refreshed, review your progress and reprioritize. Loss Aversion When a friend took up kite surfing, I felt a kind of pain inside. It wasn’t envy or resentment. It was a sense of loss. My life wasn’t allowing me to kite surf (or skydive or even play golf). One of the reasons we load our plate up so high is that we hate losing out on things. We hate narrowing our options.

Brave New Work: Are You Ready to Reinvent Your Organization?
by Aaron Dignan
Published 1 Feb 2019

Madrian, “Plan Design and 401(k) Savings Outcomes,” working paper 10486 (Cambridge, MA.: National Bureau of Economic Research, 2004): www.nber.org/papers/w10486.pdf. We prefer to stick with what we’ve got: Daniel Kahneman, Jack L. Knetsch, and Richard H. Thaler, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias,” Journal of Economic Perspectives 5, no. 1 (1991), doi:10.1257/jep.5.1.193. “put a man on the moon”: Astro Teller, “Google X Head on Moonshots: 10x Is Easier Than 10 Percent,” Wired, February 11, 2013, www.wired.com/2013/02/moonshots-matter-heres-how-to-make-them-happen.

Finding Alphas: A Quantitative Approach to Building Trading Strategies
by Igor Tulchinsky
Published 30 Sep 2019

Bloomberg Education & Quantitative Research and Education Paper. https://ssrn.com/ abstract=1358533 Mohanram, P. (2004) “Separating Winners from Losers Among Low Book-to-Market Stocks Using Financial Statement Analysis.” http:// ssrn.com/abstract=403180 Ormos, M. and Timotity, D. (2016b) “Microfoundations of Heteroscedasticity in Asset Prices: A Loss-Aversion-Based Explanation of Asymmetric GARCH Models.” https://ssrn.com/abstract=2736390 Preis, T., Moat, H., and Stanley, H. (2013) “Quantifying Trading Behavior in Financial Markets Using Google Trends.” http://www.nature. com/srep/2013/130425/srep01684/full/srep01684.html Roll, R., Schwartz, E., and Subrahmanyam, A. (2009) “O/S: The Relative Trading Activity in Options and Stock.” http://ssrn.com/ abstract=1410091 284References Scherbina, A. and Schlusche, B. (1915) “Cross-Firm Information Flows and the Predictability of Stock Returns.” https://ssrn.com/ abstract=2263033 Shlens, J. (2014) “A Tutorial on Principal Component Analysis.” https:// arxiv.org/abs/1404.1100 Sloan, R., Khimich, N., and Dechow, P. (2011) “The Accural Anomaly.” http://ssrn.com/abstract=1793364 Sprenger, T. and Welpe, I. (2011) “News or Noise?

Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity
by Douglas Rushkoff
Published 1 Mar 2016

Interestingly, as we saw earlier, it was only when debt-based currency’s limits began to surface in the twentieth century that those legions of psychologists were hired by banks and credit companies to come up with ways to get people to borrow more money, and at higher interest rates. The psychologists learned how to exploit people’s misconceptions about how money worked and gave names to each of our vulnerabilities, such as “irrationality bias,” “money illusion bias,” “loss aversion theory,” and “time discounting.”* Then, they created products and wrote advertising that took advantage of these failings in order to get people to act against their own best interests. In other words, if the money stops fulfilling the needs of human beings, you change human beings to fit the needs of the money.

The Willpower Instinct: How Self-Control Works, Why It Matters, and What You Can Doto Get More of It
by Kelly McGonigal
Published 1 Dec 2011

Then I try to bargain you down: Would you be willing to trade it in for a $50 check that is good today? Most people would not. However, if people are first given the $50 check, and then asked if they’d be willing to exchange it for a $100 delayed reward, most will not. The reward you start with is the one you want to keep. One reason is that most people are loss-averse—that is, we really don’t like to lose something we already have. Losing $50 makes people more unhappy than getting $50 makes them happy. When you think about a larger, future reward first and consider trading it in for a smaller, immediate reward, it registers as a loss. But when you start with the immediate reward (the $50 check in your hand) and consider the benefits of delaying gratification for a larger reward, it also feels like a loss.

A Fine Mess
by T. R. Reid
Published 13 Mar 2017

Farmers’ Loan and Trust Co., 157 U.S. 607 (1894). 14. An excellent history of the first U.S. income tax can be found in Steven R. Weisman, The Great Tax Wars (New York: Simon & Schuster, 2002). 15. Liz Alderman, “In Pursuit of Greek Tycoons and Tax Cheats,” New York Times, Feb. 26, 2015, B1. 16. Lucy Martin, “Taxation, Loss Aversion, and Accountability” (Yale University, Department of Political Science, 2014). Chapter 4: BBLR 1. Union des Associations Internationales, Yearbook of International Organizations, 2014–2015 (Brussels: Brill, 2015). 2. John Maynard Keynes, The General Theory of Employment, Interest, and Money (London: Macmillan, 1936), 372. 3.

I, Warbot: The Dawn of Artificially Intelligent Conflict
by Kenneth Payne
Published 16 Jun 2021

The prospect of nuclear annihilation has acted as a sobering brake on gambling.24 At the brink, in October 1962, that’s exactly what Khrushchev and Kennedy chose to do. The awful prospect of nuclear war chilled the deliberations of the antagonists. Indeed, a minimax preference has reinforced the nuclear taboo over the decades since Hiroshima. And perhaps we shouldn’t be too surprised—humans are, broadly, loss averse, and nuclear bombs amount to a catastrophic loss. We can learn much from the big international crises of the Cold War years like Cuba; not least that coercion to compel and deter enemies is an uncertain affair. Using force is not rational in some abstract, game theoretical sense, in which probabilities and utilities are known and carefully weighed ahead of any action.

Randomistas: How Radical Researchers Changed Our World
by Andrew Leigh
Published 14 Sep 2018

The authors do not disclose which kind of soft fruit their subjects pick – this information is contained in Tim Harford, ‘The fruits of their labors’, Slate, 23 August 2008. 41An even more nefarious way of delivering bonuses is to ‘provisionally’ pay workers a bonus, but then to say that it will be withdrawn if performance targets are not met. This exploitation of employee ‘loss aversion’ did indeed raise productivity in a randomised experiment in a Chinese factory: Tanjim Hossain & John A. List. ‘The behavioralist visits the factory: Increasing productivity using simple framing manipulations’, Management Science, vol. 58, no. 12, 2012, pp. 2151–67. Similarly, ridesharing company Lyft found that new drivers were more likely to shift from a quiet time of the week to a busy time of the week if the difference was expressed as a loss than a gain (the company ultimately chose not to implement the results of the study): Noam Scheiber, ‘How Uber uses psychological tricks to push its drivers’ buttons’, New York Times, 2 April 2017. 42Alexandre Mas & Enrico Moretti, ‘Peers at work’, American Economic Review, vol. 99, no. 1, 2009, pp. 112–45; Oriana Bandiera, Iwan Barankay and Imran Rasul, ‘Social incentives in the workplace’, Review of Economic Studies, vol. 77, no. 2, 2010, pp. 417–58; Lamar Pierce and Jason Snyder, ‘Ethical spillovers in firms: Evidence from vehicle emissions testing,’ Management Science, vol. 54, no. 11, 2008, pp. 1891–1903.

Alchemy: The Dark Art and Curious Science of Creating Magic in Brands, Business, and Life
by Rory Sutherland
Published 6 May 2019

I was recently part of a group that met to discuss what the government could do to make paying into a pension more appealing, particularly to younger people, without requiring such a high level of financial subsidy. We were all impressed by the work that Richard Thaler and Shlomo Benartzi had already performed in this field: together, they conceived a new mechanism for pension-saving that acknowledges one of the central principles of behavioural psychology – loss-aversion, the mental mechanism that causes us to experience more pain from losing £100 than pleasure from winning £100.* A typical pension works like this: if you buy a pension plan for £250 a month, every month thereafter you are £250 poorer, until your retirement, when you can redeem the annual salary which that pension provides.

Daughter Detox: Recovering From an Unloving Mother and Reclaiming Your Life
by Peg Streep
Published 14 May 2017

This is pretty obvious once you know about it: Our mindset at the time affects our thinking about the future. The best antidote to this forecasting error is being aware of the mood you’re in and making sure that your ability to perceive your emotions—yes, that’s emotional intelligence again—is working optimally. LOSS AVERSION, SUNK COSTS, AND GETTING STUCK Culturally, we put a high premium on staying the course, persistence, and grit, and we tend to be dismissive of those who quit or give up; that’s why you’re more likely to be surrounded by people who believe that “winners never quit and quitters never win” even though you’re actually struggling with letting go.

Everything Is Obvious: *Once You Know the Answer
by Duncan J. Watts
Published 28 Mar 2011

Taken together, the evidence from psychological experiments makes clear that there are a great many potentially relevant factors that affect our behavior in very real and tangible ways but that operate largely outside of our conscious awareness. Unfortunately, psychologists have identified so many of these effects—priming, framing, anchoring, availability, motivated reasoning, loss aversion, and so on—that it’s hard to see how they all fit together. By design, experiments emphasize one potentially relevant factor at a time in order to isolate its effects. In real life, however, many such factors may be present to varying extents in any given situation; thus it’s critical to understand how they interact with one another.

Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets
by Nassim Nicholas Taleb
Published 1 Jan 2001

Frederick, 2002, “Representativeness Revisited: Attribute Substitution in Intuitive Judgment.” In Gilovich, Griffin, and Kahneman. ———, J. L. Knetsch, and R. H. Thaler, 1986, “Rational Choice and the Framing of Decisions.” Journal of Business, Vol. 59 (4), 251–278. ———, J. L. Knetsch, and R. H. Thaler, 1991, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias.” In Kahneman and Tversky (2000). ———, and D. Lovallo, 1993, “Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk-taking. Management Science, 39, 17–31. ———, P. Slovic, and A. Tversky, eds., 1982, Judgment Under Uncertainty: Heuristics and Biases. Cambridge, Eng.: Cambridge University Press. ———, and A.

Present Shock: When Everything Happens Now
by Douglas Rushkoff
Published 21 Mar 2013

Behavioral finance is the study of the way people consistently act against their own best financial interests, as well as how to exploit these psychological weaknesses when peddling questionable securities and products. These are proven behaviors with industry-accepted names like “money illusion bias,” “loss aversion theory,” “irrationality bias,” and “time discounting.” For instance, people do not borrow opportunistically, but irrationally. As if looking at objects in the distance, they see future payments as smaller than ones in the present—even if they are actually larger. They are more reluctant to lose a small amount of money than gain a larger one, no matter the probability of either event in a particular transaction.

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines
by Thomas H. Davenport and Julia Kirby
Published 23 May 2016

Financial investment firms have a version of this for personal investing that is called “behavioral finance.” It means that no matter how good automated decision-making can be about what financial assets to buy and sell at what times, irrational humans may override the advice—whether human or automated—and make bad decisions. Some of the common ways that investors make poor decisions include “loss aversion”—caring more about not losing a dollar than gaining a dollar—and “familiarity bias”—being more willing to invest in familiar assets, like the stocks of companies in their home country, than those in companies they’ve never heard of. These irrational decision criteria lead to such woeful investor behaviors as “buying high and selling low.”

Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism
by George A. Akerlof and Robert J. Shiller
Published 1 Jan 2009

Hall and Mishkin (1982) explain some anomalies by dividing individuals into consumers who are borrowing-constrained and those without such a constraint. Shea (1995b) shows evidence that neither borrowing constraints nor “myopia” can fully explain the predictability of consumption; he proposes that preferences involving loss aversion must play a role in explaining the predictability of consumption. Shefrin and Thaler assembled some of the evidence of anomalies and produced a behavioral life-cycle model that incorporates some of the best features of the life-cycle model of Ando and Modigliani (1963) and adjusts it for known facts about human behavior (Thaler 1994).

Beyond Diversification: What Every Investor Needs to Know About Asset Allocation
by Sebastien Page
Published 4 Nov 2020

Another methodology is to add these higher moments directly into the objective function, as follows:5 Expected return – risk aversion × volatility + skewness aversion × skewness – kurtosis aversion × kurtosis Note the positive sign for skewness, as positive skewness is preferable to negative skewness for loss-averse investors. The effect of this modification to mean-variance optimization is generally the same as for the other modified approaches: assets with undesirable downside risk are penalized beyond what would be captured by volatility. How Should We Address Issues with Concentrated and Unstable Solutions?

Split-Second Persuasion: The Ancient Art and New Science of Changing Minds
by Kevin Dutton
Published 3 Feb 2011

The equity premium puzzle has long baffled financial experts. This is the tendency for large numbers of investors to invest in bonds as opposed to equities – especially during periods of stock-market decline – despite the fact that the latter, over time, have shown far better rates of return. Such conundrums as this – known as myopic loss aversion – have provided the impetus for a new, and somewhat timely, field of study: neuroeconomics. Neuroeconomics focuses on the mental processes that drive financial decision-making – and the primary finding so far has been that emotion is chicken. Emotion, it would seem, is so oriented towards risk-aversion that even when the benefits outweigh the losses it henpecks our brains into erring on the side of caution.

Messing With the Enemy: Surviving in a Social Media World of Hackers, Terrorists, Russians, and Fake News
by Clint Watts
Published 28 May 2018

Over many years of research, they identified a series of heuristics—mental rules people use to make decisions—and noted the circumstances where biases emerged that led to incorrect judgments. These two gentlemen had determined long ago the predictive missteps I observed in my polls. Status quo bias, a belief that tomorrow will most likely look like today, ruled my responses. Loss aversion, a tendency to avoid anticipated losses rather than pursue equally likely gains, filled the results of counterterrorism policy questions. Herding, the tendency of large groups of people to behave the same way and pursue groupthink, drove my social media recruits to the same set of answers. Armed with Tetlock’s insights and Kahneman and Tversky’s heuristics and biases, I changed my approach.

The Alternative: How to Build a Just Economy
by Nick Romeo
Published 15 Jan 2024

If you have some knowledge of history and philosophy, the so-called revolution in behavioral economics over the past few decades appears less revolutionary. Many major insights of behavioral economics are rediscoveries of patterns already identified by ancient philosophers. Plato’s dialogues depict and analyze the cognitive-emotional processes that behavioral economics now calls confirmation bias, availability bias, framing effects, loss aversion, representativeness heuristic, and anchoring.4 There is some value in establishing these patterns with the methods of experimental psychology, using double-blind studies and populations from around the world. But it’s striking that some of the more celebrated intellectual achievements of economics are largely a rediscovery of processes depicted by philosophers and novelists.

Is the Internet Changing the Way You Think?: The Net's Impact on Our Minds and Future
by John Brockman
Published 18 Jan 2011

I recently talked to a scholar of rare wisdom and erudition, Jon Elster, who, upon exploring themes from social science, integrates insights from all authors in the corpus of the past twenty-five hundred years, from Cicero and Seneca to Montaigne and Proust. He showed me how Seneca had a very sophisticated understanding of loss aversion. I felt guilty for the time I spent on the Internet. Upon getting home, I found in my mail a volume of posthumous essays by Bishop Pierre Daniel Huet, called Huetiana, put together by his admirers circa 1722. It is saddening to realize that, having been born nearly four centuries after Huet, and having done most of my reading with material written after his death, I am not much more advanced in wisdom than he was.

Top Dog: The Science of Winning and Losing
by Po Bronson and Ashley Merryman
Published 19 Feb 2013

Danielle, “Gender Differences in Performance in Competitive Environments: Evidence from Professional Tennis Players,” IZA Discussion Paper No. 2834, http://bit.ly/PvNd12 (June 2007) Paserman, M. Danielle, “Gender Differences in Performance in Competitive Environments: Evidence from Professional Tennis Players,” http://bit.ly/TCgsQY (2010) Pope, Devin G., & Maurice E. Schweitzer, “Is Tiger Woods Loss Averse? Persistent Bias in the Face of Experience, Competition, and High Stakes,” American Economic Review, vol. 101(1), pp. 129–157 (2011) “Rally Propels Mauresmo to First Fitle at Wimbledon,” AP via USA Today, http://usat.ly/PDjLVP (7/14/2006) “Tennis—List of Wimbledon Women’s Singles Champions,” Reuters UK, http://reut.rs/KPlZ8G (7/21/2012) The Progression of Psychological Theories of Gain/Prevention Orientation: Appelt, Kirstin C., & E.

The Startup Way: Making Entrepreneurship a Fundamental Discipline of Every Enterprise
by Eric Ries
Published 15 Mar 2017

A sole proprietor struggles with the decision of what to do with earnings in a profitable year. Every dollar invested in growth is a dollar taken out of the proprietor’s own pocket. It’s a painful choice that must be made over and over again. Risky investments are especially painful because of the psychological phenomenon of loss aversion. Because early-stage equity compensates every employee based on the company’s long-term growth and success, it creates a much closer alignment between the financial incentives of employees and managers and the organization’s long-term health. I don’t claim that this bond is perfect in all cases, and, of course, most startups pay salaries as well as offer other kinds of bonus compensation.

Nobody's Fool: Why We Get Taken in and What We Can Do About It
by Daniel Simons and Christopher Chabris
Published 10 Jul 2023

She told us that many of the professional investors who had money with Madoff “thought it was their God-given right to earn at least 8 percent per year, risk-free,” and that whenever Madoff tried to make his returns more realistic, meaning they would occasionally dip below that value, those customers were not happy.10 The proliferation of Madoff schemes likely results from another aspect of our preference for consistency beyond risk aversion and loss aversion: our poor understanding and unjustified dislike of consistency’s opposite, “noise.” In this context, noise refers to the essentially random aspects of any complex process. The temperature doesn’t go up by 1 degree every day as winter turns to spring; baseball teams don’t score the same number of runs in every game; and stock market prices can fluctuate wildly from day to day, week to week, and even decade to decade.

You've Been Played: How Corporations, Governments, and Schools Use Games to Control Us All
by Adrian Hon
Published 14 Sep 2022

It’s the only reason United isn’t bankrupt, or on the verge.”37 Frequent-flyer programs have recently landed in the crosshairs of the UK’s Committee on Climate Change, which in 2019 recommended the government “introduce a ban on air miles and frequent flyer loyalty schemes that incentivise excessive flying,” as Norway did from 2002–2013.38 Volodymyr Bilotkach, associate professor at the Singapore Institute of Technology, believes that all awards travel and mileage runs add up to only a few percent of total airline emissions, but a few percent of a billion tons of carbon dioxide per year is still on par with Denmark or Ireland’s emissions.39 As travel writer Seth Kugel argues, the symbolic value of frequent-flyer status tiers may also contribute to the demand for hyperwasteful business and first-class cabins. Even those who aren’t obsessed by their mileage status may feel a twinge of worry when they read an email warning their miles will expire if they don’t fly again, perhaps enough to sway them from choosing another mode of transport or holidaying closer to home. This is “loss aversion” in action, where people tend to prefer avoiding a loss over gaining the same amount. It’s a common technique used in the gamification industry, and it’s hard to think of it being put to a more damaging use. In comparison, you might imagine treating health insurance as a game would be relatively harmless.

Break Through: Why We Can't Leave Saving the Planet to Environmentalists
by Michael Shellenberger and Ted Nordhaus
Published 10 Mar 2009

Interest-based advocacy groups of all kinds generally define their interests narrowly; once an interest is defined, the group defends it tenaciously. It is for this reason that environmental advocacy and policy development, particularly at the national level, tends to be so reductive, small-bore, and timid. Interest-based advocacy tends to be both loss averse and single-minded. First, hold on to what you’ve got. Then look to work the legislative, legal, or regulatory process in search of small advantages to further your interest. This approach tends to work well for powerful economic interests determined to expand their grip on a particular sector of the economy.

The Four Pillars of Investing: Lessons for Building a Winning Portfolio
by William J. Bernstein
Published 26 Apr 2002

We can estimate that because of their fear of short-term loss, their portfolios were underexposed to stocks to the point where they lost 3% of return annually over the next three decades. Compounding 3% of underperformance over 30 years means that their final wealth was 59% less than it should have been. In other words, their fear of a 20% to 40% loss cost them 59% of their assets. In academic finance, this is called “myopic loss aversion”—focusing on short-term dangers and ignoring the far more serious long-term ones. Why do we do this? Human beings experience risk in the short-term. This is as it should be, of course. In the state of nature our ancestors inhabited, an ability to focus on the risks of the moment had much greater survival value than long-term strategic analytic ability.

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking
by Michael Bhaskar
Published 2 Nov 2021

Demographic effects play another role – research argues that the presence of more older people in the population translates into more concentrated and older companies, which in turns drags down dynamism.29 As the population ages, pension funds accumulate more and more of the economy; they don't want disruption, they want reliable income. The older we get, the less risk the economy can bear. In general, one would expect an older society to be less dynamic, more risk- and loss-averse. Others see the role of competition, market barriers or access to capital as having the same effect. The capital required to unseat incumbents, whose market concentration has grown even as the number of startups falls, has risen. Ufuk Akcigit and Sina T. Ates see the role of those ever-stronger market-leading companies as critical.30 They note increases in the companies’ power and wealth, and corresponding increases in aggressive patenting activity (more of which below).

Greater: Britain After the Storm
by Penny Mordaunt and Chris Lewis
Published 19 May 2021

Economic progress spans the generations, as parents see their children’s standard of living surpass their own, and their children experience the same in turn. The thing is, that endless expectation of a higher income than your parents is unsustainable without economic expansion. We’re hardwired for ‘loss aversion’, to experience more pain from loss than pleasure from gain. In an example of this, after wages, workers rank job security as one of the qualities they most value in a workplace.70 It is the lack of understanding of this factor that has been so costly to free marketeers. They advocate its benefits – and there are many – but are less keen to see or mitigate for its downside, especially through the eyes of the workforce.

Strategy: A History
by Lawrence Freedman
Published 31 Oct 2013

Framing helped explain how choices came to be viewed differently by altering the relative salience of certain features. Individuals compared alternative courses of action by focusing on one aspect, often randomly chosen, rather than keep in the frame all key aspects.14 Another important finding concerned loss aversion. The value of a good to an individual appeared to be higher when viewed as something that could be lost or given up than when evaluated as a potential gain. Richard Thaler, one of the first to incorporate the insights from behavioral economics into mainstream economics, described the “endowment effect,” whereby the selling price for consumption goods was much higher than the buying price.15 Experiments Another challenge to the rational choice model came from experiments that tested propositions derived from game theory.

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These simple decisions won’t overwhelm the prefrontal cortex. In fact they are so simple that they tend to trip up the emotions, which don’t know how to compare prices or compute the odds of a poker hand. (When people rely on their feelings in such situations, they make avoidable mistakes, like those due to loss aversion and arithmetical errors.) Complex problems, on the other hand, require the processing powers of the emotional brain, the supercomputer of the mind. This doesn’t just mean you can just blink and know what to do—even the unconscious takes a little time to process information—but it does suggest that there’s a better way to make difficult decisions.40 When the actual processes of decision-making were considered, there was therefore very little relationship to the formal model of decision-making.

Culture and Prosperity: The Truth About Markets - Why Some Nations Are Rich but Most Remain Poor
by John Kay
Published 24 May 2004

Cambridge, Mass.: Harvard University Press. ---. 1993. "The Economic Way of Looking at Behavior (Nobel Lecture)." Journal of Political Economy 101 (3) Oune): 385-409. Belsky, G., and T. Gilovich. 2000. Why Smart People Make Big Money Mistakes. New York: Fireside. Benartzi, S., and R H. Thaler. 1995. "Myopic Loss Aversion and the Equity Premium." Quarterly Journal ofEconomics, February, 73-92. Berlin, I. 2000. The Power ofIdeas. Ed. H. Hardy. Princeton: Princeton University Press. Bernstein, P. L. 1996. Against the Gods: The Remarkable Story of Risk. New York and Chichester: John Wiley and Sons, Inc. Berry, C.]. 1997.

Life Inc.: How the World Became a Corporation and How to Take It Back
by Douglas Rushkoff
Published 1 Jun 2009

Behavioral finance is the study of the way people consistently act against their own best financial interests, as well as how to exploit these psychological weaknesses when peddling questionable securities and products. These are proven behaviors with industry-accepted names like “money illusion bias,” “loss aversion theory,” “irrationality bias,” and “time discounting.” People do not borrow opportunistically, but irrationally. As if looking at objects in the distance, they see future payments as smaller than ones in the present—even if they are actually larger. They are more reluctant to lose a small amount of money than to gain a larger one—no matter the probability of either event in a particular transaction.

The Panic Virus: The True Story Behind the Vaccine-Autism Controversy
by Seth Mnookin
Published 3 Jan 2012

“Vaccines and Autism: Questions.” The Huffington Post, May 20, 2005; Web, August 10, 2010. Laidler, James R. “Through the Looking Glass: My Involvement with Autism Quackery.” Autism Watch, December 7, 2004; Web, October 12, 2009. Lehrer, Jonah. “Cable News.” ScienceBlogs, January 26, 2010; Web, August 10, 2010. ———. “Loss Aversion.” ScienceBlogs, February 10, 2010; Web, April 16, 2010. Leitch, Kevin. “DAN!—On a mission from God.” Left Brain, Right Brain, October 9, 2006; Web, August 10, 2010. McCarthy, Jenny. “A Girl’s Gotta Do What a Girl’s Gotta Do.” Oprah.com, May 17, 2009; Web, October 12, 2009. McCarthy, Jenny, and Jim Carrey.

Virtual Competition
by Ariel Ezrachi and Maurice E. Stucke
Published 30 Nov 2016

But self-learning algorithms and God View could make tacit collusion among more competitors likelier. The stability needed for tacit collusion is enhanced by the fact that computer algorithms, while not trusting, are unlikely to exhibit other human biases. Human biases can always be reflected in the programming code, but if some biases are minimized (such as loss aversion, the sunk cost fallacy, and framing effects), the algorithm acts consistently on more deliberative analysis, rather than intuition.14 Unlike humans, the computer does not fear detection and possible financial penalties or incarceration; nor does it respond in anger. The computer can quantify the payoffs that are likely achievable through cooperation in future games, and opt for forbearance rather than punishing small deviations.

Adam Smith: Father of Economics
by Jesse Norman
Published 30 Jun 2018

First, as we have seen, Smith did not originate the idea of economic man, and he was scathing about human greed. Indeed, far from adopting the modern paradigm of self-interested economic rationality, in The Wealth of Nations he identifies a host of areas, now familiar from modern behavioural economics, in which people do not behave in supposedly economically rational ways. These include loss aversion, where people react much worse to the loss of something than they do positively to its acquisition; a very marked preference for short-term over long-term gains, or what are referred to today as hyperbolic discount rates on future gains; and overconfidence in assessing risky options. This is what we should expect, for where there are obvious mistakes of perspective and judgement, it is the function of the impartial spectator within Smith’s system to correct for them.

In Pursuit of the Perfect Portfolio: The Stories, Voices, and Key Insights of the Pioneers Who Shaped the Way We Invest
by Andrew W. Lo and Stephen R. Foerster
Published 16 Aug 2021

The behavioral critics of rationality also devised empirical studies in which investors seemed to deviate from the predictions of simple models of rational behavior. According to the behavioralists, these apparent deviations from rationality could be attributed to investor biases, such as excessive optimism, overconfidence, overreaction, loss aversion, herding, miscalibration of probabilities, and mental accounting. There will be more on the behavioralists in chapter 9. Trying to Beat the Market Eugene Fama’s grandparents emigrated from Sicily and came to the United States in the early 1900s, making him a proud third-generation Italian American.4 His parents, aunts, and uncles started their working lives around the beginning of the Great Depression.

The Singularity Is Nearer: When We Merge with AI
by Ray Kurzweil
Published 25 Jun 2024

v=uqXVAo7dVRU; “Thinking Fast and Slow by Daniel Kahneman #2—Heuristics and Biases: Animated Book Summary,” One Percent Better, YouTube video, November 12, 2016, https://www.youtube.com/watch?v=Q_wBt5aSRYY; “Kahneman and Tversky: How Heuristics Impact Our Judgment,” Intermittent Diversion, YouTube video, June 7, 2018, https://www.youtube.com/watch?v=3IjIVD-KYF4; Richard H. Thaler et al., “The Effect of Myopia and Loss Aversion on Risk Taking: An Experimental Test,” Quarterly Journal of Economics 112, no. 2 (May 1997): 647–61, https://www.jstor.org/stable/2951249; Daniel Kahneman and Amos Tversky, “The Psychology of Preferences,” Scientific American 246, no. 1 (January 1981): 160–73; Daniel Kahneman, Paul Slovic, and Amos Tversky, eds., Judgment Under Uncertainty: Heuristics and Biases (Cambridge, UK: Cambridge University Press, 1982); Amos Tversky and Daniel Kahneman, “Judgment Under Uncertainty: Heuristics and Biases,” Science 185, no. 4157 (September 27, 1974): 1124–31, http://doi.org/10.1126/science.185.4157.1124; Daniel Kahneman and Amos Tversky, “On the Study of Statistical Intuitions,” Cognition 11, no. 2 (March 1982): 123–41; Daniel Kahneman and Amos Tversky, “Variants of Uncertainty,” Cognition 11, no. 2 (March 1982): 143–57.

Antifragile: Things That Gain From Disorder
by Nassim Nicholas Taleb
Published 27 Nov 2012

This asymmetry between the effects of good and bad, benefit and harm, had to be familiar to the ancients—I found an earlier exposition in Livy: “Men feel the good less intensely than the bad” (segnius homines bona quam mala sentiunt), he wrote half a generation before Seneca. Ancients—mostly thanks to Seneca—stay way ahead of modern psychologists and Triffat-style decision theorists who have developed theories around the notion of “risk (or loss) aversion,” the ancients remain deeper, more practical, while transcending vulgar therapy. Let me rephrase it in modern terms. Take the situation in which you have a lot to lose and little to gain. If an additional quantity of wealth, say, a thousand Phoenician shekels, would not benefit you, but you would feel great harm from the loss of an equivalent amount, you have an asymmetry.

The 4-Hour Chef: The Simple Path to Cooking Like a Pro, Learning Anything, and Living the Good Life
by Timothy Ferriss
Published 1 Jan 2012

You can also set up an “anti-charity,” an organization you so despise that you’d rather slam your head in a car door than donate to them. If you don’t fulfill your commitment, your funds are wired automatically. Based on stickK’s goal completion percentages from 2008–2011, we find that the success rate with no stakes is 33.5%. Once we add stakes like an anti-charity, that success rate more than doubles to 72.8%! Ah, loss aversion. How I love thee. The upshot: you gotta put your money (or reputation) where your mouth is. This works well beyond auctions. Everything from weight loss to quitting smoking is fair game. A goal without real consequences is wishful thinking. Good follow-through doesn’t depend on the right intentions.

Big Data and the Welfare State: How the Information Revolution Threatens Social Solidarity
by Torben Iversen and Philipp Rehm
Published 18 May 2022

One solution to this puzzle is that nationally organized groups, 2 The precise reason why voters should prefer a centrist candidate, party, or policy position if they are uninformed cannot, however, be exactly the same as when they assume average risks because there is no such thing as an actuarially accurate policy position. Nevertheless, under reasonable assumptions, the center is the preferred position of “loss-averse” voters who try to avoid making big mistakes if they are ill-informed. The result follows from spatial voting under uncertainty where voters minimize the expected difference (loss) between their vote and their (unobserved) interests (Iversen and Soskice 2015b). https://doi.org/10.1017/9781009151405.006 Published online by Cambridge University Press 164 Labor Market Risks who do have a tangible stake in the policy outcome, use their resources to incentivize their members to vote for a particular policy or party.

The Stuff of Thought: Language as a Window Into Human Nature
by Steven Pinker
Published 10 Sep 2007

As a result, people will often refuse to gamble for an expected profit (they turn down bets such as “Heads, you win $120; tails, you pay $100”), but they will gamble to avoid an expected loss (such as “Heads, you no longer owe $120; tails, you now owe an additional $100”). (This kind of behavior drives economists crazy, but is avidly studied by investment firms hoping to turn it to their advantage.) The combination of people’s loss aversion with the effects of framing explains the paradoxical result: the “gain” metaphor made the doctors risk-averse; the “loss” metaphor made them gamblers. Tversky and Kahneman’s 1981 study, though a bit complicated, is the gold standard for demonstrating the effects of framing on behavior: identical events, different metaphors, flipped decision—and not just any decision, but one that would affect hundreds of lives.

Behave: The Biology of Humans at Our Best and Worst
by Robert M. Sapolsky
Published 1 May 2017

Gospic et al., “Limbic Justice: Amygdala Involvement in Immediate Rejections in the Ultimatum Game,” PLoS ONE 9 (2011): e1001054; B. De Martino et al., “Frames, Biases, and Rational Decision-Making in the Human Brain,” Sci 313 (2006): 684; A. Bechara et al., “Role of the Amygdala in Decision-Making,” ANYAS 985 (2003): 356; B. De Martino et al., “Amygdala Damage Eliminates Monetary Loss Aversion,” PNAS 107 (2010): 3788; J. Van Honk et al., “Generous Economic Investments After Basolateral Amygdala Damage,” PNAS 110 (2013): 2506. 23. R. Adolphs et al., “The Human Amygdala in Social Judgment,” Nat 393 (1998): 470 24. D. Zald, “The Human Amygdala and the Emotional Evaluation of Sensory Stimuli,” Brain Res Rev 41 (2003): 88; C.

Principles of Corporate Finance
by Richard A. Brealey , Stewart C. Myers and Franklin Allen
Published 15 Feb 2014

This is not nearly large enough to explain the difference in returns. There is no simple relationship between the return on the value- and growth-stock portfolios and beta. 19We discuss aversion to loss again in Chapter 13. The implications for asset pricing are explored in S. Benartzi and R. Thaler, “Myopic Loss Aversion and the Equity Premium Puzzle,” Quarterly Journal of Economics 110 (1995), pp. 75–92; and in N. Barberis, M. Huang, and T. Santos, “Prospect Theory and Asset Prices,” Quarterly Journal of Economics 116 (2001), pp. 1–53. 20There may be some macroeconomic factors that investors are simply not worried about.