availability heuristic

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

One of the rules of thumb people often use when making judgments is the so-called availability heuristic. Suppose you’re asked, “Which are more frequent: English words that start with the letter ‘R,’ or those that have ‘R’ as their third letter?” Using the availability heuristic, most people react by trying to think of examples in each category. That approach usually works well, since examples of things that occur more frequently are in fact generally easier to summon from memory. And since most people find it easier to think of examples of words starting with “R,” the availability heuristic leads them to answer that such words occur more frequently.

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And since most people find it easier to think of examples of words starting with “R,” the availability heuristic leads them to answer that such words occur more frequently. Yet English words with “R” in the third slot are actually far more numerous. The availability heuristic fails here because frequency isn’t the only thing that governs ease of recall. We store words in our memories in multiple ways—by their meanings, by the sounds they make and the images they evoke, by their first letters, and by numerous other features. But virtually no one stores words in memory by the identity of their third letter. The availability heuristic suggests that when we construct narratives about how the world works, we rely more heavily on information that happens to be more accessible from memory.

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They probably also know, in some abstract sense, that they might not have done as well in some other environments. Yet their day-to-day experiences provide only infrequent reminders to reflect on how fortunate they were not to have been born in, say, a war-torn country like Zimbabwe. The availability heuristic biases our personal narratives in a second way, because events that work to our disadvantage are systematically easier to recall than those that affect us positively. My Cornell colleague Tom Gilovich invokes a metaphor involving headwinds and tailwinds to describe this asymmetry. If any of you go running or ride a bike, you’ll know that when you’re running or bicycling into the wind, you’re very aware of it.

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

This possibility suggests that we need a kind of behavioral science for judgments of morality, and not merely judgments of fact.11 Moral heuristics are pervasive, and they can go wrong, no less than heuristics of other kinds. Consider an analogy: The availability heuristic helps people to come up with estimates of probability, and it generally works well. When we learn of an incident in which certain actions produced serious harm, we update our probability judgments. The updating is perfectly sensible. Use of the availability heuristic can be seen as a kind of rough-and-ready statistical analysis—and perhaps it is even better than that. The problem is that use of the availability heuristic can also go badly wrong, leading to wildly exaggerated fears (or to unhealthy complacency).

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Perhaps the most important point is that disclosure requirements may turn out to be ineffective with respect to optimistically biased consumers. Any such requirements should be devised so as to reduce that risk. Graphic warnings, grabbing the attention of System 1, are a possibility here. PROBLEMS WITH PROBABILITY System 1 does not handle probability well. One problem is the availability heuristic. When people use that heuristic, they make judgments about probability by asking whether a recent event comes readily to mind.63 If an event is cognitively “available,” people might well overestimate the risk. If an event is not cognitively available, they might underestimate the risk.64 In deciding whether it is dangerous to walk in a city at night, to text while driving, or to smoke, people often ask about incidents of which they are aware.

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If an event is not cognitively available, they might underestimate the risk.64 In deciding whether it is dangerous to walk in a city at night, to text while driving, or to smoke, people often ask about incidents of which they are aware. While System 2 might be willing to do some calculations, System 1 works quickly, and it is pretty simple to use the availability heuristic. Instead of asking hard questions about statistics, System 1 asks easy questions about what comes to mind. “Availability bias” can lead to significant mistakes about the probability of bad outcomes, taking the form of either excessive fear or unjustified complacency.65 A distinct but related finding is that people sometimes do not make judgments on the basis of the expected value of outcomes, and they may neglect the central issue of probability, particularly when emotions are running high.66 In such cases, people may focus on the outcome and not on the probability that it will occur.67 If there is a small chance of catastrophe—the loss of a child, a fatal cancer—the outcome may dominate people’s thoughts, rather than the statistical likelihood that it will happen.

Infotopia: How Many Minds Produce Knowledge
by Cass R. Sunstein
Published 23 Aug 2006

We are also subject to identifiable biases, which can produce big mistakes.1 A growing literature explores the role of these heuristics and biases and their relationship to law and policy. For example, people err because they use the availability heuristic to answer difficult questions about probability. How / 75 likely is a terrorist attack, a hurricane, a traffic jam, an accident from a nuclear power plant, a case of venereal disease? When people use the availability heuristic, they answer a question of probability by asking whether examples come readily to mind.2 The point very much bears on private and public responses to risks—suggesting, for example, that people will be especially responsive to the dangers of AIDS, crime, earthquakes, and nuclear power plant accidents if examples are easy to recall.

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For example, whether people will buy insurance for natural disasters is greatly affected by recent experiences.4 In the aftermath of an earthquake, people become far readier to buy insurance for earthquakes, but their readiness to do so declines steadily from that point, as vivid memories recede. Use of the availability heuristic is not irrational, but it can easily lead to serious errors of fact. After the 2005 disaster produced by Hurricane Katrina in the United States, it was predictable that significant steps would be taken to prepare for hurricanes—and also predictable that before that disaster, such steps would be quite inadequate. 76 / Infotopia Most people are also strikingly vulnerable to framing effects, making different decisions depending on the wording of the problem.

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If so, the many minds on the jury are likely to amplify rather than to correct those biases.9 Deliberating groups have also been found to amplify, rather than to attenuate, reliance on the representativeness heuristic.10 Such groups fall prey to even larger framing effects than individuals, so that when the same situation is described in different terms, groups are especially likely to be affected by the redescriptions.11 Groups show more overconfidence than group members;12 78 / Infotopia they are even more affected by the biasing effect of bad arguments from lawyers.13 In an especially revealing finding, groups have been found to make more, rather than fewer, conjunction errors (believing that A and B are more likely to be true than A alone) than individuals when individual error rates are high—though fewer when individual error rates are low.14 Groups do demonstrate a decreased level of reliance on the availability heuristic, but the decrease is slight, even when use of that heuristic leads to clear errors.15 Here’s a disturbing finding, one with great relevance to group behavior in both politics and business: Groups are more likely than individuals to escalate their commitment to a course of action that is failing—and all the more so if members identify strongly with the groups of which they are a part.16 There is a clue here about why companies, states, and even nations often continue with projects and plans that are clearly going awry.

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

At night I wrote Attention and Effort. It was a busy year. One of our projects was the study of what we called the availability heuristic. We thought of that heuristic when we asked ourselves what people actually do when they wish to estimate the frequency of a category, such as “people who divorce after the age of 60” or “dangerous plants.” The answer was straightforward: instances of the class will be retrieved from memory, and if retrieval is easy and fluent, the category will be judged to be large. We defined the availability heuristic as the process of judging frequency by “the ease with which instances come to mind.” The statement seemed clear when we formulated it, but the concept of availability has been refined since then.

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judgment heuristics Judgment in Managerial Decision Making (Bazerman) judgments; basic assessments in; of experts, see expert intuition; intensity matching in; mental shotgun in; predictive, see predictions and forecasts; sets and prototypes in; summary, of complex information; see also decisions, decision making “Judgment Under Uncertainty: Heuristics and Biases” (Tversky and Kahneman) Julie problem jumping to conclusions; bias for belief and confirmation in; halo effect in, see halo effect; suppression of ambiguity and doubt in; WYSIATI in, see what you see is all there is Kaye, Danny keeping score; mental accounts and; regret and; responsibility and KEEP-LOSE study kidney cancer Killing Ground, The kitchen renovations Klein, Gary Knetsch, Jack know, use of word knowledge; reconstruction of past states of kouros Krueger, Alan Kunreuther, Howard Kuran, Timur labor negotiations Lady Macbeth effect language, complex vs. simple Larrick, Richard Larson, Gary law, see legal cases law of large numbers law of small numbers; and bias of confidence over doubt laziness of System 2 Layard, Richard leaderless group challenge leadership and business practices; at Google LeBoeuf, Robyn legal cases: civil, damages in; DNA evidence in; fourfold pattern and; frivolous; loss aversion in; malpractice; outcome bias in leisure time less-is-more pattern Lewis, Michael libertarian policies Lichtenstein, Sarah life: evaluation of; stories in; satisfaction in; thinking about Linda problem List, John loans logarithmic functions loss aversion; in animals; enhanced; goals as reference points in; in legal decisions; status quo and loss aversion ratio losses lotteries Lovallo, Dan Love Canal luck lying Malkiel, Burton Malmendier, Ulrike malpractice litigation Mao Zedong march of historyuote> Markowitz, Harry marriage; life satisfaction and Mathematical Psychology (Dawes, Tversky, and Coombs) matter, relation of mind to McFarland, Cathy media, availability heuristic and medical school admissions medical survey problem medicine; expertise in; malpractice litigation; overconfidence in; physicians; unique cases in; unusual treatments in Mednick, Sarnoff Meehl, Paul meetings memory, memories; associative, see associative memory; availability heuristic and, see availability; duration neglect in; experienced utility and; illusions of; and the remembering self; of vacations mental accounts mental effort, see effort mental energy mental shotgun mere exposure effect messages, persuasive metaphors Michigan/Detroit problem Michigan State University Michotte, Albert Miller, Dale mind, relation of matter to Mischel, Walter miswanting MIT money and wealth: cultural differences in attitudes toward; happiness and; income vs. leisure; mental accounts and; poverty; priming and; utility of Moneyball (Lewis) mood, see emotions and mood Morgenstern, Oskar Moses illusion motivation movies “MPG Illusion, The” (Larrick and Soll) mug experiments Mullainathan, Sendhil Müller-Lyer illusion multiple regression Mussweiler, Thomas mutual funds names: complicated; of famous people narrative fallacy narrow framing; disposition effect Naturalistic Decision Making (NDM) negativity dominance negotiations neuroeconomics New York Times, The New York University 9/11 Nisbett, Richard Nixon, Richard Nobel Prize norms norm theory novelty Nudge (Thaler and Sunstein) nutrition Oakland A’s Obama, Barack obesity Odean, Terry Office of Information and Regulatory Affairs one-sided evidence Oppenheimer, Danny optimal experience optimism; in CEOs; resilience and optimistic bias; competition neglect; in entrepreneurs; overconfidence; planning fallacy; premortem and; risk taking and Oregon Research Institute organ donation organizations outcome bias outside view ou> pain; chronic; cold-hand experiment and; colonoscopies and; duration neglect and; injection puzzle and; memory of; operation experiment and; peak-end rule and; in rats paraplegics parole past: and confusing experiences with memories; hindsight bias and; regret and pastness pattern seeking Pavlov, Ivan peak-end rule persuasive messages physicians; malpractice litigation and piano playing and weight, measuring plane crashes planning fallacy; mitigating plausibility pleasure; in rats Plott, Charles poignancy political experts political preference Pólya, George Pope, Devin Porras, Jerry I.

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On another occasion, Amos and I wondered about the rate of divorce among professors in our university. We noticed that the question triggered a search of memory for divorced professors we knew or knew about, and that we judged the size of categories by the ease with which instances came to mind. We called this reliance on the ease of memory search the availability heuristic. In one of our studies, we asked participants to answer a simple question about words in a typical English text: Consider the letter K. Is K more likely to appear as the first letter in a word OR as the third letter? As any Scrabble player knows, it is much easier to come up with words that begin with a particular letter than to find words that have the same letter in the third position.

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

Most of us give weight to these kinds of stories because they are extremely vivid and based on a personal, detailed, face-to-face account. Kahneman and Tversky discovered and reported on people’s tendency to give undue weight to some types of information in contrast to others. They called it the availability heuristic. This needs a little explaining. A heuristic is a rule of thumb, a mental shortcut. The availability heuristic works like this: suppose someone asked you a silly question like “What’s more common in English, words that begin with the letter t or words that have t as the third letter?” How would you answer this question? What you probably would do is try to call to mind words that start with t and words that have t as the third letter.

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Because I had an easier time recalling words that start with t than recalling words with t as the third letter, I must have encountered them more often in the past. So there must be more words in English that start with t than have it as the third letter.” But your conclusion would be wrong. The availability heuristic says that we assume that the more available some piece of information is to memory, the more frequently we must have encountered it in the past. This heuristic is partly true. In general, the frequency of experience does affect its availability to memory. But frequency of experience is not the only thing that affects availability to memory.

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So it’s the salience of starting letters that makes t-words come easily to mind, while people mistakenly think it’s the frequency of starting letters that makes them come easily to mind. In addition to affecting the ease with which we retrieve information from memory, salience or vividness will influence the weight we give any particular piece of information. There are many examples of the availability heuristic in operation. When college students who are deciding what courses to take next semester are presented with summaries of course evaluations from several hundred students that point in one direction, and a videotaped interview with a single student that points in the other direction, they are more influenced by the vivid interview than by the summary judgments of hundreds.

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

Of course, Ukraine was not the source of the Russian hacks; it was the target. But the Availability Heuristic works by association. Since Ukraine was associated with hacking, the heuristic lent credence to the claim that the hacking came from Ukraine. For similar reasons, phishing emails routinely refer to current events, like natural disasters and infectious diseases, when asking for donations. Because these events are vivid and sensational, the Availability Heuristic lends credibility to scams that mention them. The scams are believable because the events they mention are memorable. Closely related to the Availability Heuristic is the Affect Heuristic.

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Kahneman and Tversky hypothesized that when people are asked questions about how common objects are, they often respond to a different, easier question. Instead of “How common is this?” they answer, “How memorable is this?” According to the Availability Heuristic, the more available an object is in memory, the more common it will be judged to be. Because we assume that memorability is correlated with frequency, the media greatly affects our judgments. Accidents, for example, are thought to be greater causes of death than diabetes because car and plane crashes are covered by the news, while the more common diabetes deaths are not. The Availability Heuristic, therefore, biases our perception of frequency toward exceptional, especially vivid, events.

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MacGregor, “The Affect Heuristic,” European Journal of Operational Research 177 (2007): 1333–52. downplay its benefits: The Affect Heuristic works partially through the Availability Heuristic. The more you like something, the more likely its benefits will be available to you in memory. Conversely, the more available an event is in memory, the greater the affect experienced. For the relation between these two heuristics, see Thorsten Pachur et al., “How Do People Judge Risks: Availability Heuristic, Affect Heuristic, or Both?,” Journal of Experimental Psychology: Applied 18, no. 3 (2012): 314–30. an urn: Dale T. Miller, William Turnbull, and Cathy McFarland, “When a Coincidence Is Suspicious: The Role of Mental Simulation,” Journal of Personality and Social Psychology 57 (1989): 581–89; Lee A.

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

It’s easier to come up with words beginning with r than words having an r in the third position—because we “file” words in our minds by their initial letters and so they’re more available as we rummage through memory. But in fact there are more words with r in the third position. One problem with using the availability heuristic for judgments of frequency or plausibility is that availability is tangled up with salience. Deaths by earthquake are easier to recall than deaths by asthma, so people overestimate the frequency of earthquake deaths in their country (by a lot) and underestimate the frequency of asthma deaths (hugely). Heuristics, including the representativeness heuristic and the availability heuristic, operate quite automatically and often unconsciously. This means it’s going to be hard to know just how influential they can be.

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Honesty in the future is best predicted by honesty in the past, not by whether a person looks you steadily in the eye or claims a recent religious conversion. Competence as an editor is best predicted by prior performance as an editor, or at least by competence as a writer, and not by how verbally clever a person seems or how large the person’s vocabulary is. Another important heuristic Tversky and Kahneman identified is the availability heuristic. This is a rule of thumb we use to judge the frequency or plausibility of a given type of event. The more easily examples of the event come to mind, the more frequent or plausible they seem. It’s a perfectly helpful rule most of the time. It’s easier to come up with the names of great Russian novelists than great Swedish novelists, and there are indeed more of the former than the latter.

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The chapters are intended to help you build statistical heuristics—rules of thumb that will suggest correct answers for an indefinitely large number of everyday life events. These heuristics will shrink the range of events to which you will apply only intuitive heuristics, such as the representativeness and availability heuristics. Such heuristics invade the space of events for which only statistical heuristics are appropriate. Two years of thinking about rats or brains or memory for nonsense syllables produces little improvement in ability to apply statistical principles to everyday life events. Students in the hard areas of psychology may learn scarcely more than students in chemistry and law.

Enlightenment Now: The Case for Reason, Science, Humanism, and Progress
by Steven Pinker
Published 13 Feb 2018

See also reductionism; scientism postcolonial governments civil war and intercommunal violence of, 164 and democracies, rise of, 200, 201 famine exacerbated by policies of, 78 postmodernism, 351 hatred of science, 397 and malaise of the humanities, 406 Nietzsche as influence on, 446 relativism, 406 Poststructuralism, 446 Post-Traumatic Stress Disorder (PTSD), 281, 282 “post-truth era,” 375 Potomac River, 130 Pound, Ezra, 447 poverty, 79–96 clothing and, 80, 117, 118 conditions of, 79–80, 92–4 and consumption, 116–18, 116 as default state of humankind, 25, 79 definition of, 79 and disposable income, 115–16, 116 economic inequality confused with, 98–9 energy requirements to escape, 141 escape from, 24, 54, 85, 364, 459–60nn16,18,20 escape from, factors contributing to, 90–96, 234 homelessness, 116 pollution and, 130–31, 463n28 retirement and alleviation of, 250–51, 250 social spending to alleviate, 107–110, 115–16 workhouses, 79, 250–51 See also developing countries/world; economic inequality; wealth —EXTREME POVERTY, 87 number of people living in, 88–9, 88 per capita income distribution and, 86–7, 86 percentage of world living in, 87–8, 87 United Nations’ goal for reducing, 89, 460n28 power-law distribution, 46, 162, 290, 292–3 Prados de la Escosura, Leandro, 110, 245, 473n45 prediction, 46, 366–71 Availability heuristic and, 370 Bayesian reasoning and, 369–70 common-sense awareness of, 366 ideology driven as least successful, 368, 371 media and intellectuals unaccountable for, 366–7 prophecy distinguished from, 46 superforecasters, 368–71, 380, 393, 404 wisdom of crowds and, 370 President’s Council on Bioethics, 60 Preston Curve, 95 probabilities of imaginable events, inaccurate estimates, 292 of nuclear war, 312–13 of rare events, 46, 162, 290, 292–3 See also Availability heuristic; prediction productivity, 328 delay in effects of technological change, 330 factors affecting slowdown of, 329 technological sophistication and, 328 progress as apparent historical force, 109, 177–8, 190, 211–13, 215, 220–21 vs.

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The peace researcher John Galtung pointed out that if a newspaper came out once every fifty years, it would not report half a century of celebrity gossip and political scandals. It would report momentous global changes such as the increase in life expectancy.10 The nature of news is likely to distort people’s view of the world because of a mental bug that the psychologists Amos Tversky and Daniel Kahneman called the Availability heuristic: people estimate the probability of an event or the frequency of a kind of thing by the ease with which instances come to mind.11 In many walks of life this is a serviceable rule of thumb. Frequent events leave stronger memory traces, so stronger memories generally indicate more-frequent events: you really are on solid ground in guessing that pigeons are more common in cities than orioles, even though you’re drawing on your memory of encountering them rather than on a bird census.

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Not surprisingly, many people have a fear of flying, but almost no one has a fear of driving. People rank tornadoes (which kill about fifty Americans a year) as a more common cause of death than asthma (which kills more than four thousand Americans a year), presumably because tornadoes make for better television. It’s easy to see how the Availability heuristic, stoked by the news policy “If it bleeds, it leads,” could induce a sense of gloom about the state of the world. Media scholars who tally news stories of different kinds, or present editors with a menu of possible stories and see which they pick and how they display them, have confirmed that the gatekeepers prefer negative to positive coverage, holding the events constant.13 That in turn provides an easy formula for pessimists on the editorial page: make a list of all the worst things that are happening anywhere on the planet that week, and you have an impressive-sounding case that civilization has never faced greater peril.

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

To estimate risk, we should tally the number of instances of an event and mentally divide it by the number of occasions on which it could have taken place. Yet one of the signature findings in the science of human judgment is that this is not how human probability estimation generally works. Instead, people judge the probability of events by the ease with which instances come into mind, a habit that Tversky and Kahneman called the availability heuristic.11 We use the ranking from our brain’s search engine—the images, anecdotes, and mental videos it coughs up—as our best guess of the probabilities. The heuristic exploits a feature of human memory, namely that recall is affected by frequency: the more often we encounter something, the stronger the trace it leaves in our brains.

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Americans guess 24 percent; polls indicate 4.5 percent.14 African Americans? About a third, people say, around two and half times higher than the real figure, 12.7 percent. That’s still more accurate than their estimate for another conspicuous minority, Jews, where respondents are off by a factor of nine (18 versus 2 percent).15 The availability heuristic is a major driver of world events, often in irrational directions. Other than disease, the most lethal risk to life and limb is accidents, which kill about five million people a year (out of 56 million deaths in all), about a quarter of them in traffic accidents.16 But except when they take the life of a photogenic celebrity, car crashes seldom make the news, and people are insouciant about the carnage.

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Instead we judge the probability that an instance belongs to a category by how representative it is: how similar it is to the prototype or stereotype of that category, which we mentally represent as a fuzzy family with its crisscrossing resemblances (chapter 3). A cancer patient, typically, gets a positive diagnosis. How common the cancer is, and how common a positive diagnosis is, never enter our minds. (Horses, zebras, who cares?) Like the availability heuristic from the preceding chapter, the representativeness heuristic is a rule of thumb the brain deploys in lieu of doing the math.6 Tversky and Kahneman demonstrated base-rate neglect in the lab by telling people about a hit-and-run accident by a taxi late at night in a city with two cab companies: Green Taxi, which owns 85 percent of the cabs, and Blue Taxi, which owns 15 percent (those are the base rates, and hence the priors).

Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals
by David Aronson
Published 1 Nov 2006

That is to say, a faulty application of the generally useful representativeness rule biases us toward the perception of order where it does not exist. Heuristic Bias and the Availability Heuristic To recap, heuristics help us make complex decisions rapidly in spite of the limitations of human intelligence, but they can cause those decisions to be biased. The notion of heuristic bias is easily explained by considering the availability heuristic. We rely on the availability heuristic to estimate the likelihood of future events. It is based on the reasonable notion that the more easily we can bring to mind a particular class of events, the more likely it is that such events will occur in the future.

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It is based on the reasonable notion that the more easily we can bring to mind a particular class of events, the more likely it is that such events will occur in the future. Events that are easily brought to mind are said to be cognitively available. For example, plane crashes are a class of events with high cognitive availability. The availability heuristic makes a certain amount of sense. The ability to recall a class of events is indeed related to how frequently they have occurred in the past, and it is also true that events that have happened fre- 88 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS quently in the past are generally more likely to occur in the future.

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This is in keeping with one theory of probability that asserts that the future likelihood of an event is related to its historical frequency.143 Taken as a class, thunderstorms have been more frequent in the past than asteroid impacts, and they do indeed have a higher future likelihood. The problem with the availability heuristic is that there are factors that can enhance an event’s cognitive availability that have nothing to do with its historical frequency and are, therefore, irrelevant to estimating its future likelihood. Consequently, our judgments of likelihood are sometimes falsely inflated by the intrusion of these irrelevant factors.

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

In this case, the doctor’s error was to rule out a heart attack because the patient appeared to be a model of health and fitness. “You have to be prepared in your mind for the atypical and not so quickly reassure yourself, and the patient, that everything is okay,” the doctor later mused.10 The availability heuristic, judging the frequency or probability of an event based on what is readily available in memory, poses a related challenge. We tend to give too much weight to the probability of something if we have seen it recently or if it is vivid in our mind. Groopman tells of a woman who came to the hospital suffering from a low-grade fever and a high respiratory rate.

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She had taken too many aspirin in an attempt to treat a cold, and her fever and respiratory rate were classic symptoms. But the doctor overlooked them because of the vividness of the viral pneumonia. Like representativeness, availability encourages us to ignore alternatives.11 Think carefully about how the representativeness and availability heuristics may impose on your decisions. Have you ever judged someone solely based on how he or she looks? Have you ever feared flying more after hearing of a plane crash? If the answer is yes, you are a normal human. But you also risk misunderstanding, or missing altogether, plausible outcomes. Is the Trend Your Friend?

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Smith, eds., Handbook of Experimental Economics Results: Volume 1 (Amsterdam: North-Holland, 2008). 11. John R. Graham, Campbell R. Harvey, and Shiva Rajgopal, “Value Destruction and Financial Reporting Decisions,” Financial Analysts Journal 62, no. 6 (2006): 27–39. 12. This is a bias that arises from the availability heuristic. See Max H. Bazerman, Judgment in Managerial Decision Making, 6th ed. (New York: John Wiley & Sons, 2006), 18–21. 13. Alston Chase, Playing God in Yellowstone: The Destruction of America’s First National Park (Boston: The Atlantic Monthly Press, 1986). See also Douglas W. Smith and Gary Ferguson, Decade of the Wolf: Returning the Wild to Yellowstone (Guilford, CT: The Lyons Press, 2005). 14.

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

“Each of the problems had an objectively correct answer,” Amos and Danny wrote, after they were done with their strange mini-experiments. “This is not the case in many real-life situations where probabilities are judged. Each occurrence of an economic recession, a successful medical operation, or a divorce, is essentially unique, and its probability cannot be evaluated by a simple tally of instances. Nevertheless, the availability heuristic may be applied to evaluate the likelihood of such events. “In judging the likelihood that a particular couple will be divorced, for example, one may scan one’s memory for similar couples which this question brings to mind. Divorces will appear probable if divorces are prevalent among the instances that are retrieved in this manner.”

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This particular rule they used to judge probabilities (the easier it is for me to retrieve from my memory, the more likely it is) often worked well. But if you presented people with situations in which the evidence they needed to judge them accurately was hard for them to retrieve from their memories, and misleading evidence came easily to mind, they made mistakes. “Consequently,” Amos and Danny wrote, “the use of the availability heuristic leads to systematic biases.” Human judgment was distorted by . . . the memorable. Having identified what they took to be two of the mind’s mechanisms for coping with uncertainty, they naturally asked: Are there others? Apparently they were unsure. Before they left Eugene, they jotted down some notes about other possibilities.

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“We really couldn’t think of others,” said Danny. “There seemed to be very few mechanisms.” Just as they never tried to explain how the mind forms the models that underpinned the representativeness heuristic, they left mostly to one side the question of why human memory worked in such a way that the availability heuristic had such power to mislead us. They focused entirely on the various tricks it could play. The more complicated and lifelike the situation a person was asked to judge, they suggested, the more insidious the role of availability. What people did in many complicated real-life problems—when trying to decide if Egypt might invade Israel, say, or their husband might leave them for another woman—was to construct scenarios.

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

However, heuristics can also lead investors to make biased decisions. One facet of successful decision making is gaining an understanding of these biases so as to mitigate their cost.7 The availability heuristic allows investors to assess the frequency or likely cause of an event by the degree to which similar events are “available” in memory. Ease of recall is one bias that emanates from the availability heuristic. In other words, investors or managers may place greater emphasis on information that is available than on information that is relevant. I believe this bias is at the heart of the janitor’s-dream problem.

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See also complex adaptive systems adaptive decision rules advertising affect after-tax measures agency costs agent-based models aggregate return aggregation Alliance Capital analysts, imitation and analytical decision making anchoring Anderson, Philip anomalies ante ant examples appropriate reference class arbitrage Ariane rocket Arrow, Kenneth Arthur, W. Brian Asch, Solomon Asch experiment asset life, average asset price distributions As the Future Catches You (Enriquez) attribute-based approach authority automobile industry availability heuristic averages Axtell, Rob Babe Ruth effect baboons Baer, Gregory Bak, Per Barlow, Horace baseball basketball Beat the Dealer (Thorp) beauty-contest metaphor behavioral finance. See also loss aversion; psychology of investing behaviors: anchoring; certainty and; herding; information overload; pattern-seeking Beinhocker, Eric belief bell curve Benartzi, Shlomo Bernoulli, Daniel Bernstein, Peter Bernstein, William BetFair Bet with the Best (Crist) Bezos, Jeff blackjack Black-Scholes options-pricing model Bogle, Jack C.

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

“Respondents put too much confidence in the result of small samples and their statistical judgment showed little sensitivity to sample size.”The puzzling aspect is that not only should they have known better, “they did know better.” And yet . . . I will next list a few more heuristics. (1) The availability heuristic, which we saw in Chapter 3 with the earthquake in California deemed more likely than catastrophe in the entire country, or death from terrorism being more “likely” than death from all possible sources (including terrorism). It corresponds to the practice of estimating the frequency of an event according to the ease with which instances of the event can be recalled. (2) The representativeness heuristic: gauging the probability that a person belongs to a particular social group by assessing how similar the person’s characteristics are to the “typical” group member’s.

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Simpson had 1/500,000 chance of not being the killer from the blood standpoint (remember the lawyers used the sophistry that there were four people with such blood types walking around Los Angeles) and adding to it the fact that he was the husband of the person and that there was additional evidence, then (owing to the compounding effect) the odds against him rise to several trillion trillion. “Sophisticated” people make worse mistakes. I can surprise people by saying that the probability of the joint event is lower than either. Recall the availability heuristic: with the Linda problem rational and educated people finding the likelihood of an event greater than that of a larger one that encompasses it. I am glad to be a trader taking advantage of people’s biases but I am scared of living in such a society. An Absurd World Kafka’s prophetic book, The Trial, about the plight of a man, Joseph K., who is arrested for a mysterious and unexplained reason, hit a spot as it was written before we heard of the methods of the “scientific” totalitarian regimes.

…

Risk and emotions: Given the growing recent interest in the emotional role in behavior, there has been a growing literature on the role of emotions in both risk bearing and risk avoidance: The “risk as feeling” theory: See Loewenstein, Weber, Hsee and Welch (2001), and Slovic, Finucane, Peters and MacGregor (2003a). For a survey, see Slovic, Finucane, Peters and MacGregor (2003b). See also Slovic (1987). For a discussion of the affect heuristic: See Finucane, Alhakami, Slovic and Johnson (2000). Emotions and cognition: For the effect of emotions on cognition, see LeDoux (2002). Availability heuristic (how easily things come to mind): Tversky and Kahneman (1973). Real incidence of catastrophes: For an insightful discussion, see Albouy (2002). On sayings and proverbs: Psychologists have long examined the gullibility of people in social settings facing well-sounding proverbs. For instance, experiments since the 1960s have been made where people are asked whether they believed that a proverb is right, while another cohort is presented the opposite meaning.

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

Availability How much should you worry about hurricanes, nuclear power, terrorism, mad cow disease, alligator attacks, or avian flu? And how much care should you take in avoiding risks associated with each? What, exactly, should you do to prevent the kinds of dangers that you face in ordinary life? In answering questions of this kind, most people use what is called the availability heuristic. They assess the likelihood of risks by asking how readily examples come to mind. If people can easily think of relevant examples, they are far more likely to be frightened and concerned than if they cannot. A risk that is familiar, like that associated with terrorism in the aftermath of 9/11, will be seen as more serious than a risk that is less familiar, like that associated with sunbathing or hotter summers.

…

Thus vivid and easily imagined causes of death (for example, tornadoes) often receive inflated estimates of probability, and less-vivid causes (for example, asthma attacks) receive low estimates, even if they occur with a far greater frequency (here a factor of twenty). So, too, recent events have a greater impact on our behavior, and on our fears, than earlier ones. In all these highly available examples, the Automatic System is keenly aware of the risk (perhaps too keenly), without having to resort to any tables of boring statistics. The availability heuristic helps to explain much risk-related behavior, including both public and private decisions to take precautions. Whether people buy insurance for natural disasters is greatly affected by recent experiences.6 In the aftermath of an earthquake, purchases of new earthquake insurance policies rise sharply—but purchases decline steadily from that point, as vivid memories recede.

…

A survey by the Harvard School of Public Health found that about 44 percent of college students engaged in binge drinking in the two-week period preceding the survey.16 This is, of course, a problem, but a clue to how to correct it lies in the fact that most students believe that alcohol abuse is far more pervasive than it actually is.17 Misperceptions of this kind result in part from the availability heuristic. Incidents of alcohol abuse are easily recalled, and the consequence is to inflate perceptions. College students are influenced by their beliefs about what other college students do, and hence alcohol abuse will inevitably increase if students have an exaggerated sense of how much other students are drinking.

Superforecasting: The Art and Science of Prediction
by Philip Tetlock and Dan Gardner
Published 14 Sep 2015

So we substitute an easier question: “Can I easily recall a lion attacking someone from the long grass?” That question becomes a proxy for the original question and if the answer is yes to the second question, the answer to the first also becomes yes. So the availability heuristic—like Kahneman’s other heuristics—is essentially a bait-and-switch maneuver. And just as the availability heuristic is usually an unconscious System 1 activity, so too is bait and switch.18 Of course we aren’t always oblivious to the machinations of our minds. If someone asks about climate change, we may say, “I have no training in climatology and haven’t read any of the science.

…

If that memory comes to you easily—it is not the sort of thing people tend to forget—you will conclude lion attacks are common. And then start to worry. Spelling out this process makes it sound ponderous, slow, and calculating but it can happen entirely within System 1—making it automatic, fast, and complete within a few tenths of a second. You see the shadow. Snap! You are frightened—and running. That’s the “availability heuristic,” one of many System 1 operations—or heuristics—discovered by Daniel Kahneman, his collaborator Amos Tversky, and other researchers in the fast-growing science of judgment and choice. A defining feature of intuitive judgment is its insensitivity to the quality of the evidence on which the judgment is based.

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

. … When people formed opinions about crime in their local area, they were more reliant on their own experiences or the experiences of people in their communities. However, when people formed perceptions of crime across the whole country their main source of information was the media.11 Newspapers feed pessimism by triggering our availability heuristic, a well-studied mental shortcut or rule of thumb that makes people prone to irrationality by forcing them to recall a few immediate examples of any event. This makes them wrongly believe that such events are more widespread than they are. For example, the media typically reports airplane crashes, shark attacks, and mass shootings as front page events, which results in people remembering them in complete disproportion to the odds of their actually being involved in one.

…

Polling firm Ipsos MORI annually publishes the rather amusing Perils of Perception Index, which asks respondents in numerous countries their views on issues such as the murder rate or the number of terrorist attacks relative to the past, or the share of immigrant prisoners and teenage births.14 Countries tend to do particularly bad on issues that garner considerable media attention, which further highlights the massive impact of the availability heuristic on our political sentiments. For example, with anti-immigrant rhetoric at all-time highs it’s not surprising that Americans think immigrants compose a much larger share of the prison population: nearly a third according to the index. However, the number is just 5.2%. Likewise, large majorities of people in Turkey, the Philippines, India, and Colombia — some of the countries which have been most affected by domestic or international terrorism in the past couple of decades — all think terrorist deaths have increased or remained about the same since 9/11 when in fact they have declined, often considerably so.

…

Part of the problem is that an entire billion-dollar industry has been created to reinforce this delusion, as evidenced by the thousands of managerial and leadership books that are published each year (and which look and feel exactly like self-help books), leadership seminars led by self-appointed gurus who both look and speak like the corporate versions of televangelists, business reality shows, and the endless glorification of entrepreneurs in the media — especially the ones who have mastered the art of self-promotion regardless of merit (the now disgraced Elizabeth Holmes of over-hyped medical startup Theranos being a recent example).39 The undeserved adulation and reward that Western societies heap on their business elite undoubtedly has a terrible impact on these people’s egos, in no less a manner than it has on that of despots, which permanently impairs their capacity for accepting criticism and failure and leads to decisions based on mere hubris. The end result is for these leaders to fall for all the optimism biases that were explained in Chapter Two, and for the rest of us to fall for the availability heuristic if all the stories we hear about them are the good ones. All in all, the idea that business leaders are an enlightened elite of supermen (or occasionally superwomen40) makes for good cover stories and management biographies, but the reality is much less romantic: All too often flesh-and-blood leaders fall short.

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

Philip Tetlock reports that grandmasters at chess have 50,000–100,000 patterns stored in this deep memory system.2 But when we incorrectly see a familiar framing for a new kind of problem, we risk disastrously wrong solutions or endless work getting back on track. This kind of mistake is sometimes called an availability heuristic (you use the framework you happen to have handy, not the right one) or substitution bias (you substitute a simple model you know rather than understanding the more complicated actual model). And even when there is no absolute cognitive mistake in problem framing, excessive problem patterning is a block on novel solutions and creativity in problem solving.

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See Anxious parade of knowledge Arguments structuring, 187e types, 188e Arthroscopic partial meniscectomies (APM), 125–127 options, 126 Arthroscopy, success, 125 Artifacts, 69e, 70e Artificial intelligence role, 204 techniques, learning algorithms, 90–91 Assets/options (cleaving frame example), 76 Associated capability platform, 221e AT&T, lawsuit, 171 Audiences, interaction, 190–191 Automation, role, 204 Avahan HIV project (India), problem aperture, widening, 41–42 framing, 42e Availability bias, 101 Availability heuristic, 100 B Back of the envelope calculation, 169 Baer, Tobias, xxii Baghai, Mehrdad, 169, 203, 220 Balancing, 71 moral balancing, 71 Baur, Louise, 242 Bay Area nursing‐related patient outcomes, 90 case study, 66–67 levers, strategies, 66 nursing outcomes, improvement, 67e workplan detail, 92e Bayesian statistics, 136, 140 case study, 141, 146–149 Bayesian thinking, 118–119 Bazerman, M.H., 216 Behavioral interventions, 240 Belkin, Douglas, xviii Bell, Michael, 246 Beyond visual line of sight (BVLOS), 224, 226 BHP mining, xxiii, 184 capital, raising (cost), 214 development options, 218–219, 219e long‐term investment, 214 valuation scenarios, 217e value, 215e Bias/error (avoidance), team behavior (usage), 99–100 Biases discussion, 218 interaction, 101–106, 102e tackling, 107e types, 101 Big bets, 197, 201, 206 Big Short, The (Lewis), 201 Bill and Melinda Gates Foundation, 42 Black‐Scholes valuation, 216 Black Swan, The (Taleb), 105 Body mass index (BMI), 144, 160 Book of Why, The (Pearl/Mackenzie), 150 Borrower default, prediction, 165 Bossidy, Larry, xv Boston Public School System, cost savings, 161 Boucher, Wayne I., 199 Bradley, Chris, 201 Brainstorming, 18e, 240 session, 53 structure, 97 Branches, 55 Brandenberger, Adam, 200 Break‐even point, 120 Breakthrough thinking, 99 Broockman, David, 153, 156 Brooks, David, xvii Buffalo Technology, patent infringement, 169–170 Buffett, Warren, 115 Bulletproof problem solving cycle, 4–7, 4e steps, 5–7 Business building, staircase strategy approach, 203 competitive strategy, 169 models, challenge, 119 new businesses (building), strategic staircases (usage), 220–226 problems, focus, 75–77 Butland, Bryony, 236 BVLOS, 225e, 226 C CAE.

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

The amount of space that people need increases predictably over time as they find partners and have children; it makes sense to buy early in order to protect themselves against the risk of future price increases that would make houses unaffordable. When prices start going up, another behavioral bias starts to kick in. The “availability heuristic” captures the propensity of people to assess situations by referring to examples that come readily to mind. A 2008 paper by Hugo Benitez-Silva, Selcuk Eren, Frank Heiland, and Sergi Jiménez-Martín used the Health and Retirement Study, a biennial survey of Americans over the age of fifty, to compare people’s estimates of the value of their homes with actual values when a sale took place.

…

Index AAA credit ratings, 49–51, 233–236 AARP Public Policy Institute, report on home ownership by, 139 Abacus, 235 Accenture, 54, 56 Adaptive-market hypothesis, 115–116 Adelino, Manuel, 49 Adoption, SIB program for, 97 Adverse selection, 21, 174, 175, 182 AIG (American International Group), 65 AIR Worldwide, 222, 225 Alabama, land boom in, 74–75 Algorithms, 53–54, 56–57, 62–63, 113, 202, 216–217 Alibaba.com, 219 Allia, 108 Alzheimer’s disease, megafund for, 122 Amazon, 162, 216–217, 219 American Diabetes Association, 102 American Dream Downpayment Act of 2003, 78 American International Group (AIG), 65 American Railroad Journal, 24 American Research and Development Corporation, 150 Amsterdam Stock Exchange, 14–15, 24, 38 Anchoring effect, 137–138 Annuities, 20–22, 139 Apax Partners, 91 Aristotle, 10 Asian debt crisis (1990s), x, 30 Asian Development Bank, 27 Auto-enrollment in pension schemes, 135 Auto-escalation, 135–136 Availability heuristic, 73 Baby boomers, retirement rate of, 125 Bailouts, xi, 35, 65 Bank, derivation of word, 12 Bank deregulation, effect of on college enrollments, 171 Bank for International Settlements (BIS), 224, 226 Bank of America, 98 Banks advantages of, 192–193 bailouts of, xi commercial paper, use of, 185 competition, response to, 193–194 crisis, episodes of, 35–36 in Dark Ages, 11 deposits, xiv, 12–13 equity, 186–187 innovator’s dilemma, 189 leverage, 50, 70–71, 80, 186, 188 liquidity and, 12–14, 185–186, 193 operating expenses, 188 profits of, ix property and, xiv, 69, 75–80 public attitudes toward, ix, xi purpose of, 11–14 raising returns in, 51 repurchase “repo” markets, 15, 185 runs on, x, 13, 185 secured lending, xiv unbanked households, 200 Barbon, Nicholas, 16–17 Basel accords, 77 Basildon, England, 52–53, 58 Bass, Oren, 166, 168 Behavioral finance, 132–138, 208–214 Belinsky, Michael, 103 Benartzi, Shlomo, 136 Benitez-Silva, Hugo, 73 Bernoulli, Jacob, 18 Betting on Lives (Clark), 144 Bid-ask spreads, 55 Big data, xviii, 22, 47, 199, 201, 218, 236 Big Society Capital, 95 Biotechnology, decline in investment in, xii-xiii, 114–115 Black, Fischer, 31, 123–124 Black Monday, October, 1987, 62 BlackRock, 132 Black-Scholes equation, 31, 32, 124 Blackstone, 85 Blood donation, experiment with, 110 Bloomberg, Michael, 98 Bonds attractiveness to investors, 120 catastrophe, 224–227 income, 25 inflation protected, 26 samurai, 27 Book of Calculation (Fibonacci), 19 Bottomry, 8 Brain, reaction of to monetary rewards, 116 Brazil, financial liberalization of, 34 Breslow, Noah, 216, 219 Bretton Woods system, 30 Bridges Ventures, 93 Britain average age of first-time home buyer, 84 average house price, 74 banking crisis, 69 equity-crowdfunding, 154 government spending, 99 life expectancy, 125 peer-to-peer lending, 181 social-impact bonds (SIB), 95–97 student indebtedness, 171 total residential property value, 69–70 Brown, Gordon, 93 Bucket price (okenedan), 40 Bullae (early financial contracts), 5 Bush, George W., 78 Byng, John, 143 Call options, 9–10, 131 Calment, Jeanne, 144 Cameron, David, 95 Cancer megafund.

The Behavioral Investor
by Daniel Crosby
Published 15 Feb 2018

If that’s the case, why is it so much easier to create a list of words that start with K? It turns out that our mind’s retrieval process is far from perfect and that a number of cognitive quirks play into our ability to recall information. Psychologists call this fallibility in your memory retrieval mechanism the availability heuristic, which simply means that we predict the likelihood of an event based on how easily we can call it to mind rather than how probable it is. Kahneman and Tversky first observed this effect in their 1973 paper where they noted that an information signal is salient (i.e., memorable) if it has characteristics that differ from the background or a past state.

…

Evidence of the usefulness of the WWW was everywhere, making it easy to create internal narratives about how the internet could be paradigm changing. Likewise, we see the effects of black swan events like the Great Recession linger in the public consciousness for years after the fact, unusual and impactful as they are. Unfortunately for us, the imperfections of the availability heuristic are hard at work as we attempt to gauge the riskiness of different ways of living and investing. The power of story The basic premise of the attention pillar is that we make probability-insensitive judgments because of our reliance on information that is vivid over information that is factually accurate.

The Science of Fear: How the Culture of Fear Manipulates Your Brain
by Daniel Gardner
Published 23 Jun 2009

You’ll just have an uneasy feeling that taking a walk is dangerous—a feeling you would have trouble explaining to someone else. What System One did is apply a simple rule of thumb: If examples of something can be recalled easily, that thing must be common. Psychologists call this the “availability heuristic.” Obviously, System One is both brilliant and flawed. It is brilliant because the simple rules of thumb System One uses allow it to assess a situation and render a judgment in an instant—which is exactly what you need when you see a shadow move at the back of an alley and you don’t have the latest crime statistics handy.

…

Like the paper itself, the three rules of thumb it revealed were admirably simple and clear. The first—the Anchoring Rule—we’ve already discussed. The second is what psychologists call the “representativeness heuristic,” which I’ll call the Rule of Typical Things. And finally, there is the “availability heuristic,” or the Example Rule, which is by far the most important of the three in shaping our perceptions and reactions to risk. THE RULE OF TYPICAL THINGS Linda is thirty-one years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

…

And it’s downright bizarre that people don’t rush to get insurance when scientists issue warnings. At least, it makes no sense to Head. To Gut, it makes perfect sense. One of Gut’s simplest rules of thumb is that the easier it is to recall examples of something, the more common that something must be. This is the “availability heuristic,” which I call the Example Rule. Kahneman and Tversky demonstrated the influence of the Example Rule in a typically elegant way. First, they asked a group of students to list as many words as they could think of that fit the form _ _ _ _ _ n _. The students had 60 seconds to work on the problem.

How Markets Fail: The Logic of Economic Calamities
by John Cassidy
Published 10 Nov 2009

Dramatic and salient events of any kind stick in their minds, whereas they are apt to downplay everyday happenings. Since 9/11, for example, many Americans fret more about being killed in a terrorist attack than in a road accident, even though in advanced countries the latter outcome is roughly four hundred times more likely. Kahneman and Tversky refer to this sort of thing as the “availability heuristic.” When thinking about the dangers we face, getting blown up comes to mind more readily than getting run over: it is more available. Kahneman and Tversky also pointed out that people have a general inclination to judge things relative to arbitrary reference points. When the status quo is their reference point, people tend to assume that things won’t change very much—the bias of conservatism.

…

Hubris comes in many forms. Especially when the economy is doing well, businesses and individuals find it increasingly difficult to imagine that anything very bad could happen—a phenomenon known as “disaster myopia.” The representativeness heuristic obviously plays a role here, but so does the availability heuristic. By definition, low-probability events such as stock market crashes and credit crunches occur rarely, which means that many people don’t have any personal experience of them to draw on. After the 1981–82 recession, it was almost twenty years before the stock market underwent another lengthy downturn.

…

But Guttentag and Herring pointed out that the banks tend to underestimate the chances of a systemic shock that could render many of their lenders simultaneously unable to repay their loans, such as the American economy plunging into a deep recession, or a sovereign government defaulting on its loans. When the economy is growing strongly, such a possibility is difficult to imagine (the availability heuristic), and bankers downplay it. Eventually, the danger comes to be seen as so remote that it is ignored (the threshold heuristic), and banks take on too much lending exposure relative to their capital. Myopia is another mental trait that behavioral economists have examined. In the late 1990s and early 2000s, the U.S. personal savings rate fell sharply.

Pedigree: How Elite Students Get Elite Jobs
by Lauren A. Rivera
Published 3 May 2015

Moreover, Americans often conflate the idea of an economic class structure with that of a caste structure; if there is any mobility, it is taken as evidence of a lack of the former.4 And indeed there is some minor fluidity between strata. Most people can think of at least one person they know (often from one or more generations ago) who rose to riches despite modest means or fell from positions of affluence. In line with what behavioral economists call the availability heuristic, we tend to overgeneralize from these familiar cases to believe that mobility is far more common (and possible) than it is.5 The reality is that in any class system, including ours, mobility happens.6 But the deck is stacked against it. Chances are that children will end up in the same economic quintile as their parents.7 Furthermore, studying economic elites tends to be problematic in the United States not only because of the types of ideological barriers noted above but also because, unlike the United Kingdom, for example, the United States lacks historic metrics for measuring relative class position.

…

Rather, decisions turn on interviewers’ subjective interpretations of interviews and candidates. Moreover, people typically draw from cognitive attributions and interpretations when making complex, high-stakes decisions and those where there is a sense of personal accountability (Leach and Tiedens 2004), such as in the hiring decisions analyzed here. 11. This is a subset of the availability heuristic. For a review of this and other cognitive biases in decision making, see Kahneman 2011. 12. Chen and Miller 2012; Duckworth et al. 2007. 13. Durkheim (1912) 1995. See also Collins 2004. 14. Granfield 1992; Lubrano 2005. 15. Phillips, Rothbard, and Dumas 2009. 16. Bourdieu 1984. 17. Lamont (1992) finds a similar aversion to individuals who are motivated by money rather than personal enjoyment and moral character among American, upper-middle-class professionals and managers. 18.

…

See candidates Armstrong, Elizabeth, 13 arrogance, 178–79, 199 autobiographical narratives, 147–82, 259, 269; assessment of drive in, 147, 149–61, 333–34nn10–11; assessment of interest in firm in, 147, 161–69, 255–56, 335n17; assessment of polish in, 147, 170–81, 335n19; compelling qualities of, 148–55, 334n10; cultural dimensions of, 148; evidence of growth and maturation in, 153–54; evidence of knowledge of firm in, 165–68; resonance with interviewers of, 149, 155–56, 165–66, 182, 257–58, 335n11; vivid images of obstacles in, 156–61 availability heuristic, 287, 335n11 baller lifestyle, 59–62, 272 “better match” hypothesis, 49 Biglaw. See law firms Blake Thomas (fictitious candidate), 89, 92; extracurricular activities of, 94–95, 97; résumé of, 303f boasting, 179 bounded excitement, 176–78, 336n27 Bourdieu, Pierre: on class-based linguistic and interaction styles, 180; on class-specific cultural values, 7, 268; on distance from necessity, 163–64, 269, 330n16; on embodied cultural capital, 7, 316–17n31; on habitus, 330n29; on institutionalized cultural capital, 110, 330n32, 335n21; on objectified cultural capital, 316–17n31; on the power of consecration, 318n45 brainpower, 87–90 breaking the ice.

Artificial Unintelligence: How Computers Misunderstand the World
by Meredith Broussard
Published 19 Apr 2018

I wondered if the dog would go berserk and, if so, what would happen. The story affected my perception of risk. This is the same thinking that leads people to carry pepper spray after watching a lot of episodes of Law & Order: SVU or to check the back seat of the car for nasty surprises after watching a horror movie. The technical name is the availability heuristic.15 The stories that spring to mind first are the ones we tend to think are the most important or occur most frequently. Perhaps because it features so prominently in our collective imagination, the Titanic disaster is commonly used for teaching machine learning. Specifically, a list of the passengers on the Titanic is used to teach students how to generate predictions using data.

…

W., 46–47 Angwin, Julia, 154–156 App hackathons, 165–174 Apple Watch, 157 Artificial intelligence (AI) beginnings, 69–73 expert systems, 52–53, 179 fantasy of, 132 in film, 31, 32, 198 foundations of, 9 future of, 194–196 games and, 33–37 general, 10–11, 32 narrow, 10–11, 32–33, 97 popularity of, 90 real vs. imagined, 31–32 research, women in, 158 sentience challenge in, 129 Asimov, Isaac, 71 Assembly language, 24 Association for Computing Machinery (ACM), 145 Astrolabe, 76 Asymmetry, positive, 28 Automation technology, 176–177 Autopilot, 121 Availability heuristic, 96 Babbage, Charles, 76–77 Bailiwick (Broussard), 182–185, 190–191, 193 Barlow, John Perry, 82–83 Bell Labs, 13 Bench, Shane, 84 Ben Franklin Racing Team (Little Ben), 122–127 Berkman Klein Center (Harvard), 195 Berners-Lee, Tim, 4–5, 47 Bezos, Jeff, 73, 115 Bias in algorithms, 44, 150, 155–157 in algorithms, racial, 44, 155–156 genius myth and, 83–84 programmers and, 155–158 in risk ratings, 44, 155–156 in STEM fields, 83–84 Bill & Melinda Gates Foundation, 60–61, 157 Bipartisan Campaign Reform Act, 180 Bitcoin, 159 Bizannes, Elias, 165, 166, 171 Blow, Charles, 95 Boggs, David, 67–68 Boole, George, 77 Boolean algebra, 77 Borden, Brisha, 154–155 Borsook, Paulina, 82 Bowhead Systems Management, 137 boyd, danah, 195 Bradley, Earl, 43 Brains 19–20, 95, 128–129, 132, 144, 150 Brand, Stewart, 5, 29, 70, 73, 81–82 Brin, Sergei, 72, 151 Brown, Joshua D., 140, 142 Bump, Philip, 186 Burroughs, William S., 77 Burroughs, William Seward, 77 Calculation vs. consciousness, 37 Cali-Fame, 186 California, drug use in, 158–159 Cameron, James, 95 Campaign finance, 177–186, 191 Čapek, Karel, 129 Caprio, Mike, 170–171 Carnegie Mellon University, autonomous vehicle research ALVINN, 131 University Racing Team (Boss), 124, 126–127, 130–131 Cars deaths associated with, 136–138, 146 distracted driving of, 146 human-centered design for, 147 Cars, self-driving 2005 Grand Challenge, 123–124 2007 Grand Challenge, 122–127 algorithms in, 139 artificial intelligence in, 129–131, 133 deaths in, 140 driver-assistance technology from, 135, 146 economics of, 147 experiences in, 121–123, 125–126, 128 fantasy of, 138, 142, 146 GPS hacking, 139 LIDAR guidance system, 139 machine ethics, 144–145, 147 nausea in, 121–123 NHTSA categories for, 134 problems/limitations, 138–140, 142–146 research funding, 133 SAE standards for levels of automation, 134–135 safety, 136–137, 140–142, 143, 146 sentience in, 132 Uber’s use of, 139 Udacity open-source car competition, 135 Waymo technology, 136 CERN, 4–5 Cerulo, Karen A., 28 Chess, 33 Children’s Online Privacy Protection Act (COPPA), 63–64 Chinese Room argument, 38 Choxi, Heteen, 122 Christensen, Clayton, 163 Chrome, 25, 26 Citizens United, 177, 178, 180 Clarke, Arthur C., 71–72 Client-server model, 27 Clinkenbeard, John, 172 Cloud computing, 26, 52, 196 Cohen, Brian, 56–57 Collins, John, 117 Common Core State Standards, 60–61 Communes, 5, 10 Computer ethics, 144–145 Computer Go, 34–36 Computers assumptions about vs. reality of, 8 components, identifying, 21–22 consciousness, 17 early, 196–199 human, 77–78, 198 human brains vs., 19–20, 128–129, 132, 144, 150 human communication vs., 169–170 human mind vs., 38 imagination, 128 limitations, 6–7, 27–28, 37–39 memory, 131 modern-day, development of, 75–79 operating systems, 24–25 in schools, 63–65 sentience, 17, 129 Computer science bias in, 79 ethical training, 145 explaining the world through, 118 women in, 5 Consciousness vs. calculation, 37 Constants in programming, 88 Content-management system (CMS), 26 Cooper, Donna, 58 Copeland, Jack, 74–75 Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), 44, 155–156 Cortana, 72 Counterculture, 5, 81–82 Cox, Amanda, 41–42 Crawford, Kate, 194 Crime reporting, 154–155 CTB/McGraw-Hill, 53 Cumberbatch, Benedict, 74 Cyberspace activism, 82–83 DarkMarket, 159 Dark web, 82 Data on campaign finance, 178–179 computer-generated, 18–19 defined, 18 dirty, 104 generating, 18 people and, 57 social construction of, 18 unreasonable effectiveness of, 118–119, 121, 129 Data & Society, 195 DataCamp, 96 Data density theory, 169 Data journalism, 6, 43–47, 196 Data Journalism Awards, 196 Data journalism stories cost-benefit of, 47 on inflation, 41–42 Parliament members’ expenses, 46 on police speeding, 43 on police stops of people of color, 43 price discrimination, 46 on sexual abuse by doctors, 42–43 Data Privacy Lab (Harvard), 195 Data Recognition Corporation (DRC), 53 Datasets in machine learning, 94–95 Data visualizations, 41–42 Deaths distracted driving accidents, 146 from poisoning, 137 from road accidents, 136–138 in self-driving cars, 140 Decision making computational, 12, 43, 150 data-driven, 119 machine learning and, 115–116, 118–119 subjective, 150 Deep Blue (IBM), 33 Deep learning, 33 Defense Advanced Research Projects Agency (DARPA) Grand Challenge, 123, 131, 133, 164 Desmond, Matthew, 115 Detroit race riots story, 44 Dhondt, Rebecca, 58 Diakopoulos, Nicholas, 46 Difference engine, 76 Differential pricing and race, 116 Digital age, 193 Digital revolution, 193–194 Dinakar, Karthik, 195 Django, 45, 89 DocumentCloud, 52, 196 Domino’s, 170 Drone technology, 67–68 Drug marketplace, online, 159–160 Drug use, 80–81, 158–160 Duncan, Arne, 51 Dunier, Mitchell, 115 Edison, Thomas, 77 Education change, implementing in, 62–63 Common Core State Standards, 60–61 competence bar in, 150 computers in schools, 63–65 equality in, 77–78 funding, 60 supplies, availability of, 58 technochauvinist solutions for, 63 textbook availability, 53–60 unpredictability in, 62 18F, 178–179 Electronic Frontier Foundation, 82 Elevators, 156–157 Eliza, 27–28 Emancipation Proclamation, 78 Engelbart, Doug, 25, 80–81 Engineers, ethical training, 145 ENIAC, 71, 194, 196–199 Equality in education, 77–78 techno hostility toward, 83 technological, creating, 87 technology vs., 115, 156 for women, 5, 77–78, 83–85, 158 Essa, Irfan, 46 Ethics, 144–145, 147 EveryBlock, 46 Expertise, cognitive fallacies associated, 83 Expert systems, 52–53, 179 Facebook, 70, 83, 152, 158, 197 Facial recognition, 157 Fact checking, 45–46 Fake news, 154 Family Educational Rights and Privacy Act (FERPA), 63–64 FEC, McCutcheon v., 180 FEC, Speechnow.org v., 180 FEC.gov, 178–179 Film, AI in, 31, 32, 198 FiveThirtyEight.com, 47 Foote, Tully, 122–123, 125 Ford Motor Company, 140 Fowler, Susan, 74 Fraud campaign finance, 180 Internet advertising, 153–154 Free press, role of, 44 Free speech, 82 Fuller, Buckminster, 74 Futurists, 89–90 Games, AI and, 33–37 Gates, Bill, 61 Gates, Melinda, 157–158 Gawker, 83 Gender equality, hostility toward, 83 Gender gap, 5, 84–85, 115, 158 Genius, cult of, 75 Genius myth, 83–84 Ghost-in-the-machine fallacy, 32, 39 Giffords, Gabby, 19–20 GitHub, 135 Go, 33–37 Good Old-Fashioned Artificial Intelligence (GOFAI), 10 Good vs. popular, 149–152, 160 Google, 72 Google Docs, 25 Google Maps API, 46 Google Street View, 131 Google X, 138, 151, 158 Government campaign finance, 177–186, 191 cyberspace activism, antigovernment ideology, 82–83 tech hostility toward, 82–83 Graphical user interface (GUI), 25, 72 Greyball, 74 Guardian, 45, 46 Hackathons, 165–174 Hackers, 69–70, 82, 153–154, 169, 173 Halevy, Alon, 119 Hamilton, James T., 47 Harley, Mike, 140 Harris, Melanie, 58–59 Harvard, Andrew, 184 Harvard University Berkman Klein Center, 195 Data Privacy Lab, 195 mathematics department, 84 “Hello, world” program, 13–18 Her, 31 Hern, Alex, 159 Hernandez, Daniel, Jr., 19 Heuristics, 95–96 Hillis, Danny, 73 Hippies, 5, 82 HitchBOT, 69 Hite, William, 58 Hoffman, Brian, 159 Holovaty, Adrian, 45–46 Home Depot, 46, 115, 155 Hooke, Robert, 88 Houghton Mifflin Harcourt (HMH) HP, 157 Hugo, Christoph von, 145 Human-centered design, 147, 177 Human computers, 77–78, 198 Human error, 136–137 Human-in-the-loop systems, 177, 179, 187, 195 Hurst, Alicia, 164 Illinois quarter, 153–154 Imagination, 89–90, 128 Imitation Game, The (film), 74 Information industry, annual pay, 153 Injury mortality, 137 Innovation computational, 25 disruptive, 163, 171 funding, 172–173 hackathons and, 166 Instacart, 171 Intelligence in machine learning Interestingness threshold, 188 International Foundation for Advanced Study, 81 Internet advertising model, 151 browsers, 25, 26 careers, annual pay rates, 153 core values, 150 drug marketplace, 159–160 early development of the, 5, 81 fraud, 153–154 online communities, technolibertarianism in culture of, 82–83 rankings, 72, 150–152 Internet Explorer, 25 Internet pioneers, inspiration for, 5, 81–82 Internet publishing industry, annual pay, 153 Internet search, 72, 150–152 Ito, Joi, 147, 195 Jacquard, Joseph Marie, 76 Java, 89 JavaScript, 89 Jobs, Steve, 25, 70, 72, 80, 81 Jones, Paul Tudor, 187–188 Journalism.

AIQ: How People and Machines Are Smarter Together
by Nick Polson and James Scott
Published 14 May 2018

Musk has claimed that in developing AI technology, humanity is “summoning a demon,” and that smart machines are “our biggest existential threat” as a species. After you’ve read our book, you’ll be able to decide for yourself whether you think these worries are credible. We want to warn you up front, however, that it’s very easy to fall into a trap that cognitive scientists call the “availability heuristic”: the mental shortcut in which people evaluate the plausibility of a claim by relying on whatever immediate examples happen to pop into their minds. In the case of AI, those examples are mostly from science fiction, and they’re mostly evil—from the Terminator to the Borg to HAL 9000. We think that these sci-fi examples have a strong anchoring effect that makes many people view the “evil AI” narrative less skeptically than they should.

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See Great Andromeda Nebula anomaly detection averaging bias (type of anomaly) coin clipping and Formula 1 racing and fraud and importance of variability law enforcement and Moneyball NBA and overdispersion (type of anomaly) Patriots coin toss record and simulated coin toss record smart cities and square-root rule (de Moivre’s equation) and Trial of the Pyx (Royal Mint fraud protection) Apple data storage iPhone market dominance pattern-recognition system Aristophanes: The Frogs artificial intelligence (AI) algorithms and anxieties regarding assumptions and bias in, bias out contraception and criminal justice system and democratization of diffusion and dissemination of enabling technological trends image classification image recognition model rust models versus reality Moravec paradox policy rage to conclude bias robot cars salaries SLAM problem (simultaneous localization and mapping) speech recognition talent and workforce twenty questions game and See also anomaly detection; Bayes’s rule; health care and medicine; natural language processing (NLP); pattern recognition; personalization; prediction rules assumptions astronomy Alpha Centauri Bayes’s rule and Great Andromeda Nebula Leavitt’s original equation Leavitt’s prediction rule data measuring stars Milky Way nebulae oscillation of a pulsating star parallax pulsating stars statistics and Athey, Alex automation. See also robotics autonomous cars. See robot cars availability heuristic Baidu base-rate neglect Bayes’s rule coin flips and discovery of as an equation investing and mammograms and medical diagnostics and robot cars and search for Air France Flight 447 and search for USS Scorpion and usefulness of Bayesian search essential steps of posterior probabilities prior beliefs and revised beliefs prior beliefs and search for USS Scorpion prior probabilities Bel Geddes, Norman Belichick, Bill Bell Labs BellKor’s Pragmatic Chaos Berglund Scherwitzl, Elina Bernoulli, Johann big data.

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

In a previous study on a campus parking lot—a lot that was crowded but usually had some spaces in the back rows—Velkey polled students about how long they thought it typically took them to find a parking space. “They said four and a half minutes,” Velkey told me. “In reality, when we watched them, it takes about thirty seconds. I said, ‘Where did that extra four minutes come from?’” Velkey suggests that the psychological principle known as the “availability heuristic” was at work. Students were tending to remember the few times when it was very difficult to find a spot, instead of the everyday experience in which it was quite easy. They were remembering the things that stuck out in their memory. In the Wal-Mart lot, there was something else interesting about the two groups of parkers.

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Kobza, “A Probabilistic Approach to Evaluate Strategies for Selecting a Parking Space,” Transportation Science, vol. 32, no. 1 (January 1998), pp. 30–42. to walk somewhere: Travel Behaviour Research Baseline Survey 2004: Sustainable Travel Demonstration Towns (SUSTRANS and Socialdata, 2004). Retrieved from http://www.sustrans.org.uk/webfiles/travelsmart/STDT%20Research%20FINAL.pdf. was at work: The “availability heuristic” is credited to Daniel Kahneman and Amos Tversky. (Heuristic is a sophisticated-sounding word that really just means “mental shortcut.”) When people are asked to imagine how often something happens, they tend to overestimate the probability of things that can be more easily recalled from memory—that is, that are “available”—or that loom more vividly in the imagination.

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responsibility in the crash: Daniel Blower, “The Relative Contribution of Truck Drivers and Passenger Vehicles to Truck-Passenger Vehicle Traffic Crashes,” report prepared for the U.S. Department of Transportation, Federal Highway Administration, Office of Motor Carriers, June 1998. is actually the case): This may be the “availability heuristic” at work again. Large trucks, in part because they are driven longer distances and tend to be on the road at the same time as most motorists, seem to be more prevalent than they really are. A Canadian study found that while motorists believed that the number of trucks on the roads was rising, the number actually dropped during the period in question (while the number of cars grew).

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

Another biased heuristic cited by Kahneman and Tversky is that naive observers will expect that a coin toss is more likely to come out heads if they just experienced a run of tails.[42] This is due to a misunderstanding of regression to the mean. A third bias that explains much of society’s pessimistic skew is what Kahneman and Tversky call the “availability heuristic.” [43] People estimate the likelihood of an event or a phenomenon by how easily they can think of examples of it. For the reasons discussed previously the news and our news feeds emphasize negative events, so it is these negative circumstances that come readily to mind. That we should correct for these biases doesn’t mean we should ignore or underestimate real problems, but it provides strong rational grounds for optimism about humanity’s overall trajectory.

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Yet much of the public incorrectly perceives violence as getting worse. Pinker largely ascribes this to “historical myopia,” wherein people focus on more recent events that get more attention and remain unaware of even worse violent episodes deeper in the past.[170] Essentially, this is the availability heuristic in action. These misperceptions can in part be attributed to technologies of documentation: we have easy access to dramatic color videos of recent violent events. Compare this with the black-and-white photographs of the nineteenth century or, even before that, the text descriptions and relatively few paintings of earlier eras.

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See nuclear weapons atoms, 7, 30, 98, 246, 247, 249–50, 252, 334n attention mechanism in deep learning, 46 auditory recognition systems, 20 augmented reality (AR), 170–71, 222, 285 Australia poverty rate, 117 social safety net, 223, 223 autism, 242 autocracies, 163 autoimmune reactions and nanobots, 262 automation, 231–32, 253 jobs and, 196–99, 204, 207–11, 219, 221 autonomous vehicles, 43, 171, 195–96, 208–9, 214, 229–30, 253. See also self-driving cars availability heuristic, 121, 152 avatars, 100–105, 263 axons, 89 B Babbage, Charles, 293 backdoor life extension, 260 bacteria, 177, 178, 237, 262, 274 bad news bias, 114–16, 119–20 Bank for International Settlements (BIS), 217 banking, 198, 209 Bardem, Javier, 100 Barnes, Luke, 98 basis functions, 31 battery storage, 176 beach mice, 32 behavior, 32–34, 76, 77–80 Better Angels of Our Nature, The (Pinker), 151–53, 230–31 big bang, 7, 29–30, 97–98 big data, 2, 58–59 Bill of Rights, 161 BINAC, 165, 299 biofuels, 154, 173 biological frailties, 5, 285 biological simulation (biosimulation), 189–94, 240–41 biological weapons (bioweapons), 274 biomass, 249, 275, 397–98n biomolecular emulation, 104 biomolecules, 136 bioprinting, 186 biosphere, 274–75 biotechnology, 4, 5, 135.

Mastermind: How to Think Like Sherlock Holmes
by Maria Konnikova
Published 3 Jan 2013

(Consider also that Watson is a bachelor, just returned from war, wounded, and largely friendless. What would his chronic motivational state likely be? Now, imagine he’d been instead married, successful, the toast of the town. Replay his evaluation of Mary accordingly.) This tendency is a common and powerful one, known as the availability heuristic: we use what is available to the mind at any given point in time. And the easier it is to recall, the more confident we are in its applicability and truth. In one of the classic demonstrations of the effect, individuals who had read unfamiliar names in the context of a passage later judged those names as famous—based simply on the ease with which they could recall them—and were subsequently more confident in the accuracy of their judgments.

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INDEX activation, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9, ref10 activation spread, ref1, ref2 active perception, compared with passive perception, ref1 Adams, Richard, ref1 adaptability, ref1 ADHD, ref1 “The Adventure of the Abbey Grange,” ref1, ref2, ref3, ref4, ref5 “The Adventure of the Blue Carbuncle,” ref1, ref2 “The Adventure of the Bruce-Partington Plans,” ref1, ref2, ref3, ref4 “The Adventure of the Copper Beeches,” ref1, ref2, ref3 “The Adventure of the Creeping Man,” ref1 “The Adventure of the Devil’s Foot,” ref1, ref2 “The Adventure of the Dying Detective,” ref1 “The Adventure of the Mazarin Stone,” ref1 “The Adventure of the Norwood Builder,” ref1, ref2, ref3, ref4, ref5, ref6 “The Adventure of the Priory School,” ref1, ref2, ref3 “The Adventure of the Red Circle,” ref1, ref2, ref3, ref4, ref5 “The Adventure of the Second Stain,” ref1 “The Adventure of the Veiled Lodger,” ref1 “The Adventure of Wisteria Lodge,” ref1, ref2 affect heuristic, ref1 Anson, George, ref1 associative activation, ref1, ref2, ref3, ref4 astronomy, and Sherlock Holmes, ref1 Atari, ref1 attention, paying, ref1, ref2, ref3, ref4, ref5, ref6, ref7 attentional blindness, ref1 Auden, W. H., ref1 availability heuristic, ref1 Bacon, Francis, ref1 Barrie, J. M., ref1 base rates, ref1, ref2 Baumeister, Roy, ref1 Bavelier, Daphné, ref1 Bell, Joseph, ref1, ref2, ref3, ref4, ref5 Bem, Daryl, ref1, ref2 bias, implicit, ref1, ref2, ref3 BlackBerry, ref1 brain and aging process, ref1 baseline, ref1 cerebellum, ref1 cingulate cortex, ref1, ref2, ref3 corpus collosum, ref1 frontal cortex, ref1 hippocampus, ref1, ref2, ref3 parietal cortex, ref1 precuneus, ref1 prefrontal cortex, ref1 split, ref1, ref2, ref3 tempero-parietal junction (TPJ), ref1 temporal gyrus, ref1 temporal lobes, ref1 wandering, ref1, ref2 Watson’s compared with Holmes’, ref1 brain attic contents, ref1, ref2 defined, ref1 levels of storage, ref1 and memory, ref1 structure, ref1, ref2 System Watson compared with System Holmes, ref1, ref2 Watson’s compared with Holmes’s, ref1, ref2 Brett, Jeremy, ref1 capital punishment, ref1 Carpenter, William B., ref1 “The Case of the Crooked Lip,” ref1 cell phone information experiment, ref1 cerebellum, ref1 childhood, mindfulness in, ref1 cingulate cortex, ref1, ref2, ref3 cocaine, ref1 Cognitive Reflection Test (CRT), ref1, ref2 common sense, systematized, ref1, ref2 compound remote associates, ref1 Conan Doyle, Arthur becomes spiritualist, ref1 creation of Sherlock Holmes character, ref1 and fairy photos, ref1, ref2, ref3, ref4, ref5 and Great Wyrley sheep murders, ref1, ref2, ref3 and Joseph Bell, ref1, ref2, ref3, ref4, ref5 confidence, ref1, ref2.

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

Most people think the same. But actually, you’re in for a surprise. All of these estimates are wrong. Some of them by miles. Now ask yourself this. Of the kinds of fatality just described, which do you hear most about? Which are the most ‘available’ in your memory? 13It’s difficult to convey the power of the availability heuristic without a concrete example. So let’s take a look at one right now. Below is a list of names. Read them over carefully, and then as soon as you’ve done so cover them up with a sheet of paper: OK. Now that you’ve read the names try to recall as many of them as you can. Then estimate whether there were more women on the list, or men.

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It says: you are of value to me, rather than the other way round. And they think – why would I be of value to him? He’s already got everything he needs. He must really like me.’ Though on the surface mundane, what Khan does with his hands is actually quite intoxicating. For brown nosers, both representativeness and availability heuristics involve people of lower status sucking up to those of higher status. But confound that expectation, chuck in a bit of antithesis – switch on the hazard signs on those super-fast cognitive expressways – and suddenly, dramatically, we have to slam on the brakes. We have to make sense of the snarl-up. 14Psychologist David Strohmetz and his colleagues at Monmouth University have demonstrated a principle very similar to the one Khan uses.

The Irrational Economist: Making Decisions in a Dangerous World
by Erwann Michel-Kerjan and Paul Slovic
Published 5 Jan 2010

The floods in 1993 were much more severe than anything previously experienced. There should have been an update in the belief of residents there about the severity of future floods, a factor that would certainly affect housing values.5 Two subject areas in behavioral economics, prospect theory and the availability heuristic, help explain the over-updating of virgin risks and the under-updating of experienced risks after an extreme event. A finding of prospect theory is that individuals place excess weight on zero. The Russian Roulette problem illustrates this phenomenon. Most people are willing to pay more to remove one bullet from a six-cylinder gun when it is the only bullet than if there are two (or more) bullets in the gun.

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The theory of just noticeable differences explores such phenomena. For instance, as a noise gets louder, a greater change in volume is needed to make the change perceptible. This is similar to our argument that as base probabilities get larger, small changes in probability are not perceived as well. The availability heuristic also supports our conjecture. It asserts that individuals assess the probability of an event as higher when examples come to mind more readily. Once an event has occurred, it is much more salient, leading individuals to overestimate its probability. While the first occurrence of a risk makes it suddenly salient, the third occurrence, say, does not add much to its availability.

Licence to be Bad
by Jonathan Aldred
Published 5 Jun 2019

High earners may truly believe that they deserve their income because they are vividly aware of how hard they have worked and the obstacles they have had to overcome to be successful. Unfortunately, our memories play tricks, systematically biasing the narrative of the path to success that we construct. The biggest culprit is probably the availability heuristic (another one of Kahneman and Tversky’s major insights). When English speakers are asked whether English words beginning with the letter K are more frequent than words with K as the third letter, most choose the former. In fact, there are more words with K as the third letter, but words beginning with K are much more available to our minds because it is easier to think of examples.

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objection, 107, 119–20 Friedman, Milton, 4–5, 56, 69, 84, 88, 126, 189 awarded Nobel Prize, 132 and business responsibility, 2, 152 debate with Coase at Director’s house, 50, 132 as dominant Chicago thinker, 50, 132 on fairness and justice, 60 flawed arguments of, 132–3 influence on modern economics, 131–2 and monetarism, 87, 132, 232 at Mont Pèlerin, 5, 132 rejects need for realistic assumptions, 132–3 Sheraton Hall address (December 1967), 132 ‘The Methodology of Positive Economics’ (essay, 1953), 132–3 ‘The Social Responsibility of Business is to Increase Its Profits’ (article, 1970), 2, 152 Frost, Gerald, Antony Fisher: Champion of Liberty (2002), 7* Galbraith, John Kenneth, 242–3 game theory assumptions of ‘rational behaviour’, 18, 28, 29–32, 35–8, 41–3, 70, 124 Axelrod’s law of the instrument, 41 backward induction procedure, 36–7, 38 and Cold War nuclear strategy, 18, 20, 21–2, 24, 27, 33–4, 35, 70, 73, 198 focus on consequences alone, 43 as form of zombie science, 41 and human awareness, 21–3, 24–32 and interdependence, 23 limitations of, 32, 33–4, 37–40, 41–3 minimax solution, 22 multiplicity problem, 33–4, 35–7, 38 Nash equilibrium, 22–3, 24, 25, 27–8, 33–4, 41–2 the Nash program, 25 and nature of trust, 28–31, 41 the Prisoner’s Dilemma, 26–8, 29–32, 42–3 real world as problem for, 21–2, 24–5, 29, 31–2, 37–8, 39–40, 41–3 rise of in economics, 40–41 and Russell’s Chicken, 33–4 and Schelling, 138–9 and spectrum auctions, 39–40 theory of repeated games, 29–30, 35 tit-for-tat, 30–31 and trust, 29, 30–31, 32, 41 uses of, 23–4, 34, 38–9 view of humanity as non-cooperative/distrustful, 18, 21–2, 25–32, 36–8, 41–3 Von Neumann as father of, 18, 19, 20–22, 25, 26, 28, 30, 34, 41 zero-sum games, 21–2 Gates, Bill, 221–2 Geithner, Tim, 105 gender, 127–8, 130–31, 133, 156 General Electric, 159 General Motors (GM), 215–16 George, Prince of Cambridge, 98 Glass–Steagall Act, repeal of, 194 globalization, 215, 220 Goldman Sachs, 182, 184, 192 Google, 105 Gore, Al, 39 Great Reform Act (1832), 120 greed, 1–2, 196, 197, 204, 229, 238 Greenspan, Alan, 57, 203 Gruber, Jonathan, 245 Haifa, Israel, 158, 161 Harper, ‘Baldy’, 7 Harsanyi, John, 34–5, 40 Harvard Business Review, 153 Hayek, Friedrich and Arrow’s framework, 78–9 economics as all of life, 8 and Antony Fisher, 6–7 influence on Thatcher, 6, 7 and Keynesian economics, 5–6 and legal frameworks, 7* at LSE, 4 at Mont Pèlerin, 4, 5, 6, 15 and Olson’s analysis, 104 and public choice theory, 89 rejection of incentive schemes, 156 ‘spontaneous order’ idea, 30 The Road to Serfdom (1944), 4, 5, 6, 78–9, 94 healthcare, 91–2, 93, 178, 230, 236 hedge funds, 201, 219, 243–4 Heilbroner, Robert, The Worldly Philosophers, 252 Heller, Joseph, Catch-22, 98, 107, 243–4 Helmsley, Leona, 105 hero myths, 221–3, 224 Hewlett-Packard, 159 hippie countercultural, 100 Hoffman, Abbie, Steal This Book, 100 Holmström, Bengt, 229–30 homo economicus, 9, 10, 12, 140, 156–7 and Gary Becker, 126, 129, 133, 136 and behaviour of real people, 15, 136, 144–5, 171, 172, 173, 250–51 and behavioural economics, 170, 171, 172, 255 long shadow cast by, 248 and Nudge economists, 13, 172, 173, 174–5, 177 Hooke, Robert, 223 housing market, 128–9, 196, 240–41 separate doors for poor people, 243 Hume, David, 111 Huxley, Thomas, 114 IBM, 181, 222 identity, 32, 165–6, 168, 180 Illinois, state of, 46–7 immigration, 125, 146 Impossibility Theorem, 72, 73–4, 75, 89, 97 Arrow’s assumptions, 80, 81, 82 and Duncan Black, 77–8 and free marketeers, 78–9, 82 as misunderstood and misrepresented, 76–7, 79–82 ‘paradox of voting’, 75–7 as readily solved, 76–7, 79–80 Sen’s mathematical framework, 80–81 incentives adverse effect on autonomy, 164, 165–6, 168, 169–70, 180 authority figure–autonomy contradiction, 180 and behavioural economics, 171, 175, 176–7 cash and non-cash gifts, 161–2 context and culture, 175–6 contrast with rewards and punishments, 176–7 ‘crowding in’, 176 crowding out of prior motives, 160–61, 162–3, 164, 165–6, 171, 176 impact of economists’ ideas, 156–7, 178–80 and intrinsic motivations, 158–60, 161–3, 164, 165–6, 176 and moral disengagement, 162, 163, 164, 166 morally wrong/corrupting, 168–9 origins in behaviourism, 154 and orthodox theory of motivation, 157–8, 164, 166–7, 168–70, 178–9 payments to blood donors, 162–3, 164, 169, 176 as pervasive in modern era, 155–6 respectful use of, 175, 177–8 successful, 159–60 as tools of control/power, 155–7, 158–60, 161, 164, 167, 178 Indecent Proposal (film, 1993), 168 India, 123, 175 individualism, 82, 117 and Becker, 134, 135–8 see also freedom, individual Industrial Revolution, 223 inequality and access to lifeboats, 150–51 and climate change, 207–9 correlation with low social mobility, 227–8, 243 and demand for positional goods, 239–41 and economic imperialism, 145–7, 148, 151, 207 and efficiency wages, 237–8 entrenched self-deluding justifications for, 242–3 and executive pay, 215–16, 219, 224, 228–30, 234, 238 as falling in 1940–80 period, 215, 216 Great Gatsby Curve, 227–8, 243 hero myths, 221–3, 224 increases in as self-perpetuating, 227–8, 230–31, 243 as increasing since 1970s, 2–3, 215–16, 220–21 and lower growth levels, 239 mainstream political consensus on, 216, 217, 218, 219–21 marginal productivity theory, 223–4, 228 new doctrine on taxation since 1970s, 232–5 and Pareto, 217, 218–19, 220 poverty as waste of productive capacity, 238–9 public attitudes to, 221, 226–8 rises in as not inevitable, 220, 221, 242 role of luck downplayed, 222, 224–6, 243 scale-invariant nature of, 219, 220 ‘socialism for the rich’, 230 Thatcher’s praise of, 216 and top-rate tax cuts, 231, 233–5, 239 trickle-down economics, 232–3 US and European attitudes to, 226–7 ‘you deserve what you get’ belief, 223–6, 227–8, 236, 243 innovation, 222–3, 242 Inside Job (documentary, 2010), 88 Institute of Economic Affairs, 7–8, 15, 162–3 intellectual property law, 57, 68, 236 Ishiguro, Kazuo, Never Let Me Go, 148 Jensen, Michael, 229 Journal of Law and Economics, 49 justice, 1, 55, 57–62, 125, 137 Kahn, Herman, 18, 33 Kahneman, Daniel, 170–72, 173, 179, 202–3, 212, 226 Kennedy, President John, 139–40 Keynes, John Maynard, 11, 21, 162, 186, 204 and Buchanan’s ideology, 87 dentistry comparison, 258–9, 261 on economics as moral science, 252–3 Friedman’s challenge to orthodoxy of, 132 Hayek’s view of, 5–6 massive influence of, 3–4, 5–6 on power of economic ideas, 15 and probability, 185, 186–7, 188–9, 190, 210 vision of the ideal economist, 20 General Theory (1936), 15, 188–9 Khomeini, Ayatollah, 128 Khrushchev, Nikita, 139–40, 181 Kilburn Grammar School, 48 Kildall, Gary, 222 Kissinger, Henry, 184 Knight, Frank, 185–6, 212 Krugman, Paul, 248 Kubrick, Stanley, 35*, 139 labour child labour, 124, 146 and efficiency wages, 237–8 labour-intensive services, 90, 92–3 lumpenproletariat, 237 Olson’s hostility to unions, 104 Adam Smith’s ‘division of labour’ concept, 128 Laffer, Arthur, 232–3, 234 Lancet (medical journal), 257 Larkin, Philip, 67 law and economics movement, 40, 55, 56–63, 64–7 Lazear, Edward, ‘Economic Imperialism’, 246 legal system, 7* and blame for accidents, 55, 60–61 and Chicago School, 49, 50–52, 55 and Coase Theorem, 47, 49, 50–55, 63–6 criminal responsibility, 111, 137, 152 economic imperialist view of, 137 law and economics movement, 40, 55, 56–63, 64–7 ‘mimic the market’ approach, 61–3, 65 Posner’s wealth-maximization principle, 57–63, 64–7, 137 precautionary principle, 211–12, 214 transaction costs, 51–3, 54–5, 61, 62, 63–4, 68 Lehmann Brothers, 194 Lexecon, 58, 68 Linda Problem, 202–3 LineStanding.com, 123 Little Zheng, 123, 124 Lloyd Webber, Andrew, 234–5, 236 lobbying, 7, 8, 88, 115, 123, 125, 146, 230, 231, 238 loft-insulation schemes, 172–3 logic, mathematical, 74–5 The Logic of Life (Tim Harford, 2008), 130 London School of Economics (LSE), 4, 48 Long-Term Capital Management (LTCM), 201, 257 Machiavelli, Niccoló, 89, 94 Mafia, 30 malaria treatments, 125, 149 management science, 153–4, 155 Mandelbrot, Benoît, 195, 196, 201 Mankiw, Greg, 11 marginal productivity theory, 223–4 Markowitz, Harry, 196–7, 201, 213 Marx, Karl, 11, 101, 102, 104, 111, 223 lumpenproletariat, 237 mathematics, 9–10, 17–18, 19, 21–4, 26, 247, 248, 255, 259 of 2007 financial crash, 194, 195–6 and Ken Arrow, 71, 72, 73–5, 76–7, 82–3, 97 axioms (abstract assumptions), 198 fractals (scale-invariance), 194, 195–6, 201, 219 and orthodox decision theory, 190–91, 214 Ramsey Rule on discounting, 208–9, 212 and Savage, 189–90, 193, 197, 198, 199, 205 and Schelling, 139 Sen’s framework on voting systems, 80–81 standard deviation, 182, 192, 194 and stock market statistics, 190–91, 195–6 use of for military ends, 71–2 maximizing behaviour and Becker, 129–31, 133–4, 147 and catastrophe, 211 and Coase, 47, 55, 59, 61, 63–9 economic imperialism, 124–5, 129–31, 133–4, 147, 148–9 Posner’s wealth-maximization principle, 57–63, 64–7, 137 profit-maximizing firms, 228 see also wealth-maximization principle; welfare maximization McCluskey, Kirsty, 194 McNamara, Robert, 138 median voter theorem, 77, 95–6 Merton, Robert, 201 Meucci, Antonio, 222 microeconomics, 9, 232, 259 Microsoft, 222 Miles, David, 258 Mill, John Stuart, 102, 111, 243 minimum wage, national, 96 mobility, economic and social correlation with inequality, 226–8, 243 as low in UK, 227 as low in USA, 226–7 US–Europe comparisons, 226–7 Modern Times (Chaplin film, 1936), 154 modernism, 67 Moivre, Abraham de, 193 monetarism, 87, 89, 132, 232 monopolies and cartels, 101, 102, 103–4 public sector, 48–9, 50–51, 93–4 Mont Pèlerin Society, 3–9, 13, 15, 132 Morgenstern, Oskar, 20–22, 24–5, 28, 35, 124, 129, 189, 190 Mozart, Wolfgang Amadeus, 91, 92–3 Murphy, Kevin, 229 Mussolini, Benito, 216, 219 Nash equilibrium, 22–3, 24, 25, 27–8, 33–4, 41–2 Nash, John, 17–18, 22–3, 24, 25–6, 27–8, 33–4, 41–2 awarded Nobel Prize, 34–5, 38, 39, 40 mental health problems, 25, 26, 34 National Health Service, 106, 162 ‘neoliberalism’, avoidance of term, 3* Neumann, John von ambition to make economics a science, 20–21, 24–5, 26, 35, 125, 151, 189 as Cold War warrior, 20, 26, 138 and expansion of scope of economics, 124–5 as father of game theory, 18, 19, 20–22, 25, 26, 28, 30, 34, 41 final illness and death of, 19, 34, 35, 43–4 genius of, 19–20 as inspiration for Dr Strangelove, 19 and Nash’s equilibrium, 22–3, 25, 38* simplistic view of humanity, 28 theory of decision-making, 189, 190, 203 neuroscience, 14 New Deal, US, 4, 194, 231 Newton, Isaac, 223 Newtonian mechanics, 21, 24–5 Nixon, Richard, 56, 184, 200 NORAD, Colorado Springs, 181 nuclear weapons, 18–19, 20, 22, 27, 181 and Ellsberg, 200 and game theory, 18, 20, 21–2, 24, 27, 33–4, 35, 70, 73, 198 MAD (Mutually Assured Destruction), 35, 138 and Russell’s Chicken, 33–4 and Schelling, 138, 139 Nudge economists, 13, 171–5, 177–8, 179, 180, 251 Oaten, Mark, 121 Obama, Barack, 110, 121, 157, 172, 180 Olson, Mancur, 103, 108, 109, 119–20, 122 The Logic of Collective Action (1965), 103–4 On the Waterfront (Kazan film, 1954), 165 online invisibility, 100* organs, human, trade in, 65, 123, 124, 145, 147–8 Orwell, George, Nineteen Eighty-Four, 42–3 Osborne, George, 233–4 Packard, David, 159 Paine, Tom, 243 Pareto, Vilfredo 80/20 rule’ 218 and inequality, 217, 218–19, 220 life and background of, 216–17 Pareto efficiency, 217–18, 256* Paul the octopus (World Cup predictor, 2010), 133 pensions, workplace, 172, 174 physics envy, 9, 20–21, 41, 116, 175–6, 212, 247 Piketty, Thomas, 234, 235 plastic shopping bag tax, 159–60 Plato’s Republic, 100–101, 122 political scientists and Duncan Black, 78, 95–6 Black’s median voter theorem, 95–6 Buchanan’s ideology, 84–5 crises of the 1970s, 85–6 influence of Arrow, 72, 81–2, 83 see also public choice theory; social choice theory Posner, Richard, 54, 56–63, 137 ‘mimic the market’ approach, 61–3, 65 ‘The Economics of the Baby Shortage’ (1978), 61 precautionary principle, 211–12, 214 price-fixing, 101, 102, 103–4 Princeton University, 17, 19–20 Prisoner’s Dilemma, 26–8, 29–32, 42–3 prisons, cell upgrades in, 123 privatization, 50, 54, 88, 93–4 probability, 182–4 and Keynes, 185, 186–7, 188–9, 210 Linda Problem, 202–3 modern ideas of, 184–5 Ramsey’s personal probabilities (beliefs as probabilities), 187–8, 190, 197, 198, 199, 204–5 and Savage, 190, 193, 197, 198, 199, 203, 205 ‘Truth and Probability’ (Ramsey paper), 186–8, 189, 190 see also risk and uncertainty Proceedings of the National Academy of Sciences, 22 productivity Baumol’s cost disease, 90–92, 93, 94 and efficiency wages, 237–8 improvement in labour-intensive services, 92–3 labour input, 92 protectionism, 246, 255 psychology availability heuristic, 226 behaviourism, 154–8, 237 and behavioural economics, 12, 170–71 cognitive dissonance, 113–14 and financial incentives, 156–7, 158–60, 163–4, 171 framing effects, 170–71, 259 of free-riding, 113–14, 115 intrinsic motivations, 158–60, 161–3, 164, 165–6, 176 irrational behaviour, 12, 15, 171 learning of social behaviour, 163–4 moral disengagement, 162, 163, 164, 166 motivated beliefs, 227 ‘self-command’ strategies, 140 view of in game theory, 26–31 view of in public choice theory, 85–6 and welfare maximization, 149 ‘you deserve what you get’ belief, 223–6, 227–8, 236, 243 public choice theory as consensus view, 84–5 and crises of the 1970s, 85–6 foolish voter assumption, 86–8 ‘paradox of voter turnout’, 88–9, 95–6, 115–16 partial/self-contradictory application of, 86, 87–9 ‘political overload’ argument, 85, 86–7 ‘public bad, private good’ mantra, 93–4, 97 and resistance to tax rises, 94, 241 self-fulfilling prophecies, 95–7 and selfishness, 85–6, 87–8, 89, 94, 95–7 as time-bomb waiting to explode, 85 public expenditure in 1970s and ’80s, 89 Baumol’s cost disease, 90–92, 93, 94 and Keynesian economics, 4 and public choice theory, 85–8, 89, 241 and tax rises, 241–2 public-sector monopolies, 48–9, 50–51, 93–4 Puzzle of the Harmless Torturers, 118–19 queue-jumping, 123, 124 QWERTY layout, 42 racial discrimination, 126–7, 133, 136, 140 Ramsey, Frank, 186–8, 189, 190, 205, 208 Ramsey Rule, 208–9, 212 RAND Corporation, 17, 41, 103, 138, 139 and Ken Arrow, 70–71, 72–3, 74, 75–6, 77, 78 and behaviourism, 154 and Cold War military strategy, 18, 20, 21–2, 24, 27, 33–4, 70, 73, 75–6, 141, 200, 213 and Ellsberg, 182–4, 187, 197–8, 200 and Russell’s Chicken, 33 Santa Monica offices of, 18 self-image as defender of freedom, 78 rational behaviour assumptions in game theory, 18, 28, 29–32, 35–8, 41–3, 70, 124 axioms (abstract mathematical assumptions), 198 Becker’s version of, 128–9, 135, 140, 151 behavioural economics/Nudge view of, 173, 174–5 distinction between values and tastes, 136–8 economic imperialist view of, 135, 136–8, 140, 151 and free-riding theory, 100–101, 102, 103–4, 107–8, 109–10, 115–16 and orthodox decision theory, 198, 199 public choice theory relates selfishness to, 86 term as scientific-sounding cover, 12 see also homo economicus Reader’s Digest, 5, 6 Reagan, Ronald, 2, 87–8, 89, 104, 132 election of as turning point, 6, 216, 220–21 and top-rate tax cuts, 231, 233 regulators, 1–2 Chicago view of, 40 Reinhart, Carmen, 258 religion, decline of in modern societies, 15, 185 renewable energy, 116 rent-seeking, 230, 238 ‘right to recline’, 63–4 risk and uncertainty bell curve distribution, 191–4, 195, 196–7, 201, 203–4, 257 catastrophes, 181–2, 191, 192, 201, 203–4, 211–12 delusions of quantitative ‘risk management’, 196, 213 Ellsberg’s experiment (1961), 182–4, 187, 197, 198–200 errors in conventional thinking about, 191–2, 193–4, 195–7, 204–5, 213 financial orthodoxy on risk, 196–7, 201–2 and First World War, 185 and fractals (scale-invariance), 194, 195–6, 201 hasard and fortuit, 185* ‘making sense’ of through stories, 202–3 ‘measurable’ and ‘unmeasurable’ distinction, 185–6, 187–9, 190, 210–11, 212–13 measurement in numerical terms, 181–4, 187, 189, 190–94, 196–7, 201–2, 203–5, 212–13 orthodox decision theory, 183–4, 185–6, 189–91, 193–4, 201–2, 203–5, 211, 212–14 our contemporary orthodoxy, 189–91 personal probabilities (beliefs as probabilities), 187–8, 190, 197, 198, 199, 204–5 precautionary principle, 211–12, 214 pure uncertainty, 182–3, 185–6, 187–9, 190, 197, 198–9, 210, 211, 212, 214, 251 redefined as ‘volatility’, 197, 213 the Savage orthodoxy, 190–91, 197, 198–200, 203, 205 scenario planning as crucial, 251 Taleb’s black swans, 192, 194, 201, 203–4 ‘Truth and Probability’ (Ramsey paper), 186–8, 189, 190 urge to actuarial alchemy, 190–91, 197, 201 value of human life (‘statistical lives’), 141–5, 207 see also probability Robertson, Dennis, 13–14 Robinson, Joan, 260 Rodrik, Dani, 255, 260–61 Rogoff, Ken, 258 Rothko, Mark, 4–5 Rumsfeld, Donald, 232–3 Russell, Bertrand, 33–4, 74, 97, 186, 188 Ryanair, 106 Sachs, Jeffrey, 257 Santa Monica, California, 18 Sargent, Tom, 257–8 Savage, Leonard ‘Jimmie’, 189–90, 193, 203, 205scale-invariance, 194, 195–6, 201, 219 Scandinavian countries, 103, 149 Schelling, Thomas, 35* on access to lifeboats, 150–51 awarded Nobel Prize, 138–9 and Cold War nuclear strategy, 138, 139–40 and economic imperialism, 141–5 and game theory, 138–9 and Washington–Moscow hotline, 139–40 work on value of human life, 141–5, 207 ‘The Intimate Contest for Self-command’ (essay, 1980), 140, 145 ‘The Life You Save May be Your Own’ (essay, 1968), 142–5, 207 Schiphol Airport, Amsterdam, 172 Schmidt, Eric, 105 Scholes, Myron, 201 Schwarzman, Stephen, 235 Second World War, 3, 189, 210 selfishness, 41–3, 178–9 and Becker, 129–30 and defence of inequality, 242–3 as free marketeers’ starting point, 10–12, 13–14, 41, 86, 178–9 and game theory, 18 and public choice theory, 85–6, 87–8, 89, 94, 95–7 Selten, Reinhard, 34–5, 36, 38, 40 Sen, Amartya, 29, 80–81 service sector, 90–93, 94 Shakespeare, William, Measure for Measure, 169 Shaw, George Bernard, 101 Shiller, Robert, 247 Simon, Herbert, 223 Skinner, Burrhus, 154–5, 158 Smith, Adam, 101, 111, 122 The Wealth of Nations (1776), 10–11, 188–9 snowflakes, 195 social choice theory, 72 and Ken Arrow, 71–83, 89, 95, 97, 124–5, 129 and Duncan Black, 78, 95 and free marketeers, 79, 82 Sen’s mathematical framework, 80–81 social media, 100* solar panels, 116 Solow, Bob, 163, 223 Sorites paradox, 117–18, 119 sovereign fantasy, 116–17 Soviet Union, 20, 22, 70, 73, 82, 101, 104, 167, 237 spectrum auctions, 39–40, 47, 49 Stalin, Joseph, 70, 73, 101 the state anti-government attitudes in USA, 83–5 antitrust regulation, 56–8 dismissal of almost any role for, 94, 135, 235–6, 241 duty over full employment, 5 economic imperialist arguments for ‘small government’, 135 increased economic role from 1940s, 3–4, 5 interventions over ‘inefficient’ outcomes, 53 and monetarism, 87, 89 and Mont Pèlerin Society, 3, 4, 5 and privatization, 50, 54, 88, 93–4 public-sector monopolies, 48–50, 93–4 replacing of with markets, 79 vital role of, 236 statistical lives, 141–5, 207 Stern, Nick, 206, 209–10 Stigler, George, 50, 51, 56, 69, 88 De Gustibus Non Est Disputandum (with Becker, 1977), 135–6 Stiglitz, Joseph, 237 stock markets ‘Black Monday’ (1987), 192 and fractals (scale-invariance), 194, 195–6, 201 orthodox decision theory, 190–91, 193–4, 201 Strittmatter, Father, 43–4 Summers, Larry, 10, 14 Sunstein, Cass, 173 Nudge (with Richard Thaler, 2008), 171–2, 175 Taleb, Nassim, 192 Tarski, Alfred, 74–5 taxation and Baumol’s cost disease, 94 and demand for positional goods, 239–41 as good thing, 231, 241–2, 243 Laffer curve, 232–3, 234 new doctrine of since 1970s, 232–4 property rights as interdependent with, 235–6 public resistance to tax rises, 94, 239, 241–2 and public spending, 241–2 revenue-maximizing top tax rate, 233–4, 235 tax avoidance and evasion, 99, 105–6, 112–13, 175, 215 ‘tax revolt’ campaigns (1970s USA), 87 ‘tax as theft’ culture, 235–6 top-rate cuts and inequality, 231, 233–5, 239 whines from the super-rich, 234–5, 243 Taylor, Frederick Winslow, 153–4, 155, 167, 178, 237 Thaler, Richard, 13 Nudge (with Cass Sunstein, 2008), 171–2, 175 Thatcher, Margaret, 2, 88, 89, 104, 132 election of as turning point, 6, 216, 220–21 and Hayek, 6, 7 and inequality, 216, 227 privatization programme, 93–4 and top-rate tax cuts, 231 Theory of Games and Economic Behavior (Von Neumann and Morgenstern, 1944), 20, 21, 25, 189 Titanic, sinking of (1912), 150 Titmuss, Richard, The Gift Relationship, 162–3 tobacco-industry lobbyists, 8 totalitarian regimes, 4, 82, 167–8, 216, 219 see also Soviet Union trade union movement, 104 Tragedy of the Commons, 27 Truman, Harry, 20, 237 Trump, Donald, 233 Tucker, Albert, 26–7 Tversky, Amos, 170–72, 173, 202–3, 212, 226 Twitter, 100* Uber, 257 uncertainty see risk and uncertainty The Undercover Economist (Tim Harford, 2005), 130 unemployment and Coase Theorem, 45–7, 64 during Great Depression, 3–4 and Keynesian economics, 4, 5 United Nations, 96 universities auctioning of places, 124, 149–50 incentivization as pervasive, 156 Vietnam War, 56, 198, 200, 249 Villari, Pasquale, 30 Vinci, Leonardo da, 186 Viniar, David, 182, 192 Volkswagen scandal (2016), 2, 151–2 Vonnegut, Kurt, 243–4 voting systems, 72–4, 77, 80, 97 Arrow’s ‘Independence of Irrelevant Alternatives’, 81, 82 Arrow’s ‘Universal Domain’, 81, 82 and free marketeers, 79 ‘hanging chads’ in Florida (2000), 121 recount process in UK, 121 Sen’s mathematical framework, 80–81 Waldfogel, Joel, 161* Wanniski, Jude, 232 Watertown Arsenal, Massachusetts, 153–4 Watson Jr, Thomas J., 181 wealth-maximization principle, 57–63 and Coase, 47, 55, 59, 63–9 as core principle of current economics, 253 created markets, 65–7 extension of scope of, 124–5 and justice, 55, 57–62, 137 and knee space on planes, 63–4 practical problems with negotiations, 62–3 and values more important than efficiency, 64–5, 66–7 welfare maximization, 124–5, 129–31, 133–4, 148–9, 176 behavioural economics/Nudge view of, 173 and vulnerable/powerless people, 146–7, 150 welfare state, 4, 162 Wilson, Charlie, 215 Wittgenstein, Ludwig, 186, 188 Wolfenschiessen (Swiss village), 158, 166–7 Woolf, Virginia, 67 World Bank, 96 World Cup football tournament (2010), 133 World Health Organization, 207 Yale Saturday Evening Pest, 4–5 Yellen, Janet, 237 THE BEGINNING Let the conversation begin … Follow the Penguin twitter.com/penguinukbooks Keep up-to-date with all our stories youtube.com/penguinbooks Pin ‘Penguin Books’ to your pinterest.com/penguinukbooks Like ‘Penguin Books’ on facebook.com/penguinbooks Listen to Penguin at soundcloud.com/penguin-books Find out more about the author and discover more stories like this at penguin.co.uk ALLEN LANE UK | USA | Canada | Ireland | Australia India | New Zealand | South Africa Allen Lane is part of the Penguin Random House group of companies whose addresses can be found at global.penguinrandomhouse.com First published 2019 Copyright © Jonathan Aldred, 2019 The moral right of the author has been asserted Jacket photograph © Getty Images ISBN: 978-0-241-32544-5 This ebook is copyright material and must not be copied, reproduced, transferred, distributed, leased, licensed or publicly performed or used in any way except as specifically permitted in writing by the publishers, as allowed under the terms and conditions under which it was purchased or as strictly permitted by applicable copyright law.

Reset
by Ronald J. Deibert
Published 14 Aug 2020

According to a major study by marketing psychologists Ezgi Akpinar and Jonah Berger, who combined controlled laboratory experiments with systematic analysis of hundreds of online ads, it is clear that “emotional appeals (which use drama, mood, music, and other emotion-eliciting strategies) are more likely to be shared” than “informative appeals (which focus on product features).”155 As attention merchants have known for decades, emotional appeals sell better than rational ones. Social media’s flood of content also amplifies other cognitive biases in ways that uniquely allow false information to root itself in the public consciousness.156 Consider “the availability heuristic” and “the illusory truth effect” — terms describing cognitive traits that explain how collective beliefs are reinforced regardless of their veracity or merit, as a result of their repetition.157 Social psychology experiments have demonstrated that repeated exposure to information increases the likelihood that observers will believe that information, even if it is false.

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OR Books; Matz et al. Psychological targeting. Social media’s flood of content also amplifies other cognitive biases: Beasley, B. (2019, December 26). How disinformation hacks your brain. Retrieved from https://blogs.scientificamerican.com/observations/how-disinformation-hacks-your-brain/ “The availability heuristic” and “the illusory truth effect”: Kuran, T. (2007). Availability cascades and risk regulation. University of Chicago Public Law & Legal Theory Working Paper No. 181, 683–768. Retrieved from https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1036&context=public_law_and_legal_theory; Pennycook, G., Cannon, T.

Inside the Nudge Unit: How Small Changes Can Make a Big Difference
by David Halpern
Published 26 Aug 2015

Rather, most people use a mental shortcut along the lines of how easily they can recall examples of planes versus cars crashing – what Tversky and Kahneman called an ‘availability’ heuristic. The more easily the person can call to mind an example, the more likely or common they infer it to be. It’s generally not a bad heuristic. It gives you a pretty good idea of how many tigers versus pigeons you might meet walking around the streets of London or New York. But when it comes to aeroplane versus car safety, using the availability heuristic can lead us badly astray. Rare but devastating air crashes make the news and stick in our minds, but the daily death toll on our roads passes largely without comment or lasting attention.

Can It Happen Here?: Authoritarianism in America
by Cass R. Sunstein
Published 6 Mar 2018

The concept of availability chambers harkens to availability cascade, which is a self-reinforcing process of collective belief formation. Under an availability cascade, an expressed perception triggers a chain reaction that gives that perception increasing plausibility through its rising availability in public discourse.22 The process is intermediated by the availability heuristic, a pervasive mental shortcut through which the perceived likelihood of any given event depends on the ease with which its occurrences can be brought to mind. Cognitive psychologists find that individual beliefs regarding truth and falsehood are generally based on the ease with which pertinent examples come to mind.

…

See Obamacare Agenda-setting, 89 Albright, Jonathan, 88 Alien Act of 1798, 430–32 Alien Registration Act, 442 Alliance for Securing Democracy, 95–96 American Civil Liberties Union (ACLU), 315, 319 American Civil War, 139, 432–33 American exceptionalism, 57–58, 142, 170–71 Amidala, Padmé, 392 Analogies, 303 Analogies at War (Khong), 303 Anarchy, 42–43 Anceau, E., 287, 296, 298, 300, 301, 303, 306–7 Andic, Suphan, 45–46 Andrew, Christopher, 84 Andropov, Yuri, 82, 85 “Another Road to Serfdom: Cascading Intolerance” (Kuran), 233–75 Antifederalists, 62–64, 66, 75–76 Arab Spring, 140, 388 Articles of Confederation, 59–61, 64 Associations, 236, 237, 263 Attack on Pearl Harbor, 318, 438, 439 Atwood, Margaret, 138–39 Austria, 177 Authoritarian dynamic, 179–81, 185–86 EuroPulse survey and analysis, 188–209 Authoritarianism civil liberties vs., 429 conservatism compared with, 181–84 impact on populism, 201–3 steps to, 365–66 what it does, 184–85 when it does this, 185–88 “Authoritarianism Is Not a Momentary Madness, but an Eternal Dynamic within Liberal Democracies” (Stenner and Haidt), 175–219 Authoritarian Personality, The (Adorno), 183 Authoritarian predisposition, 179, 180, 187, 191–94, 196, 197, 203, 215–16, 219n Authoritarian rule, and Trump, 2–16 Authorization for Use of Military Force (AUMF), 224 Authorization of Regulatory Force and Adjustment (ARFA), 136–37, 144 Autocracy, 24, 140, 154, 267 psychology of, 279–84 Availability cascade, 251 Availability chambers, 249–58, 261–62 Availability heuristic, 251–52 Background circumstances, 344, 348 Balkin, Jack M., 452 “Constitutional Rot,” 19–35 Bannon, Steve, 41 “Basket of deplorables,” 242 Bastid, P., 296, 297, 300 Beattie, James, 335–36, 338 Beaumont, Gustave de, 291–92 Beck, Glenn, 243 Behavioral economics, 341–43 Berlusconi, Silvio, 147, 277 Berton, H., 296 “Beyond Elections: Foreign Interference with American Democracy” (Power), 81–103 Biases, 341–43, 348 Bicameralism, 70, 71, 72 Biddle, Nicholas, 221, 440 Big Five personality traits, 183 Big government, 38, 47, 50–53, 268 Bill of Rights, 79 Black, Hugo, 442 Black Lives Matter, 93, 190, 258 Black sites, 115, 124 Blanc, Louis, 286 Born rulers, 280–82 Brandeis, Louis, 437, 445 Brandenburg v.

The Precipice: Existential Risk and the Future of Humanity
by Toby Ord
Published 24 Mar 2020

Behavioral psychology has identified two more reasons why we neglect existential risk, rooted in the heuristics and biases we use as shortcuts for making decisions in a complex world.62 The first of these is the availability heuristic. This is a tendency for people to estimate the likelihood of events based on their ability to recall examples. This stirs strong feelings about avoiding repeats of recent tragedies (especially those that are vivid or widely reported). But it means we often underweight events which are rare enough that they haven’t occurred in our lifetimes, or which are without precedent. Even when experts estimate a significant probability for an unprecedented event, we have great difficulty believing it until we see it. For many risks, the availability heuristic is a decent guide, allowing us to build up methods for managing the risk through trial and error.

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

Is K more likely to appear as the first letter in a word or as the third letter?’ 15 According to Kahneman, most people wrongly respond that K appears more often as the first letter. It is easier to think of words that begin with a particular letter than to acknowledge words that have that letter in the third position. Kahneman and Tversky christened this the ‘availability heuristic’. It uses the simplest memory search to come up with an answer. But the experiment did not provide any serious motivation for answering the question, or even for defining properly the question being asked. Most people might reasonably answer ‘I don’t know, but if it is important I will try to find out’.

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INDEX 10 (film, 1979), 97 737 Max aircraft, 228 9/11 terror attacks, 7 , 74–6 , 202 , 230 Abbottabad raid (2011), 9–10 , 20 , 26 , 44 , 71 , 102 , 118–19 , 120 , 174–5 ; reference narrative of, 122–3 , 277 , 298 ; role of luck in, 262–3 ; and unhelpful probabilities, 8–19 , 326 abductive reasoning, 138 , 147 , 211 , 388 , 398 ABN AMRO, 257 Abraham (biblical character), 206 Abrahams, Harold, 273 Abramovich, Roman, 265 accountancy, 409 aeronautics, 227–8 , 352–6 , 383 Agdestein, Simen, 273 AIDS, 57 , 230 , 375–6 Airbus A380, 40 , 274–6 , 408 Akerlof, George, 250–1 , 252 , 253 , 254 , 382 Alchian, Armen, 158 alien invasion narratives, 295–6 Allais, Maurice, 134–5 , 136 , 137 , 437 , 440–3 Allen, Bill, 227–8 Allen, Paul, 28 , 29 Altair desktop, 28 Amazon, 289 , 309 Anderson, Roy, 375 ant colonies, 173 anthropology, 160 , 189–91 , 193–4 , 215–16 antibiotics, 40 , 45 , 284 , 429 Antz (film, 1998), 274 apocalyptic narratives, 331–2 , 335 , 358–62 Appiah, Anthony, 117–18 Apple, 29–30 , 31 , 169 , 309 Applegarth, Adam, 311 arbitrage, 308 Archilochus (Greek poet), 222 Aristotle, 137 , 147 , 303 Arrow, Kenneth, 254 , 343–5 , 440 artificial intelligence (AI), xvi , 39 , 135 , 150 , 173–4 , 175–6 , 185–6 , 387 ; the ‘singularity’, 176–7 Ashtabula rail bridge disaster (1876), 33 Asimov, Isaac, 303 asteroid strikes, 32 , 71–2 , 238 , 402 astrology, 394 astronomical laws, 18–19 , 35 , 70 , 373–4 , 388 , 389 , 391–2 , 394 AT&T, 28 auction theory, 255–7 Austen, Jane, 217 , 224–5 , 383 autism, 394 , 411 aviation, commercial, 23–4 , 40 , 227–8 , 274–6 , 315 , 383 , 414 axiomatic rationality: Allais disputes theory, 134–5 , 136 , 137 ; Arrow– Debreu world, 343–5 ; assumption of transitivity, 437 ; and Becker, 114 , 381–2 ; and behavioural economics, 116 , 135–6 , 141–9 , 154–5 , 167–8 , 386–7 , 401 ; capital asset pricing model (CAPM), 307–8 , 309 , 320 , 332 ; completeness axiom, 437–8 ; consistency of choice axiom, 108–9 , 110–11 ; continuity axiom, 438–40 ; definition of rationality, 133–4 , 137 , 436 ; definition of risk, 305 , 307 , 334 , 420–1 ; efficient market hypothesis, 252 , 254 , 308–9 , 318 , 320 , 332 , 336–7 ; efficient portfolio model, 307–8 , 309 , 318 , 320 , 332–4 , 366 ; and evolutionary rationality, 16 , 152–3 , 154–5 , 157 , 158 , 166–7 , 171–2 , 386–7 , 407 ; and ‘expectations’ concept, 97–8 , 102–3 , 121–2 , 341–2 ; extended to decision-making under uncertainty, xv , 40–2 , 110–14 , 133–7 , 257–9 , 420–1 ; and Friedman, 73–4 , 111–12 , 113–14 , 125 , 257–9 , 307 , 399–400 , 420 , 437 ; hegemony of over radical uncertainty, 40–2 , 110–14 ; implausibility of assumptions, xiv–xv , 16 , 41–4 , 47 , 74–84 , 85–105 , 107–9 , 111 , 116–22 , 344–9 , 435–44 ; independence axiom, 440–4 ; as limited to small worlds, 170 , 309–10 , 320–1 , 342–9 , 382 , 400 , 421 ; and Lucas, 36 , 92 , 93 , 338–9 , 341 , 345 , 346 ; and Markowitz, 307 , 308 , 309–10 , 318 , 322 , 333 ; maximising behaviour, 310 ; ‘pignistic probability’, 78–84 , 438 ; and Popperian falsificationism, 259–60 ; Prescott’s comparison with engineering, 352–6 ; ‘rational expectations theory, 342–5 , 346–50 ; and Samuelson, xv , 42 , 110–11 , 436 ; and Savage, 111–14 , 125 , 257–9 , 309 , 345 , 400 , 435 , 437 , 442–3 ; shocks and shifts discourse, 42 , 346 , 347 , 348 , 406–7 ; Simon’s work on, 134 , 136 , 149–53 ; triumph of probabilistic reasoning, 15–16 , 20 , 72–84 , 110–14 ; Value at risk models (VaR), 366–8 , 405 , 424 ; von Neumann–Morgenstern axioms, 111 , 133 , 435–44 ; see also maximising behaviour Ballmer, Steve, 30 , 227 Bank of England, xiii , 45 , 103–5 , 286 , 311 Barclays Bank, 257 Barings Bank, 411 Basel regulations, 310 , 311 Bay of Pigs fiasco (1961), 278–9 Bayes, Reverend Thomas, 60–3 , 66–7 , 70 , 71 , 358 , 431 Beane, Billy, 273 Bear Stearns, 158–9 Becker, Gary, 114 , 381–2 Beckham, David, 267–8 , 269 , 270 , 272–3 , 414 behavioural economics, 116 , 145–8 , 154 , 386–7 ; and Allais paradox, 442 ; ‘availability heuristic’, 144–5 ; biases in human behaviour, 16 , 136 , 141–8 , 154 , 162 , 165 , 167–8 , 170–1 , 175–6 , 184 , 401 ; and evolutionary science, 154–5 , 165 ; Kahneman’s dual systems, 170–1 , 172 , 271 ; Kahneman–Tversky experiments, 141–7 , 152 , 215 ; ‘noise’ (randomness), 175–6 ; nudge theory, 148–9 Bentham, Jeremy, 110 Berkshire Hathaway, 153 , 319 , 324 , 325–6 Berlin, Isaiah, 222 Bernoulli, Daniel, 114–16 , 199 Bernoulli, Nicolaus, 199 , 442 Bertrand, Joseph, 70 Bezos, Jeff, 289 big data, 208 , 327 , 388–90 billiard players, 257–8 bin Laden, Osama, 7 , 8–10 , 21 , 44 , 71 , 118–19 , 120 , 122–3 , 262–3 , 326 Bismarck, Otto von, 161 Bitcoin, 96 , 316 Black Death, 32 , 39–40 BlackBerry, 30 , 31 blackjack, 38 Blackstone, Sir William, 213 BNP Paribas, 5 , 6 BOAC, 23–4 Boas, Franz, 193 Boeing, 24 , 227–8 Boer War, 168 Bolt, Usain, 273 bonobos, 161–2 , 178 Borges, Jorge Luis, 391 Borodino, battle of (1812), 3–4 , 433 Bortkiewicz, Ladislaus, 235–6 Bower, Tom, 169–70 Bowral cricket team, New South Wales, 264 Box, George, 393 Boycott, Geoffrey, 264–5 Bradman, Don, 237 , 264 Brahe, Tycho, 388–9 Brånemark, Per-Ingvar, 387 , 388 Branson, Richard, 169–70 Brearley, Michael, 140–1 , 264–5 Breslau (now Wrocław), 56 Brexit referendum (June 2016), 241–2 ; lies told during, 404 bridge collapses, 33 , 341 Brownian motion, 37 Brunelleschi, Filippo, 143 , 147 Buffett, Warren, 83 , 152 , 179 , 319–20 , 324 , 335 , 336–7 Burns, Robert, 253 Bush, George W., 295 , 407 , 412 business cycles, 347 business history (academic discipline), 286 business schools, 318 business strategy: approach in 1970s, 183 ; approach in 1980s, 181–2 ; aspirations confused with, 181–2 , 183–4 ; business plans, 223–4 , 228 ; collections of capabilities, 274–7 ; and the computer industry, 27–31 ; corporate takeovers, 256–7 ; Lampert at Sears, 287–9 , 292 ; Henry Mintzberg on, 296 , 410 ; motivational proselytisation, 182–3 , 184 ; quantification mistaken for understanding, 180–1 , 183 ; and reference narratives, 286–90 , 296–7 ; risk maps, 297 ; Rumelt’s MBA classes, 10 , 178–80 ; Shell’s scenario planning, 223 , 295 ; Sloan at General Motors, 286–7 ; strategy weekends, 180–3 , 194 , 296 , 407 ; three common errors, 183–4 ; vision or mission statements, 181–2 , 184 Buxton, Jedediah, 225 Calas, Jean, 199 California, 48–9 Cambridge Growth Project, 340 Canadian fishing industry, 368–9 , 370 , 423 , 424 cancer, screening for, 66–7 Candler, Graham, 352 , 353–6 , 399 Cardiff City Football Club, 265 Carlsen, Magnus, 175 , 273 Carnegie, Andrew, 427 Carnegie Mellon University, 135 Carré, Dr Matt, 267–8 Carroll, Lewis, Through the Looking-Glass , 93–4 , 218 , 344 , 346 ; ‘Jabberwocky’, 91–2 , 94 , 217 Carron works (near Falkirk), 253 Carter, Jimmy, 8 , 119 , 120 , 123 , 262–3 cartography, 391 Casio, 27 , 31 Castro, Fidel, 278–9 cave paintings, 216 central banks, 5 , 7 , 95 , 96 , 103–5 , 285–6 , 348–9 , 350 , 351 , 356–7 Central Pacific Railroad, 48 Centre for the Study of Existential Risk, 39 Chabris, Christopher, 140 Challenger disaster (1986), 373 , 374 Chamberlain, Neville, 24–5 Chandler, Alfred, Strategy and Structure , 286 Chariots of Fire (film, 1981), 273 Charles II, King, 383 Chelsea Football Club, 265 chess, 173 , 174 , 175 , 266 , 273 , 346 Chicago economists, 36 , 72–4 , 86 , 92 , 111–14 , 133–7 , 158 , 257–8 , 307 , 342–3 , 381–2 Chicago Mercantile Exchange, 423 chimpanzees, 161–2 , 178 , 274 China, 4–5 , 419–20 , 430 cholera, 283 Churchill, Winston: character of, 25–6 , 168 , 169 , 170 ; fondness for gambling, 81 , 168 ; as hedgehog not fox, 222 ; on Montgomery, 293 ; restores gold standard (1925), 25–6 , 269 ; The Second World War , 187 ; Second World War leadership, 24–5 , 26 , 119 , 167 , 168–9 , 170 , 184 , 187 , 266 , 269 Citibank, 255 Civil War, American, 188 , 266 , 290 Clapham, John, 253 Clark, Sally, 197–8 , 200 , 202 , 204 , 206 Clausewitz, Carl von, On War , 433 climate systems, 101–2 Club of Rome, 361 , 362 Coase, Ronald, 286 , 342 Cochran, Johnnie, 198 , 217 Cochrane, John, 93 coffee houses, 55–6 cognitive illusions, 141–2 Cohen, Jonathan, 206–7 Colbert, Jean-Baptiste, 411 Cold War, 293–4 , 306–7 Collier, Paul, 276–7 Columbia disaster (2003), 373 Columbia University, 117 , 118 , 120 Columbus, Christopher, 4 , 21 Colyvan, Mark, 225 Comet aircraft, 23–4 , 228 communication: communicative rationality, 172 , 267–77 , 279–82 , 412 , 414–16 ; and decision-making, 17 , 231 , 272–7 , 279–82 , 398–9 , 408 , 412 , 413–17 , 432 ; eusociality, 172–3 , 274 ; and good doctors, 185 , 398–9 ; human capacity for, 159 , 161 , 162 , 172–3 , 216 , 272–7 , 408 ; and ill-defined concepts, 98–9 ; and intelligibility, 98 ; language, 98 , 99–100 , 159 , 162 , 173 , 226 ; linguistic ambiguity, 98–100 ; and reasoning, 265–8 , 269–77 ; and the smartphone, 30 ; the ‘wisdom of crowds’, 47 , 413–14 Community Reinvestment Act (USA, 1977), 207 comparative advantage model, 249–50 , 251–2 , 253 computer technologies, 27–31 , 173–4 , 175–7 , 185–6 , 227 , 411 ; big data, 208 , 327 , 388–90 ; CAPTCHA text, 387 ; dotcom boom, 228 ; and economic models, 339–40 ; machine learning, 208 Condit, Phil, 228 Condorcet, Nicolas de, 199–200 consumer price index, 330 , 331 conviction narrative theory, 227–30 Corinthians (New Testament), 402 corporate takeovers, 256–7 corporations, large, 27–31 , 122 , 123 , 286–90 , 408–10 , 412 , 415 Cosmides, Leda, 165 Cretaceous–Paleogene extinction, 32 , 39 , 71–2 Crick, Francis, 156 cricket, 140–1 , 237 , 263–5 crime novels, classic, 218 crosswords, 218 crypto-currencies, 96 , 316 Csikszentmihalyi, Mihaly, 140 , 264 Cuba, 278–80 ; Cuban Missile Crisis, 279–81 , 299 , 412 Custer, George, 293 Cutty Sark (whisky producer), 325 Daily Express , 242–3 , 244 Damasio, Antonio, 171 Dardanelles expedition (1915), 25 Darwin, Charles, 156 , 157 Davenport, Thomas, 374 Dawkins, Richard, 156 de Havilland company, 23–4 Debreu, Gerard, 254 , 343–4 decision theory, xvi ; critiques of ‘American school’, 133–7 ; definition of rationality, 133–4 ; derived from deductive reasoning, 138 ; Ellsberg’s ‘ambiguity aversion’, 135 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128 – 30 , 135 , 400 , 435–44 ; hegemony of optimisation, 40–2 , 110–14 ; as unable to solve mysteries, 34 , 44 , 47 ; and work of Savage, 442–3 decision-making under uncertainty: and adaptation, 102 , 401 ; Allais paradox, 133–7 , 437 , 440–3 ; axiomatic approach extended to, xv , 40–2 , 110–14 , 133–7 , 257–9 , 420–1 ; ‘bounded rationality concept, 149–53 ; as collaborative process, 17 , 155 , 162 , 176 , 411–15 , 431–2 ; and communication, 17 , 231 , 272–7 , 279–82 , 398–9 , 408 , 412 , 413–17 , 432 ; communicative rationality, 172 , 267–77 , 279–82 , 412 , 414–16 ; completeness axiom, 437–8 ; continuity axiom, 438–40 ; Cuban Missile Crisis, 279–81 , 299 , 412 ; ‘decision weights’ concept, 121 ; disasters attributed to chance, 266–7 ; doctors, 184–6 , 194 , 398–9 ; and emotions, 227–9 , 411 ; ‘evidence-based policy’, 404 , 405 ; excessive attention to prior probabilities, 184–5 , 210 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128–30 , 135 , 400 , 435–44 ; first-rate decision-makers, 285 ; framing of problems, 261 , 362 , 398–400 ; good strategies for radical uncertainty, 423–5 ; and hindsight, 263 ; independence axiom, 440–4 ; judgement as unavoidable, 176 ; Klein’s ‘primed recognition decision-making’, 399 ; Gary Klein’s work on, 151–2 , 167 ; and luck, 263–6 ; practical decision-making, 22–6 , 46–7 , 48–9 , 81–2 , 151 , 171–2 , 176–7 , 255 , 332 , 383 , 395–6 , 398–9 ; and practical knowledge, 22–6 , 195 , 255 , 352 , 382–8 , 395–6 , 405 , 414–15 , 431 ; and prior opinions, 179–80 , 184–5 , 210 ; ‘prospect theory’, 121 ; public sector processes, 183 , 355 , 415 ; puzzle– mystery distinction, 20–4 , 32–4 , 48–9 , 64–8 , 100 , 155 , 173–7 , 218 , 249 , 398 , 400–1 ; qualities needed for success, 179–80 ; reasoning as not decision-making, 268–71 ; and ‘resulting’, 265–7 ; ‘risk as feelings’ perspective, 128–9 , 310 ; robustness and resilience, 123 , 294–8 , 332 , 335 , 374 , 423–5 ; and role of economists, 397–401 ; Rumelt’s ‘diagnosis’, 184–5 , 194–5 ; ‘satisficing’ (’good enough’ outcomes), 150 , 167 , 175 , 415 , 416 ; search for a workable solution, 151–2 , 167 ; by securities traders, 268–9 ; ‘shock’ and ‘shift’ labels, 42 , 346 , 347 , 348 , 406–7 ; simple heuristics, rules of thumb, 152 ; and statistical discrimination, 207–9 , 415 ; triumph of probabilistic reasoning, 20 , 40–2 , 72–84 , 110–14 ; von Neumann– Morgenstern axioms, 111 , 133 , 435–44 ; see also business strategy deductive reasoning, 137–8 , 147 , 235 , 388 , 389 , 398 Deep Blue, 175 DeepMind, 173–4 The Deer Hunter (film, 1978), 438 democracy, representative, 292 , 319 , 414 demographic issues, 253 , 358–61 , 362–3 ; EU migration models, 369–70 , 372 Denmark, 426 , 427 , 428 , 430 dentistry, 387–8 , 394 Derek, Bo, 97 dermatologists, 88–9 Digital Equipment Corporation (DEC), 27 , 31 dinosaurs, extinction of, 32 , 39 , 71–2 , 383 , 402 division of labour, 161 , 162 , 172–3 , 216 , 249 DNA, 156 , 198 , 201 , 204 ‘domino theory’, 281 Donoghue, Denis, 226 dotcom boom, 316 , 402 Doyle, Arthur Conan, 34 , 224–5 , 253 Drapers Company, 328 Drescher, Melvin, 248–9 Drucker, Peter, Concept of the Corporation (1946), 286 , 287 Duhem–Quine hypothesis, 259–60 Duke, Annie, 263 , 268 , 273 Dulles, John Foster, 293 Dutch tulip craze (1630s), 315 Dyson, Frank, 259 earthquakes, 237–8 , 239 Eco, Umberto, The Name of the Rose , 204 Econometrica , 134 econometrics, 134 , 340–1 , 346 , 356 economic models: of 1950s and 1960s, 339–40 ; Akerlof model, 250–1 , 252 , 253 , 254 ; ‘analogue economies’ of Lucas, 345 , 346 ; artificial/complex, xiv–xv , 21 , 92–3 , 94 ; ‘asymmetric information’ model, 250–1 , 254–5 ; capital asset pricing model (CAPM), 307–8 , 309 , 320 , 332 ; comparative advantage model, 249–50 , 251–2 , 253 ; cost-benefit analysis obsession, 404 ; diversification of risk, 304–5 , 307–9 , 317–18 , 334–7 ; econometric models, 340–1 , 346 , 356 ; economic rent model, 253–4 ; efficient market hypothesis, 252 , 254 , 308–9 , 318 , 320 , 332 , 336–7 ; efficient portfolio model, 307–8 , 309 , 318 , 320 , 332–4 , 366 ; failure over 2007–08 crisis, xv , 6–7 , 260 , 311–12 , 319 , 339 , 349–50 , 357 , 367–8 , 399 , 407 , 423–4 ; falsificationist argument, 259–60 ; forecasting models, 7 , 15–16 , 68 , 96 , 102–5 , 347–50 , 403–4 ; Goldman Sachs risk models, 6–7 , 9 , 68 , 202 , 246–7 ; ‘grand auction’ of Arrow and Debreu, 343–5 ; inadequacy of forecasting models, 347–50 , 353–4 , 403–4 ; invented numbers in, 312–13 , 320 , 363–4 , 365 , 371 , 373 , 404 , 405 , 423 ; Keynesian, 339–40 ; Lucas critique, 341 , 348 , 354 ; Malthus’ population growth model, 253 , 358–61 , 362–3 ; misuse/abuse of, 312–13 , 320 , 371–4 , 405 ; need for, 404–5 ; need for pluralism of, 276–7 ; pension models, 312–13 , 328–9 , 405 , 423 , 424 ; pre-crisis risk models, 6–7 , 9 , 68 , 202 , 246–7 , 260 , 311–12 , 319 , 320–1 , 339 ; purpose of, 346 ; quest for large-world model, 392 ; ‘rational expectations theory, 342–5 , 346–50 ; real business cycle theory, 348 , 352–4 ; role of incentives, 408–9 ; ‘shift’ label, 406–7 ; ‘shock’ label, 346–7 , 348 , 406–7 ; ‘training base’ (historical data series), 406 ; Value at risk models (VaR), 366–8 , 405 , 424 ; Viniar problem (problem of model failure), 6–7 , 58 , 68 , 109 , 150 , 176 , 202 , 241 , 242 , 246–7 , 331 , 366–8 ; ‘wind tunnel’ models, 309 , 339 , 392 ; winner’s curse model, 256–7 ; World Economic Outlook, 349 ; see also axiomatic rationality; maximising behaviour; optimising behaviour; small world models Economic Policy Symposium, Jackson Hole, 317–18 economics: adverse selection process, 250–1 , 327 ; aggregate output and GDP, 95 ; ambiguity of variables/concepts, 95–6 , 99–100 ; appeal of probability theory, 42–3 ; ‘bubbles’, 315–16 ; business cycles, 45–6 , 347 ; Chicago School, 36 , 72–4 , 86 , 92 , 111–14 , 133–7 , 158 , 257–8 , 307 , 342–3 , 381–2 ; data as essential, 388–90 ; division of labour, 161 , 162 , 172–3 , 216 , 249 ; and evolutionary mechanisms, 158–9 ; ‘expectations’ concept, 97–8 , 102–3 , 121–2 , 341–2 ; forecasts and future planning as necessary, 103 ; framing of problems, 261 , 362 , 398–400 ; ‘grand auction’ of Arrow and Debreu, 343–5 ; hegemony of optimisation, 40–2 , 110 – 14 ; Hicks–Samuelson axioms, 435–6 ; market fundamentalism, 220 ; market price equilibrium, 254 , 343–4 , 381–2 ; markets as necessarily incomplete, 344 , 345 , 349 ; Marshall’s definition of, 381 , 382 ; as ‘non-stationary’, 16 , 35–6 , 45–6 , 102 , 236 , 339–41 , 349 , 350 , 394–6 ; oil shock (1973), 223 ; Phillips curve, 340 ; and ‘physics envy’, 387 , 388 ; and power laws, 238–9 ; as practical knowledge, 381 , 382–3 , 385–8 , 398 , 399 , 405 ; public role of the social scientist, 397–401 ; reciprocity in a modern economy, 191–2 , 328–9 ; and reflexivity, 35–6 , 309 , 394 ; risk and volatility, 124–5 , 310 , 333 , 335–6 , 421–3 ; Romer’s ‘mathiness’, 93–4 , 95 ; shift or structural break, 236 ; Adam Smith’s ‘invisible hand’, 163 , 254 , 343 ; social context of, 17 ; sources of data, 389 , 390 ; surge in national income since 1800, 161 ; systems as non-linear, 102 ; teaching’s emphasis on quantitative methods, 389 ; validity of research findings, 245 ‘Economists Free Ride, Does Anyone Else?’

Global Catastrophic Risks
by Nick Bostrom and Milan M. Cirkovic
Published 2 Jul 2008

'R' appears in the third-letter position of more English words than in the first-letter position, yet it is much easier to recall words that begin with ' R' than words whose third letter is 'R'. Thus, a majority of respondents guess that words beginning with 'R' are more frequent, when the reverse is the case (Tversky and Kahneman, 1973). Biases implicit in the availability heuristic affect estimates of risk. A pioneering study by Lichtenstein et al. ( 1 978) examined absolute and relative probability judgements of risk. People know in general terms which risks cause large numbers of deaths and which cause few deaths. However, asked to quantify risks more precisely, people severely overestimate the frequency of rare causes of death and severely underestimate the frequency of common causes of death.

…

Leitenberg 452, 475 asteroid defence, cost-benefit analysis 187, 193 asteroids 14- 1 5 a s source o f dust showers 23 1-3 asteroid strikes 5-6, 5 1-2, 184, 214- 1 5 , 258-9 as cause of mass extinctions 255 contemporary risk 233-4 distribution functions 125 effects 229-31 frequency dynamical analysis of near-Earth objects 226-9 impact craters 223-5 near·Earth object searches 226 on Moon 127 mythological accounts 222 uncertainties 234-5 astrobiology 128-9 astroengineering 1 3 3 astronomy, Malquist bias 120 Atlantic meridional overturning circulation disruption 274, 281 atmosphere carbon dioxide content 243, 244 effects of volcanic eruptions 206-7, 208 protection from extraterrestrial radiation 239 atmospheric Atomic Energy Commission (AEC) 404 atomic fetishism, as motivation for nuclear terrorism 410, 4 1 7 attitude polarization 100 attitude strength effect 100 audience impact, nuclear terrorism 407 Aum Shinrikyo 85, 406, 409, 410, 467 attempted anthrax attack 466 interest in Ebola virus 467 nuclear ambitions 417, 419-20, 422 authoritarianism 507, 509 Autonomous Pathogen Detection System (APDS) 469 availability heuristic, biases 92-3 avoidable risk 176 Aztecs, effect of smallpox 296 Bacillus anthracis 456 bacteria, drug resistance 302-3 bacterial toxins 300 Bacteriological (Biological) and Toxins Weapons Convention (BWC), 1972 453-4, 462, 463 Baillie, M .G . L. 233 Exodus to Arthur 235 balancing selection 50, 63 ballistic nuclear weapons 396 Bandura, A. 408 Banks, E . , Catastrophic Risk 165, 182 Baron, J. and Greene, J. 106 Barrett, J .

…

J 45 behavioural evolution ongoing 61-3 as response to climate change 66-7 behavioural genetics, role in totalitarianism 5 1 1 behavioural responses t o climate change 279 Bensimon, C.M. and Upshur, R.E.G. 472 Benton, M . ) . and Twitchett, R.J. 273 biases 10, 91-2, 1 1 3-15 affect heuristic 104-5 anchoring, adjustment and contamination effects 101-4 anthropomorphism 308-11, 312, 326 in availability heuristic 92-3 Black Swans 94-5 bystander apathy 109-1 1 cautions 1 1 1-12 confirmation bias 98-101 conjunction fallacy 95-8 hindsight bias 93-4 overconfidence 107-9 scope neglect 105-7 technical experts 19 see also observation selection effects big bang, laws of physics 355 Bill and Melinda Gates Foundation 473 Bindeman, I .

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

The idea that a repeated pattern, such as a roulette wheel coming up black, must at some point be reversed is very common – even though the wheel of course has no memory of the past. The same bias can explain why investors buy into falling share prices on the expectation that at some point at least they must rise. Chapter 10 • Daniel Kahneman225 The availability heuristic in turn leads to biases, including the retrievability, imaginability and illusory correlation biases. Events that are easy to recall or which readily spring to mind – and so can easily be ‘retrieved’ – are more likely to be used by someone making a quick decision than information that would have to be researched.

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

The market was euphoric (India’s Nifty index went up 7× between 2003 and 2007), and liquidity dried up completely (credit markets froze after the collapse of Lehman Brothers). How can excessive booms and busts take place so frequently in an efficient market whose primary foundation is rooted in the combined assumptions of utility-maximizing behavior, market equilibrium, and stable preferences? The answer is found in what Daniel Kahneman describes as the “availability heuristic” (one of the most insidious and potent cognitive biases): People tend to assess the relative importance of issues by the ease with which they are retrieved from memory—and this is largely determined by the extent of coverage in the media. Frequently mentioned topics populate the mind even as others slip away from awareness.

…

(Zell), 189 analytical reading, 17 anchoring: biases from, 136, 335–338; stock prices and, 337–338 AngelList, 18 Aniston, Jennifer, 322 annual growth rate, long-term, 197 annualizing, 310 anomalies, 310 Apple, 222 app risk, 170 Archilochus, 27 Aristotle, 64 Arjuna, 332 Ars Poetica (Horace), 261 asset bubbles, 282 Astral Poly Technik, 323 asymmetric payoffs, 314 attention span, of Munger, 30 Aurelius, Marcus, 284, 355 authenticity: of brands, 223; Buffett on, 87–88; personal philosophy and, 251–252 authority, overinfluence of, 333–335 availability bias, 14 availability heuristic, 237–238 Avanti Feeds, 309–310, 323 average net fixed assets, 132 B2B. See business-to-business B2C. See business-to-consumer Babe Ruth effect, 247–248, 314 bad habits, 113, 359 Bakshi, Sanjay, 109, 293, 305–306; on personal prejudices, 297 balance sheet: analysis, 132–133; as asset, 312–313 Balrampur Chini, 187 Bandhan Bank, 369 Bandyopadhyay, Tamal, 369 Bank of America, 186 bankruptcy, 240, 338 Barber, Brad, 348 Barnum, P.

Progress: Ten Reasons to Look Forward to the Future
by Johan Norberg
Published 31 Aug 2016

When something bad happens anywhere, two billion smartphones will nowadays make sure that we find out, even if no reporters are on the scene. The psychologists Daniel Kahneman and Amos Tversky have shown that people do not base their estimates of how frequent something is on data, but on how easy it is to recall examples from memory.16 This ‘availability heuristic’ means that the more memorable an incident is, the more probable we think it is, so we imagine that horrible and shocking things, which stay in our thoughts, are more frequent than they are. We are probably built to be worried. We are interested in exceptions. We notice the new things, the strange and unexpected.

The Ministry for the Future: A Novel
by Kim Stanley Robinson
Published 5 Oct 2020

But unavoidable mistakes have been demonstrated in test after test, and given names like anchor bias (you want to stick to your first estimate, or to what you have been told), ease of representation (you think an explanation you can understand is more likely to be true than one you can’t). On and on it goes— online there is an excellent circular graphic display of cognitive errors— a wheel of mistakes that both lists them and organizes them into categories, including the law of small numbers, neglect of base rates, the availability heuristic, asymmetrical similarity, probability illusions, choice framing, context segregation, gain/loss asymmetry, conjunction effects, the law of typicality, misplaced causality, cause/effect asymmetry, the certainty effect, irrational prudence, the tyranny of sunk costs, illusory correlations, and unwarranted overconfidence— the graphic itself being a funny example of this last phenomenon, in that it pretends to know how we think and what would be normal.

…

The big banner with the Keeling Curve stretching across it, with its continuous rise, then the leveling, then the recent downturn, stood over everything else like a flag. And under that, there was so much going on that she had never heard of. She felt again the power of the cognitive error called the availability heuristic, in which you feel that what is real is what you know. But there was so much more going on than any one person could know, reality was so much bigger than the self, that it was alarming to contemplate. This explained the error: one felt the vastness and shrunk in on oneself like a snail’s horns, instinctively trying to protect one’s mind.

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

If the “law of the jungle” means anything, it means collaboration for the most part, with a few perceptional distortions caused by our otherwise well-functioning risk-management intuitions. Even predators end up in some type of arrangement with their prey. HISTORY SEEN FROM THE EMERGENCY ROOM History is largely peace punctuated by wars, rather than wars punctuated by peace. The problem is that we humans are prone to the availability heuristic, by which the salient is mistaken for the statistical, and the conspicuous and emotional effect of an event makes us think it is occurring more regularly than in reality. This helps us to be prudent and careful in daily life, forcing us to add an extra layer of protection, but it does not help with scholarship.

Know Thyself
by Stephen M Fleming
Published 27 Apr 2021

New York: Oxford University Press, 2011. Case, Paula. “Dangerous Liaisons? Psychiatry and Law in the Court of Protection—Expert Discourses of ‘Insight’ (and ‘Compliance’).” Medical Law Review 24, no. 3 (2016): 360–378. Cervone, Daniel. “Effects of Envisioning Future Activities on Self-Efficacy Judgments and Motivation: An Availability Heuristic Interpretation.” Cognitive Therapy and Research 13, no. 3 (1989): 247–261. Cervone, Daniel, and Philip K. Peake. “Anchoring, Efficacy, and Action: The Influence of Judgmental Heuristics on Self-Efficacy Judgments and Behavior.” Journal of Personality and Social Psychology 50, no. 3 (1986): 492.

Made to Stick: Why Some Ideas Survive and Others Die
by Chip Heath and Dan Heath
Published 18 Dec 2006

A good summary of the issues can be found in Ernest T. Goetz and Mark Sadoski, “Commentary: The Perils of Seduction: Distracting Details or Incomprehensible Abstractions?” Reading Research Quarterly 30 (1995), 500–11. In 1986, Jonathan Shedler and Melvin Manis: Jonathan Shedler and Melvin Manis, “Can the Availability Heuristic Explain Vividness Effects?” Journal of Personality and Social Psychology 51 (1986), 26–36. “If, say, a soccer team”: The Covey example is from an excerpt from his book reprinted in Fortune, November 29, 2004, 162. A SHARK A DEER: We thank Tim O’Hara for the idea for the comparison in Message 2 of the Shark Attack Hysteria Clinic.

The Loop: How Technology Is Creating a World Without Choices and How to Fight Back
by Jacob Ward
Published 25 Jan 2022

Kahneman and Tversky went on in the paper to describe how our vulnerability to representativeness also means we often make mistakes about the nature of chance, the effects of sample size, whether we have reliable information, and whether something can be predicted at all. They fundamentally destabilized our self-image as a rational species. And that was just the fourth page of the paper. Next, they turned to the availability heuristic, in which “people assess the frequency of a class or the probability of an event by the ease with which instances of occurrences can be brought to mind.” Our ability to remember something alters how common we consider that thing to be. Why is that a problem? “Availability”—our ability to think of an example—“is affected by factors other than frequency and probability,” they wrote.

Deep Value
by Tobias E. Carlisle
Published 19 Aug 2014

The question was then altered such that no information was conveyed about the individual’s personality, and the subjects used the underlying probabilities properly. The example illustrates that when no specific, representative evidence is given, we use the prior probabilities correctly, but when worthless representative evidence is given, we tend to ignore the prior probabilities and become distracted by the representative evidence. The availability heuristic leads us to consider only those things that can be brought to mind with ease, often because we have personal experience with them. For example, we assess the risk of heart attack among middle-aged people by recalling such occurrences among our acquaintances, rather than by considering the underlying probabilities.

Originals: How Non-Conformists Move the World
by Adam Grant
Published 2 Feb 2016

Glen Hass and Darwyn Linder, “Counterargument Availability and the Effects of Message Structure on Persuasion,” Journal of Personality and Social Psychology 23 (1972): 219–33. We use ease of retrieval: Norbert Schwarz, Herbert Bless, Fritz Strack, Gisela Klumpp, Helga Rittenauer-Schatka, and Annette Simons, “Ease of Retrieval as Information: Another Look at the Availability Heuristic,” Journal of Personality and Social Psychology 61 (1991): 195–202. they actually liked him more: Geoffrey Haddock, “It’s Easy to Like or Dislike Tony Blair: Accessibility Experiences and the Favourability of Attitude Judgments,” British Journal of Psychology 93 (2002): 257–67. tap out the rhythm of a song: Elizabeth L.

The Weather of the Future
by Heidi Cullen
Published 2 Aug 2010

M. et al., Communication and Mental Processes: Experiential and Analytic Processing of Uncertain Climate Information. Global Environmental Change 17 (1), 47–58 (2007). 4. Hirshleifer, D., and Shumway, T., Good Day Sunshine: Stock Returns and the Weather. Journal of Finance 58 (3), 1009–1032 (2003). 5. Sunstein, C. R., The Availability Heuristic, Intuitive Cost-Benefit Analysis, and Climate Change. Climatic Change 77 (1–2), 195–210 (2006). 1. Imbrie, J., and Imbrie, K. P., Ice Ages: Solving the Mystery (Enslow Publishers, Short Hills, NJ, 1979). 2. Weart, S. R. ed., The Discovery of Global Warming (Harvard University Press, Cambridge, MA, 2003). 3.

The Intelligence Trap: Revolutionise Your Thinking and Make Wiser Decisions
by David Robson
Published 7 Mar 2019

Seeing the original price anchors your perception of what is an acceptable price to pay, meaning that you will go above your initial budget. If, on the other hand, you had not seen the original price, you would have probably considered it too expensive, and moved on. You may also have been prey to the availability heuristic, which causes us to over-estimate certain risks based on how easily the dangers come to mind, thanks to their vividness. It’s the reason that many people are more worried about flying than driving – because reports of plane crashes are often so much more emotive, despite the fact that it is actually far more dangerous to step into a car.

Mindf*ck: Cambridge Analytica and the Plot to Break America
by Christopher Wylie
Published 8 Oct 2019

They tested for five letters (k, l, n, r, and v) like this. It is easier for people to think of words by first letter because we are taught to organize (or alphabetize) words by their first letter. However, people conflate this ease of recall with frequency or probability, even when this is far from the truth. This cognitive bias is called the availability heuristic, and is just one of many biases that affect our thinking. The bias is why, for example, people who see more news reports of violent murders on the news tend to think that society is becoming more violent when in fact global murder rates have been declining overall during the last quarter century.

How Big Things Get Done: The Surprising Factors Behind Every Successful Project, From Home Renovations to Space Exploration
by Bent Flyvbjerg and Dan Gardner
Published 16 Feb 2023

Goldstein, “Reasoning the Fast and Frugal Way: Models of Bounded Rationality,” Psychological Review 103, no. 4 (1996): 650–69; Gerd Gigerenzer, “Models of Ecological Rationality: The Recognition Heuristic,” Psychological Review 109, no. 1 (2002): 75–90. Gerd Gigerenzer is the leading proponent of this school. The second school concentrates on “negative heuristics,” defined as heuristics that trip up people, violating basic laws of rationality and logic; e.g., the availability heuristic and the anchoring heuristic; see Amos Tversky and Daniel Kahneman, “Availability: A Heuristic for Judging Frequency and Probability,” Cognitive Psychology 5, no. 2 (September 1973): 207–32; Daniel Kahneman, “Reference Points, Anchors, Norms, and Mixed Feelings,” Organizational Behavior and Human Decision Processes 51, no. 2 (1992): 296–312.

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

So the death count of a war in 1600, for instance, would have to be multiplied by 4.5 for us to compare its destructiveness to those in the middle of the 20th century.9 The second illusion is historical myopia: the closer an era is to our vantage point in the present, the more details we can make out. Historical myopia can afflict both common sense and professional history. The cognitive psychologists Amos Tversky and Daniel Kahneman have shown that people intuitively estimate relative frequency using a shortcut called the availability heuristic: the easier it is to recall examples of an event, the more probable people think it is.10 People, for example, overestimate the likelihoods of the kinds of accidents that make headlines, such as plane crashes, shark attacks, and terrorist bombings, and they underestimate those that pile up unremarked, like electrocutions, falls, and drownings.11 When we are judging the density of killings in different centuries, anyone who doesn’t consult the numbers is apt to overweight the conflicts that are most recent, most studied, or most sermonized.

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Dionysian cultures apologies Aquinas, Saint Thomas Arafat, Yasir archaeology Archer, John Archimedes Ardipithecus ramidus Arendt, Hannah Argentina aristocrats deaths from violence Aristophanes, Lysistrata Aristotle armed forces: as band of brothers conscription of effectiveness of Ethical Marine Warrior file closers mercenary military revolution reluctance to shoot size of willingness to die Armenians, genocide of Aronson, Elliot Asal, Victor Asch, Solomon Ash-Sheikh, Abdulaziz Asia: abortion in female infanticide in historiography in homicide rates in hunter-gatherers in legal discrimination in massacres in New Peace in spanking in violence against animals in violence against women in wars in assassinations; see also regicide Astell, Mary Athens, democracy in Atlas, Charles Atran, Scott atrocities, twenty worst in history attention deficit hyperactivity disorder Atwood, Brian Augustine, Saint Australia domestic violence in homicide in peace imposed in New Guinea penal colony warfare among aborigines australopithecenes Austria-Hungary autarky; see also trade, international Authority Ranking autocracy and Age of Nationalism and democide Islamic punishment in and the social dilemma autonomic nervous system Autonomy, ethic of availability heuristic Axelrod, Robert Aztecs baby boomers: crime among influence of television on and 1960s counterculture Bacon, Francis balance of power balance of terror Bales, Kevin Bandura, Albert Bangladesh Barbara, Saint Barth, Karl Batson, Daniel Baumeister, Roy Evil and self-control Bays, Paul Beatles Beccaria, Cesare On Crimes and Punishments Beirut, U.S. servicemen bombed in Belarus Belgium Bell, David Bell, Derrick bell curve, see normal distribution Belloc, Hillaire Benedict, Ruth, Patterns of Culture Bentham, Jeremy Berlin, Isaiah Berlin Wall Bethmann-Hollweg, Theobald von Betzig, Laura Bhagavad-Gita Bhutto, Benazir Bible: capital punishment in debt bondage in historical analysis of and homophobia human sacrifice in and legislation New Testament Old Testament popularity of slavery in Big Parade, The (film) Bill of Rights, U.S.

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death toll in Clark, Gregory clash of civilizations Clauset, Aaron Clausewitz, Karl von Clay, Henry Cleaver, Eldridge Cleveland, Robert Nasruk climate change Clinton, Bill Clockwork Orange, A (film) cluster illusion Cobden, Richard Cochran, Gregory Cockburn, J. S. code of the streets; see also honor cognitive dissonance cognitive illusions, xxiii; see also availability heuristic; cluster illusion; conjunction fallacy; loss aversion; overconfidence; positive illusions; sunk-cost fallacy Cohen, Dov Cohen, Jonathan Cold War end of interstate wars in Europe mutually assured destruction proxy wars superpower confrontations Cole, Michael Collier, Paul Collins, Randall Colombia Columbine High School commerce: Christian ideology vs.

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

This is called the illusion-of-truth effect. You believe to be true what you hear often. The same applies to whatever comes to mind first or most easily. People, including you, believe the examples they can think of right away to be most representative and therefore indicative of the truth. This is called the availability heuristic. Let me give you a famous example. In English, what is the relative proportion of words that start with the letter K versus words that have the letter K in third position? The reason most people believe the former to be more common than the latter is that they can easily remember a lot of words that start with a K but few that have a K in the third position.

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

” • • • Which is more common, words that begin with r (like “road”) or words with r as the third letter (like “car”)? Most say that words beginning with r are more common. It’s easy to rattle off words beginning with r; harder and slower to free-associate words with r in third place. This is an example of the availability heuristic, and here it leads us astray. Words with r in third place happen to be more common. But because words beginning with r are more mentally available, we overrate how common they are. A familiar example of availability is the way we all assume that the tastes, politics, education level, and TV viewing habits of our social set are widely shared.

Miracle Cure
by William Rosen
Published 14 Apr 2017

It took another fifty years before the notoriously conservative Lords of the Admiralty issued an order that made drinking lemon, and later lime, juice—hence “limeys”—compulsory for sailors on long voyages. * In the taxonomy of cognitive science, these are known, respectively, as anchoring, the availability heuristic, and confirmation bias. * Not everyone was as punctilious about ethics as the Tuberculosis Trials Committee. At the moment in 1947 when the war crimes tribunal convicted seventeen of the twenty-three defendants in the so-called Doctors’ Trial and published the ten-point Nuremberg Code for human experimentation, the infamous Tuskegee syphilis experiments had already been under way for fifteen years, and would continue observing the course of untreated syphilis in African American men for another twenty-five years

Paved Paradise: How Parking Explains the World
by Henry Grabar
Published 8 May 2023

Monthly parking functioned like a gym membership, one of those sunk costs workers felt guilty for not making the most of every day. * * * — Even if parking is a kind of narcotic that creates its own demand, we mostly managed to build enough parking. So why does parking feel scarce? Part of the explanation may be what psychologists call the availability heuristic: It’s easy to recall the anxiety of being unable to park. It’s a terrible feeling to be trapped behind the wheel, especially when you have to pee. It’s also easy to remember finding a great spot on a crowded block. But when parking is easy, it is as thoughtless an experience as you can have behind the wheel.

When Computers Can Think: The Artificial Intelligence Singularity
by Anthony Berglas , William Black , Samantha Thalind , Max Scratchmann and Michelle Estes
Published 28 Feb 2015

It is a good essay on critical thinking and the dangers of lazy analysis. For example, he considers the question A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?. Most people say 10 cents which is, of course, wrong. Muehlhauser notes that due to the availability heuristic, your brain will tell you that an AGI wiping out mankind is incredibly unlikely because you’ve never encountered this before. He also notes that extraordinary claims require extraordinary evidence. One point Muehlhauser refutes is that people that write about AGI are merely atheists whose fear of nihilism make them seek a moral purpose to save the world and fall for the seduction of Singularitarianism.

Imaginable: How to See the Future Coming and Feel Ready for Anything―Even Things That Seem Impossible Today
by Jane McGonigal
Published 22 Mar 2022

(Geneva, Switzerland: World Economic Forum, 2021), 88, http://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2021.pdf. 5 Jamie Ducharme, “COVID-19 Is Making America’s Loneliness Epidemic Even Worse,” Time, May 8, 2020, https://time.com/5833681/loneliness-COVID-19/; Philip Jefferies and Michael Ungar, “Social Anxiety in Young People: A Prevalence Study in Seven Countries,” PLOS One 15, no. 9 (2020): e0239133, https://doi.org/10.1371/journal.pone.0239133; “The Impact of Covid-19 on Young People with Mental Health Needs,” Summer 2020 Survey, Young Minds, accessed August 27, 2012, https://youngminds.org.uk/about-us/reports/coronavirus-impact-on-young-people-with-mental-health-needs/. 6 Haim Omer and Nahman Alon, “The Continuity Principle: A Unified Approach to Disaster and Trauma,” American Journal of Community Psychology 22, no. 2 (April 1994): 273–87, https://doi.org/10.1007/BF02506866. 7 Mark Murphy, “Leadership IQ Study: Mismanagement, Inaction Among the Real Reasons Why CEOs Get Fired,” Cision, June 21, 2005, http://www.prweb.com/releases/2005/06/prweb253465.htm. 8 Ann Garrison, “Should California Secede? An Interview with David Swanson,” Free Press, February 12, 2017, https://freepress.org/article/should-california-secede-interview-david-swanson. 9 John S. Carroll, “The Effect of Imagining an Event on Expectations for the Event: An Interpretation in Terms of the Availability Heuristic,” Journal of Experimental Social Psychology 14, no. 1 (January 1978): 88–96, https://doi.org/10.1016/0022-1031(78)90062-8. 10 Steven J. Sherman et al., “Imagining Can Heighten or Lower the Perceived Likelihood of Contracting a Disease: The Mediating Effect of Ease of Imagery,” Personality and Social Psychology Bulletin 11, no. 1 (1985): 118–127, https://doi.org/10.1177/0146167285111011. 11 Other highly cited studies in this area of research include: Richard J.

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

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.) 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.)

Rationality: From AI to Zombies
by Eliezer Yudkowsky
Published 11 Mar 2015

Perhaps the machinery is evolutionarily optimized to purposes that actively oppose epistemic accuracy; for example, the machinery to win arguments in adaptive political contexts. Or the selection pressure ran skew to epistemic accuracy; for example, believing what others believe, to get along socially. Or, in the classic heuristic-and-bias, the machinery operates by an identifiable algorithm that does some useful work but also produces systematic errors: the availability heuristic is not itself a bias, but it gives rise to identifiable, compactly describable biases. Our brains are doing something wrong, and after a lot of experimentation and/or heavy thinking, someone identifies the problem in a fashion that System 2 can comprehend; then we call it a “bias.” Even if we can do no better for knowing, it is still a failure that arises, in an identifiable fashion, from a particular kind of cognitive machinery—not from having too little machinery, but from the machinery’s shape.

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While I am not averse (as you can see) to discussing definitions, we should remember that is not our primary goal. We are here to pursue the great human quest for truth: for we have desperate need of the knowledge, and besides, we’re curious. To this end let us strive to overcome whatever obstacles lie in our way, whether we call them “biases” or not. * 5 Availability The availability heuristic is judging the frequency or probability of an event by the ease with which examples of the event come to mind. A famous 1978 study by Lichtenstein, Slovic, Fischhoff, Layman, and Combs, “Judged Frequency of Lethal Events,” studied errors in quantifying the severity of risks, or judging which of two dangers occurred more frequently.1 Subjects thought that accidents caused about as many deaths as disease; thought that homicide was a more frequent cause of death than suicide.

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A neighboring flaw is the logical fallacy of arguing from imaginary evidence: “Well, if you did go to the end of the rainbow, you would find a pot of gold—which just proves my point!” (Updating on evidence predicted, but not observed, is the mathematical mirror image of hindsight bias.) The brain has many mechanisms for generalizing from observation, not just the availability heuristic. You see three zebras, you form the category “zebra,” and this category embodies an automatic perceptual inference. Horse-shaped creatures with white and black stripes are classified as “Zebras,” therefore they are fast and good to eat; they are expected to be similar to other zebras observed.

New York 2140
by Kim Stanley Robinson
Published 14 Mar 2017

“But isn’t it a little weird that we have all the right players here to change the world?” Charlotte shakes her head. “Confirmation bias. That or else representation error. I’m forgetting the name, shit. It’s the one where you think what you see is all of what’s going on. A very elementary cognitive error.” “Ease of representation,” Jeff says. “It’s an availability heuristic. You think what you see is the totality.” “That’s right, that’s the one.” Mutt acknowledges this, but says, “On the other hand, we do have quite a crew here.” Charlotte says, “Everybody does. There are two thousand people living in this building, and you only know twenty of them, and I only know a couple hundred, and so we think they’re the important ones.

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't
by Nate Silver
Published 31 Aug 2012

Murrah Federal Building, 425 algorithms, 265, 426 all-in bet, 306 Allison, Graham, 433–35 Al Qaeda, 422, 424, 425, 426, 433, 435–36, 440, 444 Alzheimer’s, 420 Amazon.com, 352–53, 500 American exceptionalism, 10 American Football League (AFL), 185–86, 480 American League, 79 American Stock Exchange, 334 Amsterdam, 228 Anchorage, Alaska, 149 Anderson, Chris, 9 Angelo, Tommy, 324–26, 328 animals, earthquake prediction and, 147–48 Annals of Applied Statistics, 511–12 ANSS catalog, 478 Antarctic, 401 anthropology, 228 antiretroviral therapy, 221 Apple, 264 Archilochus, 53 Arctic, 397, 398 Arianism, 490 Aristotle, 2, 112 Armstrong, Scott, 380–82, 381, 388, 402–3, 405, 505, 508 Arrhenius, Svante, 376 artificial intelligence, 263, 293 Asia, 210 asset-price bubble, 190 asymmetrical information, 35 Augustine, Saint, 112 Australia, 379 autism, 218, 218, 487 availability heuristic, 424 avian flu, see bird flu A/Victoria flu strain, 205–6, 208, 483 Babbage, Charles, 263, 283 Babyak, Michael, 167–68 baby boom, 31 Babylonians, 112 Bachmann, Michele, 217 bailout bills, 19, 461 Bak, Per, 172 Baker, Dean, 22 Bane, Eddie, 87 Bank of England, 35 Barbour, Haley, 140 baseball, 9, 10, 16, 74–106, 128, 426, 446, 447, 451n aging curve in, 79, 81–83, 81, 83, 99, 164 betting on, 286 luck vs. skill in, 322 minor league system in, 92–93 results in, 327 rich data in, 79–80, 84 Baseball America, 75, 87, 89, 90, 90, 91 Baseball Encyclopedia, 94 Baseball Prospectus, 75, 78, 88, 297 basic reproduction number (R0), 214–15, 215, 224, 225, 486 basketball, 80n, 92–93, 233–37, 243, 246, 256, 258, 489 batting average, 86, 91, 95, 100, 314, 321, 321, 339 Bayer Laboratories, 11–12, 249 Bayes, Thomas, 240–43, 251, 253, 254, 255, 490 Bayesian reasoning, 240, 241–42, 259, 349, 444 biases and beliefs in, 258–59 chess computers’ use of, 291 Christianity and, 490 in climatology, 371, 377–78, 403, 406–7, 407, 410–11 consensus opinion and, 367 Fisher’s opposition to, 252 gambling esteemed in, 255–56, 362 priors in, 244, 245, 246, 252, 255, 258–59, 260, 403, 406–7, 433n, 444, 451, 490, 497 stock market and, 259–60 Bayes’s theorem, 15, 16, 242, 243–49, 246, 247, 248, 249, 250, 258, 266, 331, 331, 448–49, 450–51 in poker, 299, 301, 304, 306, 307, 322–23 Beane, Billy, 77, 92, 93–94, 99–100, 103, 105–7, 314 Bear Stearns, 37 beauty, complexity and, 173 beer, 387, 459 behavioral economics, 227–28 Belgium, 459 Bellagio, 298–99, 300, 318, 495 bell-curve distribution, 368n, 496 Bengkulu, Indonesia, 161 Benjamin, Joel, 281 Berlin, Isaiah, 53 Berners-Lee, Tim, 448, 514 BetOnSports PLC, 319 bets, see gambling Betsy, Hurricane, 140 betting markets, 201–3, 332–33 see also Intrade biases, 12–13, 16, 293 Bayesian theory’s acknowledgment of, 258–59 in chess, 273 and errors in published research, 250 favorite-longshot, 497 of Fisher, 255 objectivity and, 72–73 toward overconfidence, 179–83, 191, 203, 454 in polls, 252–53 as rational, 197–99, 200 of scouts, 91–93, 102 of statheads, 91–93 of weather forecasts, 134–38 Bible, 2 Wicked, 3, 13 Biden, Joseph, 48 Big Data, 9–12, 197, 249–50, 253, 264, 289, 447, 452 Big Short, The (Lewis), 355 Billings, Darse, 324 Bill James Baseball Abstract, The, 77, 78, 84 bin Laden, Osama, 432, 433, 434, 440, 509 binomial distribution, 479 biological weapons, 437, 438, 443 biomedical research, 11–12, 183 bird flu, 209, 216, 229 Black, Fisher, 362, 367, 369 “Black Friday,” 320 Black Swan, The (Taleb), 368n Black Tuesday, 349 Blanco, Kathleen, 140 Blankley, Tony, 50 Blodget, Henry, 352–54, 356, 364–65, 500 Blue Chip Economic Indicators survey, 199, 335–36 Bluefire, 110–11, 116, 118, 127, 131 bluffing, 301, 303, 306, 310, 311, 328 Bonus Baby rule, 94 books, 2–4 cost of producing, 2 forecasting and, 5 number of, 2–3, 3, 459 boom, dot-com, 346–48, 361 Boston, 77 Boston Red Sox, 63, 74–77, 87, 102, 103–5 Bowman, David, 161–62, 167 Box, George E.

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

Empirically we observe that various risk factors’ ex ante excess returns seem to be higher for a long while after a large negative factor shock. This pattern could reflect time-varying risk premia (the perceived amount and/or price of risk is high) or rational learning from the past but it is also suggestive of behavioral memory biases. Our probability assessments are distorted by the availability heuristic in that we tend to overweight strong signals, salient (vivid, emotion-triggering) events, and recent events. Memories decay only gradually after a major event; this fact might cause attractive reward-to-risk ratios to linger for surprisingly long periods. Famously, the ex ante equity premium remained high for over 20 years after the Great Depression (whereas investor memories were shorter after the 2000–2002 and 2007–2009 bear markets).

EuroTragedy: A Drama in Nine Acts
by Ashoka Mody
Published 7 May 2018

And so Pompidou wondered if “more Europe” could solve France’s problems and help it catch up. True, the European integration process had reached a successful end. But the narrative of more integration as a solution for European problems was still alive. Psychologists Amos Tversky and Daniel Kahneman coined the phrase “availability heuristic” to explain that human beings instinctively believe the world will continue to work in the future as it has in the recent past.65 Europe’s infrastructure seemed “available” to take another leap. The Hague 1969: The Third Leap Georges Pompidou was elected president of France in June 1969.