Adaptive Markets: Financial Evolution at the Speed of Thought
by Andrew W. Lo · 3 Apr 2017 · 733pp · 179,391 words
the power of efficient markets to use a market as a combination news aggregator and supercomputer. In fact, such markets already exist. They’re called prediction markets because that’s exactly what they do—make predictions. Their structure is fiendishly simple: create a financial security that pays $1 if a particular future
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price fully reflects all available information in the crowd. What a fantastic way to gather information. And not just about the Red Sox; imagine creating prediction markets for collecting information about terrorist events, flu epidemics, nuclear meltdowns, and presidential elections. This may sound a little like science fiction, but
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prediction markets were widely used in the United States in the nineteenth and early twentieth centuries to forecast elections before the advent of modern polling techniques.44
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price quotations as the election season heated up. In the 1920s and 1930s, specialist firms of “betting commissioners” operated out of offices on Wall Street. Prediction markets were widely viewed as producing the most accurate information about the state of the presidential races and were usually successful in forecasting the winner well
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with money and approaches Coolidge and Davis as dispassionately as it pronounces judgment on Anaconda and Bethlehem Steel.”45 With the advent of the Internet, prediction markets have experienced something of a renaissance. The Iowa Electronic Markets—originally the Iowa Presidential Stock Market—is perhaps the best-known research
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prediction market, dating back to the 1988 presidential election,46 but commercial prediction markets have also become popular. Many of these markets have done as well or better than conventional forecasting methods; for instance
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result few people expected. On the other hand, in some particularly rancorous political races, such as the 2012 presidential election, people have tried to manipulate prediction markets in attempts to gain “momentum” for their preferred candidate, in much the same way an unscrupulous trader might try to run up the price of
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of affecting the outcome of the election (at least, not given the small size of these markets), it did briefly damage the usefulness of the prediction market before it corrected itself. In the end, however, people with the more accurate prediction gained more money at the manipulators’ expense, just as the Efficient
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Markets Hypothesis would predict. But prediction markets are only one possible use of the Efficient Markets Hypothesis. A casual conversation I had with a former MIT colleague, a marketing professor named Ely
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portfolio theory, 27, 48, 212, 249 positron emission tomography (PET), 78, 110 Pounds, William, 35–36 poverty, 411–415, 416 prediction, 130 Prediction Company, 278 prediction markets, 38–40 preference orderings, 97 prefrontal cortex, 162, 279; damage to, 102–103, 107; dorsolateral, 337, 339; evolution of, 153–154, 163; as executive brain
Superminds: The Surprising Power of People and Computers Thinking Together
by Thomas W. Malone · 14 May 2018 · 344pp · 104,077 words
pass. Rather than asking our subjects to just make a simple prediction, we asked them to express their predictions by participating in a prediction market. Somewhat like futures markets, prediction markets let you buy and sell “shares” of predictions about possible future events. For instance, if you think the next play will very likely
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diverse approaches resulted in overall predictions that were better than either the people or computers made alone. It’s easy to imagine that cyber-human prediction markets like this could be used in many ways. Google and Microsoft, for example, have already let their employees use
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prediction markets to estimate completion dates for internal projects. The University of Iowa has been using prediction markets to predict the winners of US presidential races for decades. The Hollywood Stock Exchange website uses prediction markets to predict movie box-office receipts. In all these cases
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, the predictions from the prediction markets are usually as good as or better than any alternative prediction methods. Our results suggest that predictions for these and many
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other kinds of things could be even better if computer bots were involved, too. Taking Cyber-Human Prediction Markets Even Further One of the intriguing possibilities about using a market for prediction is that it provides incentives for people to get involved if, and
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successful. The upshot here is twofold: providing an incentive for bot designers to make increasingly smarter bots will lead to advances in AI, and the prediction markets in which those bots participate will become more accurate. It’s important to note, by the way, that this approach can be useful with the
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regression, that have existed for decades. Cyber-Human Markets for Everything Most of what we’ve just been saying applies to far more than just prediction markets; it applies to many other kinds of markets, too. Today’s financial markets are leading the way, with investment managers increasingly relying on quantitative, often
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other probabilities within it to be consistent with this new information.16 In fact, you could even imagine letting both people and computers participate in prediction markets, like those we saw in chapter 8, to estimate the probabilities of key events. Since people would be paid for making accurate predictions, this would
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included high prices open. However, many options are eliminated after evaluating only one or two questions, thus saving large amounts of unnecessary evaluation effort. Using Prediction Markets, Online Argumentation, and Voting Even after doing research about some of the questions, there may not be a clear answer either way. In these cases
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the warning and response systems for severe floods. For instance, it’s easy to imagine using many of the techniques we saw previously (such as prediction markets) to improve the accuracy and timeliness of coastal-flood warnings. It should also be possible to use all kinds of information technology tools to help
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–81; Pavel Atanasov, Phillip Rescober, Eric Stone, Samuel A. Swift, Emile Servan-Schreiber, Philip Tetlock, Lyle Ungar, and Barbara Mellers, “Distilling the Wisdom of Crowds: Prediction Markets vs. Prediction Polls,” Management Science 63, no. 3 (2016), http://dx.doi.org/10.1287/mnsc.2015.2374. 9. Atanasov et al., “Distilling the Wisdom
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and Slow (New York: Macmillan, 2011), chapter 21. 4. Yiftach Nagar and Thomas W. Malone, “Making Business Predictions by Combining Human and Machine Intelligence in Prediction Markets,” Proceedings of the International Conference on Information Systems, Shanghai, China, December 5, 2011, http://web.mit.edu/ynagar/www/papers/Nagar_Malone_MakingBusinessPredictionsbyCombiningHumanandMachineIntelligence.ICIS2011.pdf
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, Cambridge, MA, 2011), http://cci.mit.edu/publications/CCIwp2011-02.pdf. 5. Justin Wolfers and Eric Zitzewitz, “Prediction Markets,” Journal of Economic Perspectives 18, no. 2 (2004): 107–26. 6. Justin Wolfers and Eric Zitzewitz, “Interpreting Prediction Market Prices as Probabilities” (working paper no. W12200, National Bureau of Economic Research, Cambridge, MA, 2006). CHAPTER
On the Edge: The Art of Risking Everything
by Nate Silver · 12 Aug 2024 · 848pp · 227,015 words
its own terms. As you’ll see, I have complicated feelings about it. Act 4: Berkeley, California, September 2023. Set at Manifest, a conference on prediction markets—where you’ll meet everyone from a rationalist former OnlyFans model to a man who made hundreds of thousands of dollars betting on Biden even
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utilitarianism. The second is associated with Yudkowsky and the George Mason University economist Robin Hanson and a whole different cluster of topics: futurism, artificial intelligence, prediction markets, and being willing to argue about just about anything on the internet, including subjects that others often find taboo. Let’s start with the Singer
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MacAskill, Toby Ord Robin Hanson, Eliezer Yudkowsky, Nick Bostrom, Scott Alexander Favorite Subjects Animal welfare, global poverty reduction, effective giving, existential risk Artificial intelligence, futurism, prediction markets, cognitive biases, existential risk Political Orientation Center left, progressive, relatively well aligned with U.S. Democratic Party Libertarian, eclectic, suspicious of major parties Ethical Philosophy
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make probabilistic bets on real-world events. This might seem like a relatively obscure interest, but people in the EA/rationalist community are obsessed with prediction markets. Why? Partly because of their economist-brain appreciation for markets in general. But also because they believe we’ll have more accurate and honest discussions
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mean, and you’ll often be criticized even if your probabilities are as accurate as advertised.) There’s another reason that EAs and rationalists think prediction markets are important. It gets at the very definition of rationality itself. If you look up “rational” in a thesaurus, you’ll find that it’s
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in The Signal and the Noise, making testable predictions is one of the only ways to know whether you’re epistemically rational. So in theory, prediction markets play an important role in making the world more rational. But what about in practice? My views are mostly sympathetic, but not without some reservations
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. That’s in part because of some scar tissue from too many arguments I’ve had on the internet about the accuracy of prediction markets versus FiveThirtyEight forecasts. The FiveThirtyEight forecasts have routinely been better—I know that’s what you were expecting me to say, but it’s true
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to happen if the markets are efficient. Then again, maybe this doesn’t tell us that much. Elections are quite literally the Super Bowl of prediction markets—there’s so much dumb money out there (lots of people who have very strong opinions about politics) that there isn’t necessarily enough smart
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bet millions on Donald Trump after Joe Biden had already been declared the winner (we’ll get to that shortly). In many other circumstances, though, prediction markets are clearly quite useful. As news organizations scrambled to correct their coverage, for instance, traders at Manifold determined that the IDF probably hadn’t been
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responsible for whatever had happened on that particular night at the Gaza hospital.[*19] But the bigger concern I have, ironically enough, is that prediction markets may become less reliable if people trust them too much. When I spoke with MacAskill after the FTX collapse and asked him whether he ought
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had a debate on AI risk at Jane Street Capital in 2011, the firm that would later employ SBF. Some of the rationalist interest in prediction markets also stems from Hanson, who has expressed his support for futarchy, a system of government where decisions are made by betting markets. These various strands
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you’re going to get out of any one bite of the EA/rationalist cookie.[*24] If you’re someone like me who (mostly) likes prediction markets and thinks the concerns about existential risk and effective giving are well-placed, but is uncertain about futurism and downright wary about utilitarianism, it’s
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to Polymarket. The negotiations were not underway at the time I originally researched and wrote this chapter. *19 I’m also skeptical that play-money prediction markets can be as efficient as real-money ones. Still, as Austin Chen pointed out to me, rationalists are exactly the sort of people who care
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—although Riverians do have some similar ideas of their own. See also: Chesterton’s fence. Prediction market: A platform where people may bet on the outcome of real-world events, from presidential elections to personal occurrences. Prediction markets are highly regarded in the River because they are seen as promoting epistemic rationality, i.e
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: Kevin Roose, “The Wager That Betting Can Change the World,” The New York Times, October 8, 2023, sec. Technology, nytimes.com/2023/10/08/technology/prediction-markets-manifold-manifest.html. GO TO NOTE REFERENCE IN TEXT Magic: The Gathering: “Zvi Mowshowitz,” MTG Wiki, December 30, 2023, mtg.fandom.com/wiki/Zvi_Mowshowitz
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spoke with Habryka in an interview we arranged after Manifest, not during the event. GO TO NOTE REFERENCE IN TEXT prices that implied: Vitalik Buterin, “Prediction Markets: Tales from the Election,” Vitalik Buterin’s Website (blog), February 18, 2021, vitalik.eth.limo/general/2021/02/18/election.html. GO TO NOTE REFERENCE
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, 414 optimism and, 407–8, 413 poker and, 40, 46–48, 60–61, 430–33, 437, 439, 507n poor interpretability of, 433–34, 437, 479 prediction markets and, 369, 372 probabilistic thinking and, 439 randomization and, 438 rationalism and, 353, 355 regulation of, 270, 458, 541n religion and, 434 risk impact and
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and, 358–59, 366–67, 377 independence and, 358 overfitting/underfitting and, 361, 361, 362–68 poker and, 347–48, 367 politics and, 377–78 prediction markets and, 369 quantification and, 345–51, 359–60 rationalism and, 354–55 River and, 343 River-Village conflict and, 377 SBF and, 20, 340–42
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, 135–37, 158, 180 MacAskill, Will effective altruism brand and, 352 futurism and, 380 impartiality and, 359 longtermism and, 488 Elon Musk and, 344 on prediction markets, 373–74 quantification and, 347–48 SBF and, 20, 340, 341 on statistical modeling, 360–61 utilitarianism and, 378 Mac Aulay, Tara, 338, 400 Machine
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–21, 511n Elon Musk’s strategy, 251 origins of, 40 personality and, 111–17, 129–30 PokerGO studio, 48–49 post-oak bluffing, 64–65 prediction markets and, 370–71 preparation and, 233 prisoner’s dilemma and, 56–57, 508n privilege and, 82–83, 120–21 probabilistic thinking and, 41, 104–5
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forecasting, 13–14, 16–17, 27, 137, 182n, 433, 448n EV maximizing and, 14–15 expertise and, 272 gambling and, 17, 504n NFTs and, 326 prediction markets and, 373, 374–75, 535n probabilistic thinking and, 15, 17 reference classes and, 448n River-Village conflict and, 27–28, 29, 30, 267–68, 271
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(poker), 493 Pot-Limit Omaha (PLO), 487, 493 Poundstone, William, 396 Power Law, The (Mallaby), 286 precautionary principle, 493 Precipice, The (Ord), 369–70, 443 prediction markets, 369–75, 380, 493, 535n preflop (poker), 41, 493 preparation, 232–33 price discovery, 493 priors, 493–94; see also Bayes’ theorem prisoner’s dilemma
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, 264, 482 distribution, 9, 491 effective altruism and, 367 importance of, 15–16 poker and, 41, 104–5, 127, 154n, 237 politics and, 15, 17 prediction markets, 369–75, 493, 535n slots and, 153–55, 155 sports betting and, 16–17 theory invention, 22 See also EV maximizing probability distribution, 494 process
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existential risk and, 21, 457 defined, 352–53, 354, 495 effective hedonism and, 376 futurism and, 379 impartiality and, 377 politics and, 17, 377–78 prediction markets and, 369, 372–73, 380 River and, 343 tech sector and, 21 Upriver and, 20 utilitarianism and, 364 varying streams of, 355–56, 356, 380
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, 29, 117, 506n Las Vegas veneration of, 139 map of, 18, 19, 20–26 megalothymia and, 468 name of, 18, 42, 504n obsession and, 196 prediction markets and, 371–72, 493 process-oriented thinking and, 495 quantification and, 352 race and, 29, 506n rationalism and, 343 SBF’s presence in, 299 self
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, 346, 357, 400, 499 Truman, Harry S., 409 Trump, Donald casinos and, 142, 145, 146, 150–52, 514n effective altruism on, 378 NFTs and, 326 prediction markets and, 373, 375, 535n River and, 299 River-Village conflict and, 267–68 Silicon Valley and, 272 Peter Thiel and, 254n Village on, 30 Billy
Machine, Platform, Crowd: Harnessing Our Digital Future
by Andrew McAfee and Erik Brynjolfsson · 26 Jun 2017 · 472pp · 117,093 words
that are emergent and thus generate knowledge. As groups went online and became the crowd, innovators found different ways to detect and harvest this knowledge. Prediction markets were one of the earliest of these, and the ones that built most directly from Hayek’s insights. These are markets not for goods and
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in the box office in its first week, or the official US inflation rate averaging more than 3% over the next quarter. Here’s how prediction markets work. First, the market maker creates a set of securities that participants can buy and sell, just like they sell a company’s shares on
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did, in fact, average more than 3%, all the people holding the “above 3%” security would get $1 for every share they had. Results from prediction markets confirm Hayek’s insights about the knowledge-aggregating power of prices within markets. In markets like the ones just described, events with final share prices
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about $0.70 tend to actually happen about 70% of the time, making these prices pretty accurate probability estimates. There are active debates about whether prediction markets provide more accurate forecasts than other methods (such as properly weighted averages of polls, or reliance on the superforecasters identified by Philip Tetlock and discussed
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in Chapter 2), but few people anymore doubt that prediction markets can be very effective under the right conditions. As economist Robin Hanson, the scholar who has done the most to advance both the theory and
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practice of prediction markets, puts it, “Prediction markets reflect a fundamental principle underlying the value of market-based pricing: Because information is often widely dispersed among economic actors, it is highly desirable
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powerful force. The crowd is not unstructured, however. Its structure is emergent, appearing over time as a result of the interactions of members. Stock markets, prediction markets, and modern search engines extract valuable information from this emergent structure. Overcentralization fails because of Hayek’s insights and Polanyi’s Paradox: people can’t
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-it-really-means-to-be-kafkaesque/283096. 237 “The marvel [of prices]”: Hayek, “Use of Knowledge in Society.” 239 “Prediction markets reflect a fundamental principle”: Kenneth J. Arrow et al., “The Promise of Prediction Markets,” Science 320 (May 16, 2008): 877–78, http://mason.gmu.edu/~rhanson/PromisePredMkt.pdf. 240 “Hello everybody out there
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device design, 272–75 methods for handling unruliness of, 232–35 open-source software development by, 240–45 organizing, 239–49 origins, 229–30 and prediction markets, 237–39 principles for effective harnessing of, 241–45 Wikipedia and, 246–49 crowdfunding, 13–14, 262–63 crowdlending platforms, 263 cryptocurrency, 280, 296–97
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, 289–90 kinases, 116–17 kitchen, automated, 94 Kiva Systems, 103 Klein, Gary, 56 knowledge access to, in second machine age, 18 markets and, 332 prediction markets and, 238 knowledge differentials, See information asymmetries Kodak, 131, 132 Kohavi, Ronny, 45, 51 Kohl’s, 62–63 Koike, Makoto, 79–80 Komatsu, 99 Koum
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(s) centrally planned economies vs., 235–37 companies and, 309–11 costs inherent in, 310–11 as crowd, 235–39 information asymmetries and, 206–7 prediction markets, 237–39 production costs vs. coordination costs, 313–14 Markowitz, Henry, 268 Marshall, Matt, 62 Martin, Andrew, 40–41 Marx, Karl, 279 Masaka, Makoto, 79
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, 90 Pratt, Gil, 94–95, 97, 103–4 prediction data-driven, 59–60 experimentation and, 61–63 statistical vs. clinical, 41 “superforecasters” and, 60–61 prediction markets, 237–39 premium brands, 210–11 presidential elections, 48–51 Priceline, 61–62, 223–24 price/pricing data-driven, 47; See also revenue management demand
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curves and, 154 elasticities, 212–13 loss of traditional companies’ power over, 210–11 in market economies, 237 and prediction markets, 238–39 product makers and platform prices, 220 supply curves and, 154–56 in two-sided networks, 213–16 Principia Mathematica (Whitehead and Russell), 69
Chaos Kings: How Wall Street Traders Make Billions in the New Age of Crisis
by Scott Patterson · 5 Jun 2023 · 289pp · 95,046 words
The Quiet Coup: Neoliberalism and the Looting of America
by Mehrsa Baradaran · 7 May 2024 · 470pp · 158,007 words
on FTX ran the gamut: price speculation on cryptotokens, NFTs, and derivatives of NFTs, as well as variations on sports betting and speculation in the prediction markets. Market mayhem—and likely increased boredom during the pandemic—led to incredible rates of trading on the platform, and, in turn, skyrocketing revenues. In 2020
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
by Eric Siegel · 19 Feb 2013 · 502pp · 107,657 words
. The collective intelligence of a crowd emerges on many occasions, as explored thoroughly by James Surowiecki in his book The Wisdom of Crowds. Examples include: Prediction markets, wherein a group of people together estimate the prospects for a horse race, political event, or economic occurrence by way of placing bets (unfortunately, this
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and prediction, effects of and on about Data Effect, The Ensemble Effect, The Induction Effect, The Persuasion Effect, The Prediction Effect, The Prediction Effect, The prediction markets predictive analytics. See PA (predictive analytics) Predictive Analytics World (PAW) conferences predictive models defined marketing models overlearning and assuming response modeling response uplift modeling univariate
Never Let a Serious Crisis Go to Waste: How Neoliberalism Survived the Financial Meltdown
by Philip Mirowski · 24 Jun 2013 · 662pp · 180,546 words
www.sfgate.com/cgi-bin/article.cgi?file=/c/a/2003/07/29/MN126930.DTL (accessed December 2, 2006) and Justin Wolfers and Eric Zitzowitz, “Prediction Markets in Theory and Practice,” www.dartmouth.edu/~ericz/palgrave.pdf. 108 For unabashed examples of this neoliberal argument, see Litan, “In Defense of Much, but
Expected Returns: An Investor's Guide to Harvesting Market Rewards
by Antti Ilmanen · 4 Apr 2011 · 1,088pp · 228,743 words
Everything Is Obvious: *Once You Know the Answer
by Duncan J. Watts · 28 Mar 2011 · 327pp · 103,336 words
to make reliably. How should they go about making them? MARKETS, CROWDS, AND MODELS One increasingly popular method is to use what is called a prediction market—meaning a market in which buyers and sellers can trade specially designed securities whose prices correspond to the predicted probability that a specific outcome will
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$0.92 for a contract in the Iowa Electronic Markets—one of the longest-running and best-known prediction markets—that would have yielded him or her $1 if Barack Obama had won. Participants in prediction markets therefore behave much like participants in financial markets, buying and selling contracts for whatever price is on
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offer. But in the case of prediction markets, the prices are explicitly interpreted as making a prediction about the outcome in question—for example, the probability of an Obama victory on the eve
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of Election Day was predicted by the Iowa Electronic Markets to be 92 percent. In generating predictions like this one, prediction markets exploit a phenomenon that New Yorker writer James Surowiecki dubbed the “wisdom of crowds”—the notion that although individual people tend to make highly error
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who know something about a particular topic are more likely to participate than people who don’t. What’s so powerful about this feature of prediction markets is that it doesn’t matter who has the relevant market information—a single expert or a large number of nonexperts, or any combination in
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making money in the market would immediately shift the prices to incorporate the new information.3 The potential of prediction markets to tap into collective wisdom has generated a tremendous amount of excitement among professional economists and policy makers alike. Imagine, for example, that a market
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the oil industry before a disaster took place. Possibly the disaster could have been averted. These are the sorts of claims that the proponents of prediction markets tend to make, and it’s easy to see why they’ve generated so much interest. In recent years, in fact
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, prediction markets have been set up to make predictions as varied as the likely success of new products, the box office revenues of upcoming movies, and the
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outcomes of sporting events. In practice, however, prediction markets are more complicated than the theory suggests. In the 2008 presidential election, for example, one of the most popular prediction markets, Intrade, experienced a series of strange fluctuations when an unknown trader started placing very large bets
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which case it’s no longer clear what signal the market is sending.4 Problems like this one have led some skeptics to claim that prediction markets are not necessarily superior to other less sophisticated methods, such as opinion polls, that are harder to manipulate in practice. However, little attention has been
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two polls with the Vegas sports betting market—one of the oldest and most popular betting markets in the world—as well as with another prediction market, TradeSports. And finally, we compared the prediction of both the markets and the polls against two simple statistical models. The first model relied only on
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polls.6 Given how different these methods were, what we found was surprising: All of them performed about the same. To be fair, the two prediction markets performed a little better than the other methods, which is consistent with the theoretical argument above. But the very best performing method—the Las Vegas
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sabermetrics has developed specifically for the purpose of analyzing baseball statistics, even spawning its own journal, the Baseball Research Journal. One might think, therefore, that prediction markets, with their far greater capacity to factor in different sorts of information, would outperform simplistic statistical models by a much wider margin for baseball than
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random events than football games. Since then, we have either found or learned about the same kind of result for other kinds of events that prediction markets have been used to predict, from the opening weekend box office revenues for feature films to the outcomes of presidential elections. Unlike sports, these events
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occur without any of the rules or conditions that are designed to make sports competitive. There is also a lot of relevant information that prediction markets could conceivably exploit to boost their performance well beyond that of a simple model or a poll of relatively uninformed individuals. Yet when we compared
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the Hollywood Stock Exchange (HSX)—one of the most popular prediction markets, which has a reputation for accurate prediction—with a simple statistical model, the HSX did only slightly better.7 And in a separate study of
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not—and take the average. Precisely how you do this, it turns out, may not matter so much. With all their fancy bells and whistles, prediction markets may produce slightly better predictions than a simple method like a poll, but the difference between the two is much less important than the gain
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can do is start using any one of several different methods—or even use all of them together, as we did in our study of prediction markets—and keep track of their performance over time. As I mentioned at the beginning of the previous chapter, keeping track of our predictions is not
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badly underestimated the risk of mortgage defaults and foreclosure rates.14 At first, it might seem that this would have been a perfect application for prediction markets, which might have done a better job of anticipating the crisis than all the “quants” working in the banks. But in fact it would have
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doing it. Planning techniques like scenario analysis and strategic flexibility, which I discussed earlier, can help organizations expose questionable assumptions and avoid obvious mistakes, while prediction markets and polls can exploit the collective intelligence of their employees to evaluate the quality of plans before their outcome is known. Alternatively, crowdsourcing, field experiments
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. Long-Range Forecasting: From Crystal Ball to Computer. New York: John Wiley. Arrow, Kenneth J., Robert Forsythe, Michael Gorham, et al. 2008. “The Promise of Prediction Markets.” Science 320 (5878):877–78. Arthur, W. Brian. 1989. “Competing Technologies, Increasing Returns, and Lock-in by Historical Events.” Economic Journal 99 (394): 116–31
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as Spectroscopy: Automated Discovery of Community Structure Within Organizations.” The Information Society 21(2): 143–153. Tziralis, George, and Ilias Tatsiopoulos. 2006. “Prediction Markets: An Extended Literature Review.” Journal of Prediction Markets 1 (1). Wack, Pierre. 1985a. “Scenarios: Shooting the Rapids.” Harvard Business Review 63 (6):139–50. Wack, Pierre. 1985b. “Scenarios: Uncharted Waters
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Racial Homophily: ERG Models of a Friendship Network Documented on Facebook.” American Journal of Sociology 116 (2):583–642. Wolfers, Justin, and Eric Zitzewitz. 2004. “Prediction Markets.” Journal of Economic Perspectives 18 (2):107–26. Wortman, Jenna. 2010. “Once Just a Site with Funny Cat Pictures, and Now a Web Empire.” New
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major tournaments. 2. See Ayres (2008) for details. See also Baker (2009) and Mauboussin (2009) for more examples of supercrunching. 3. For more details on prediction markets, see Arrow et al. (2008), Wolfers and Zitzewitz (2004), Tziralis and Tatsiopoulos (2006), and Sunstein (2005). See also Surowiecki (2004) for a more general overview
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. 5. In a recent blog post, Ian Ayres (author of Supercrunchers) calls the relative performance of prediction markets “one of the great unresolved questions of predictive analytics” (http://freakonomics.blogs.nytimes.com/2009/12/23/prediction-markets-vs-super-crunching-which-can-better-predict-how-justice-kennedy-will-vote/). 6. To be precise, we
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week before it opened. See Goel, Reeves, et al. (2010) for details. See Sunstein (2005) for more details on the Hollywood Stock Exchange and other prediction markets. 8. See Erikson and Wlezien (2008) for details of their comparison between opinion polls and the Iowa Electronic Markets. 9. Ironically, the problem with experts
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Reinventing Capitalism in the Age of Big Data
by Viktor Mayer-Schönberger and Thomas Ramge · 27 Feb 2018 · 267pp · 72,552 words
Future Files: A Brief History of the Next 50 Years
by Richard Watson · 1 Jan 2008
The Transparent Society: Will Technology Force Us to Choose Between Privacy and Freedom?
by David Brin · 1 Jan 1998 · 205pp · 18,208 words
The Bitcoin Guidebook: How to Obtain, Invest, and Spend the World's First Decentralized Cryptocurrency
by Ian Demartino · 2 Feb 2016 · 296pp · 86,610 words
Automate This: How Algorithms Came to Rule Our World
by Christopher Steiner · 29 Aug 2012 · 317pp · 84,400 words
The Smartest Investment Book You'll Ever Read: The Simple, Stress-Free Way to Reach Your Investment Goals
by Daniel R. Solin · 7 Nov 2006
Beautiful Data: The Stories Behind Elegant Data Solutions
by Toby Segaran and Jeff Hammerbacher · 1 Jul 2009
Trend Commandments: Trading for Exceptional Returns
by Michael W. Covel · 14 Jun 2011
The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes, and Real Money
by Steven Drobny · 18 Mar 2010 · 537pp · 144,318 words
Plenitude: The New Economics of True Wealth
by Juliet B. Schor · 12 May 2010 · 309pp · 78,361 words
Hive Mind: How Your Nation’s IQ Matters So Much More Than Your Own
by Garett Jones · 15 Feb 2015 · 247pp · 64,986 words
Finding Alphas: A Quantitative Approach to Building Trading Strategies
by Igor Tulchinsky · 30 Sep 2019 · 321pp
The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution
by Gregory Zuckerman · 5 Nov 2019 · 407pp · 104,622 words
How I Became a Quant: Insights From 25 of Wall Street's Elite
by Richard R. Lindsey and Barry Schachter · 30 Jun 2007
The Permanent Portfolio
by Craig Rowland and J. M. Lawson · 27 Aug 2012
The Jobs to Be Done Playbook: Align Your Markets, Organization, and Strategy Around Customer Needs
by Jim Kalbach · 6 Apr 2020
Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond: The Innovative Investor's Guide to Bitcoin and Beyond
by Chris Burniske and Jack Tatar · 19 Oct 2017 · 416pp · 106,532 words
Computer: A History of the Information Machine
by Martin Campbell-Kelly and Nathan Ensmenger · 29 Jul 2013 · 528pp · 146,459 words
Trees on Mars: Our Obsession With the Future
by Hal Niedzviecki · 15 Mar 2015 · 343pp · 102,846 words
Commodore: A Company on the Edge
by Brian Bagnall · 13 Sep 2005 · 781pp · 226,928 words
The Deep Learning Revolution (The MIT Press)
by Terrence J. Sejnowski · 27 Sep 2018
Terms of Service: Social Media and the Price of Constant Connection
by Jacob Silverman · 17 Mar 2015 · 527pp · 147,690 words
One Up on Wall Street
by Peter Lynch · 11 May 2012
The Intelligent Asset Allocator: How to Build Your Portfolio to Maximize Returns and Minimize Risk
by William J. Bernstein · 12 Oct 2000
Thinking in Bets
by Annie Duke · 6 Feb 2018 · 288pp · 81,253 words
Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market
by Scott Patterson · 11 Jun 2012 · 356pp · 105,533 words
Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It)
by Salim Ismail and Yuri van Geest · 17 Oct 2014 · 292pp · 85,151 words
Dragnet Nation: A Quest for Privacy, Security, and Freedom in a World of Relentless Surveillance
by Julia Angwin · 25 Feb 2014 · 422pp · 104,457 words
Flash Boys: Not So Fast: An Insider's Perspective on High-Frequency Trading
by Peter Kovac · 10 Dec 2014 · 200pp · 54,897 words
The Future Is Asian
by Parag Khanna · 5 Feb 2019 · 496pp · 131,938 words
The Fund: Ray Dalio, Bridgewater Associates, and the Unraveling of a Wall Street Legend
by Rob Copeland · 7 Nov 2023 · 412pp · 122,655 words
A New History of the Future in 100 Objects: A Fiction
by Adrian Hon · 5 Oct 2020 · 340pp · 101,675 words
In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence
by George Zarkadakis · 7 Mar 2016 · 405pp · 117,219 words
New Market Wizards: Conversations With America's Top Traders
by Jack D. Schwager · 28 Jan 1994 · 512pp · 162,977 words
In the Plex: How Google Thinks, Works, and Shapes Our Lives
by Steven Levy · 12 Apr 2011 · 666pp · 181,495 words
Here Comes Everybody: The Power of Organizing Without Organizations
by Clay Shirky · 28 Feb 2008 · 313pp · 95,077 words
Programming Collective Intelligence
by Toby Segaran · 17 Dec 2008 · 519pp · 102,669 words
The People vs Tech: How the Internet Is Killing Democracy (And How We Save It)
by Jamie Bartlett · 4 Apr 2018 · 170pp · 49,193 words
The Rise of the Network Society
by Manuel Castells · 31 Aug 1996 · 843pp · 223,858 words
I Will Teach You To Be Rich
by Sethi, Ramit · 22 Mar 2009 · 357pp · 91,331 words
The Techno-Human Condition
by Braden R. Allenby and Daniel R. Sarewitz · 15 Feb 2011
The Age of Cryptocurrency: How Bitcoin and Digital Money Are Challenging the Global Economic Order
by Paul Vigna and Michael J. Casey · 27 Jan 2015 · 457pp · 128,838 words
Accessory to War: The Unspoken Alliance Between Astrophysics and the Military
by Neil Degrasse Tyson and Avis Lang · 10 Sep 2018 · 745pp · 207,187 words
Making Sense of Chaos: A Better Economics for a Better World
by J. Doyne Farmer · 24 Apr 2024 · 406pp · 114,438 words
The Internet of Us: Knowing More and Understanding Less in the Age of Big Data
by Michael P. Lynch · 21 Mar 2016 · 230pp · 61,702 words
Think Twice: Harnessing the Power of Counterintuition
by Michael J. Mauboussin · 6 Nov 2012 · 256pp · 60,620 words
Superforecasting: The Art and Science of Prediction
by Philip Tetlock and Dan Gardner · 14 Sep 2015 · 317pp · 100,414 words
Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum & Smart Contracts
by David Gerard · 23 Jul 2017 · 309pp · 54,839 words
The Lights in the Tunnel
by Martin Ford · 28 May 2011 · 261pp · 10,785 words
The Meritocracy Myth
by Stephen J. McNamee · 17 Jul 2013 · 440pp · 108,137 words
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
by Cathy O'Neil · 5 Sep 2016 · 252pp · 72,473 words
Economic Gangsters: Corruption, Violence, and the Poverty of Nations
by Raymond Fisman and Edward Miguel · 14 Apr 2008
The Economics Anti-Textbook: A Critical Thinker's Guide to Microeconomics
by Rod Hill and Anthony Myatt · 15 Mar 2010
Griftopia: Bubble Machines, Vampire Squids, and the Long Con That Is Breaking America
by Matt Taibbi · 15 Feb 2010 · 291pp · 91,783 words
Alpha Trader
by Brent Donnelly · 11 May 2021
Python for Algorithmic Trading: From Idea to Cloud Deployment
by Yves Hilpisch · 8 Dec 2020 · 1,082pp · 87,792 words
Uncharted: How to Map the Future
by Margaret Heffernan · 20 Feb 2020 · 335pp · 97,468 words
In Pursuit of the Perfect Portfolio: The Stories, Voices, and Key Insights of the Pioneers Who Shaped the Way We Invest
by Andrew W. Lo and Stephen R. Foerster · 16 Aug 2021 · 542pp · 145,022 words
Beyond Diversification: What Every Investor Needs to Know About Asset Allocation
by Sebastien Page · 4 Nov 2020 · 367pp · 97,136 words
Mastering Blockchain, Second Edition
by Imran Bashir · 28 Mar 2018
The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy
by Sharon Bertsch McGrayne · 16 May 2011 · 561pp · 120,899 words
What Would Google Do?
by Jeff Jarvis · 15 Feb 2009 · 299pp · 91,839 words
Investing Demystified: How to Invest Without Speculation and Sleepless Nights
by Lars Kroijer · 5 Sep 2013 · 300pp · 77,787 words
To Serve God and Wal-Mart: The Making of Christian Free Enterprise
by Bethany Moreton · 15 May 2009 · 391pp · 22,799 words
Algorithms to Live By: The Computer Science of Human Decisions
by Brian Christian and Tom Griffiths · 4 Apr 2016 · 523pp · 143,139 words
Traders at Work: How the World's Most Successful Traders Make Their Living in the Markets
by Tim Bourquin and Nicholas Mango · 26 Dec 2012 · 327pp · 91,351 words
Rationality: From AI to Zombies
by Eliezer Yudkowsky · 11 Mar 2015 · 1,737pp · 491,616 words
Super Thinking: The Big Book of Mental Models
by Gabriel Weinberg and Lauren McCann · 17 Jun 2019
Global Catastrophic Risks
by Nick Bostrom and Milan M. Cirkovic · 2 Jul 2008
The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis
by James Rickards · 15 Nov 2016 · 354pp · 105,322 words
What Money Can't Buy: The Moral Limits of Markets
by Michael Sandel · 26 Apr 2012 · 231pp · 70,274 words
Mastering Ethereum: Building Smart Contracts and DApps
by Andreas M. Antonopoulos and Gavin Wood Ph. D. · 23 Dec 2018 · 960pp · 125,049 words
The Cryptopians: Idealism, Greed, Lies, and the Making of the First Big Cryptocurrency Craze
by Laura Shin · 22 Feb 2022 · 506pp · 151,753 words
When Free Markets Fail: Saving the Market When It Can't Save Itself (Wiley Corporate F&A)
by Scott McCleskey · 10 Mar 2011
Digital Bank: Strategies for Launching or Becoming a Digital Bank
by Chris Skinner · 27 Aug 2013 · 329pp · 95,309 words
Think Like a Freak
by Steven D. Levitt and Stephen J. Dubner · 11 May 2014 · 240pp · 65,363 words
Superintelligence: Paths, Dangers, Strategies
by Nick Bostrom · 3 Jun 2014 · 574pp · 164,509 words
The Economic Singularity: Artificial Intelligence and the Death of Capitalism
by Calum Chace · 17 Jul 2016 · 477pp · 75,408 words
The Great Fragmentation: And Why the Future of All Business Is Small
by Steve Sammartino · 25 Jun 2014 · 247pp · 81,135 words
Loonshots: How to Nurture the Crazy Ideas That Win Wars, Cure Diseases, and Transform Industries
by Safi Bahcall · 19 Mar 2019 · 393pp · 115,217 words
#Republic: Divided Democracy in the Age of Social Media
by Cass R. Sunstein · 7 Mar 2017 · 437pp · 105,934 words
The Disciplined Trader: Developing Winning Attitudes
by Mark Douglas · 1 Jan 1990
The Bank That Lived a Little: Barclays in the Age of the Very Free Market
by Philip Augar · 4 Jul 2018 · 457pp · 143,967 words
Range: Why Generalists Triumph in a Specialized World
by David Epstein · 1 Mar 2019 · 406pp · 109,794 words
Before Babylon, Beyond Bitcoin: From Money That We Understand to Money That Understands Us (Perspectives)
by David Birch · 14 Jun 2017 · 275pp · 84,980 words
The Volatility Smile
by Emanuel Derman,Michael B.Miller · 6 Sep 2016
Mastering Blockchain: Unlocking the Power of Cryptocurrencies and Smart Contracts
by Lorne Lantz and Daniel Cawrey · 8 Dec 2020 · 434pp · 77,974 words
The Infinite Machine: How an Army of Crypto-Hackers Is Building the Next Internet With Ethereum
by Camila Russo · 13 Jul 2020 · 349pp · 102,827 words
The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology
by William Mougayar · 25 Apr 2016 · 161pp · 44,488 words
Ctrl Alt Delete: Reboot Your Business. Reboot Your Life. Your Future Depends on It.
by Mitch Joel · 20 May 2013 · 260pp · 76,223 words
The Knowledge Illusion
by Steven Sloman · 10 Feb 2017 · 313pp · 91,098 words
Kings of Crypto: One Startup's Quest to Take Cryptocurrency Out of Silicon Valley and Onto Wall Street
by Jeff John Roberts · 15 Dec 2020 · 226pp · 65,516 words
Windfall: The Booming Business of Global Warming
by Mckenzie Funk · 22 Jan 2014 · 337pp · 101,281 words
The Handbook of Personal Wealth Management
by Reuvid, Jonathan. · 30 Oct 2011
Starstruck: The Business of Celebrity
by Currid · 9 Nov 2010 · 332pp · 91,780 words
The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future
by Keach Hagey · 19 May 2025 · 439pp · 125,379 words
Easy Money: Cryptocurrency, Casino Capitalism, and the Golden Age of Fraud
by Ben McKenzie and Jacob Silverman · 17 Jul 2023 · 329pp · 99,504 words
Pax Technica: How the Internet of Things May Set Us Free or Lock Us Up
by Philip N. Howard · 27 Apr 2015 · 322pp · 84,752 words
The Myth of Capitalism: Monopolies and the Death of Competition
by Jonathan Tepper · 20 Nov 2018 · 417pp · 97,577 words
This Is Not Normal: The Collapse of Liberal Britain
by William Davies · 28 Sep 2020 · 210pp · 65,833 words
In Defense of Global Capitalism
by Johan Norberg · 1 Jan 2001 · 233pp · 75,712 words
Number Go Up: Inside Crypto's Wild Rise and Staggering Fall
by Zeke Faux · 11 Sep 2023 · 385pp · 106,848 words
Care: The Highest Stage of Capitalism
by Premilla Nadasen · 10 Oct 2023 · 288pp · 82,972 words
Free culture: how big media uses technology and the law to lock down culture and control creativity
by Lawrence Lessig · 15 Nov 2004 · 297pp · 103,910 words
Eat People: And Other Unapologetic Rules for Game-Changing Entrepreneurs
by Andy Kessler · 1 Feb 2011 · 272pp · 64,626 words
Confessions of a Crypto Millionaire: My Unlikely Escape From Corporate America
by Dan Conway · 8 Sep 2019 · 218pp · 68,648 words
Stake Hodler Capitalism: Blockchain and DeFi
by Amr Hazem Wahba Metwaly · 21 Mar 2021 · 80pp · 21,077 words
The Mysterious Mr. Nakamoto: A Fifteen-Year Quest to Unmask the Secret Genius Behind Crypto
by Benjamin Wallace · 18 Mar 2025 · 431pp · 116,274 words
Bleeding Edge: A Novel
by Thomas Pynchon · 16 Sep 2013 · 532pp · 141,574 words
Know Thyself
by Stephen M Fleming · 27 Apr 2021
DeFi and the Future of Finance
by Campbell R. Harvey, Ashwin Ramachandran, Joey Santoro, Vitalik Buterin and Fred Ehrsam · 23 Aug 2021 · 179pp · 42,081 words
The Truth Machine: The Blockchain and the Future of Everything
by Paul Vigna and Michael J. Casey · 27 Feb 2018 · 348pp · 97,277 words
Bitcoin: The Future of Money?
by Dominic Frisby · 1 Nov 2014 · 233pp · 66,446 words
How I Invest My Money: Finance Experts Reveal How They Save, Spend, and Invest
by Brian Portnoy and Joshua Brown · 17 Nov 2020 · 149pp · 43,747 words
How to DeFi
by Coingecko, Darren Lau, Sze Jin Teh, Kristian Kho, Erina Azmi, Tm Lee and Bobby Ong · 22 Mar 2020 · 135pp · 26,407 words
Collaborative Futures
by Mike Linksvayer, Michael Mandiberg and Mushon Zer-Aviv · 24 Aug 2010 · 188pp · 9,226 words