hindsight bias

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

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After a two-week interval, 67 percent of the students remembered their predictions as being more accurate than they actually were. In other words, they were unable to recall their prior uncertainty. After several months, the percentage of students afflicted with hindsight bias jumped from 67 percent to 84 percent. The hindsight bias has been found to operate powerfully in trial testimony. Witnesses believe they are giving accurate accounts, but their recall of the order of events and of specific details are altered by knowing how matters actually turned out.53 The hindsight bias infects historical accounts. Historians, having the benefit of hindsight, will often point out that the rise of the Third Reich was quite predictable. They claim that the seeds of Nazism were obvious in various writings that preceded the Third Reich.

Other experimental evidence shows that strategies aimed specifically at reducing the hindsight bias are not effective.54 Even when people are warned about hindsight bias and told to avoid it, it still occurs. It appears to be beyond rational control. Not even professional expertise is helpful. In one study, a group of doctors were asked to evaluate the diagnostic errors made by other doctors. The doctors doing the evaluations were armed with the knowledge of the disease that was ultimately confirmed by a pathology report. The evaluators were unable to understand how such errors could have been made by a trained physician. Again, outcome knowledge makes the past appear as if it should have been more predictable than it really was. What cognitive processes are responsible for hindsight bias? Though The Illusory Validity of Subjective Technical Analysis 57 the matter is not settled, it seems to go beyond a desire to see ourselves as smart and in control.

The various methods used become familiar as they are studied and used regularly. The fifth factor, early success, is a matter of chance. Some will experience initial success and because of the self-attribution bias are likely to attribute it to their expertise and the efficacy of the TA methods rather than chance. All of these factors can induce and maintain an unjustified sense of control and an ability to earn market-beating returns. The Hindsight Bias: I Knew Things Would Turn Out That Way The hindsight bias creates the illusion that the prediction of an uncertain event is easier than it really is when the event is viewed in retrospect, after its outcome is known. Once we learn the upshot of an uncertain situation, such as which team won a football game or in which direction prices 51 The Illusory Validity of Subjective Technical Analysis moved, subsequent to a TA pattern, we tend to forget how uncertain we really were prior to knowing the outcome.

 

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Nothing to Hide: The False Tradeoff Between Privacy and Security by Daniel J. Solove

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Suppose the police do a dragnet search for drugs, but they don’t find any in your house. During the search, they find out about your religious or political beliefs, and they don’t like them. They also discover you’ve been betting on sports. They might arrest you for the illegal gambling as a pretext—just because they despise your beliefs. Hindsight Bias The timing of the warrant is crucial. It must be obtained before the government conducts the search. Why? The primary reason is hindsight bias. Suppose the police illegally search the home of a suspected terrorist and find various weapons. What judge is going to throw that evidence out because the police merely had a hunch when they did the search? Knowing the hunch turned out to be correct makes it very hard to question its validity. This is why warrants are issued in advance.

In many cases, warrants aren’t inconsistent with the prevention of crime. And in circumstances where warrants truly are impractical, we must do more than just shove them aside; we must ensure that their key functions are achieved by other means. Why Require Warrants Supported by Probable Cause? Warrants supported by probable cause serve at least three critical functions. They limit police power and discretion, they restrict dragnet searches, and they prevent hindsight bias. Police Power and Discretion Warrants require a neutral and detached judge to decide whether a search is justified. They restrain police power. The police have a tremendous amount of discretion about when, where, and 126 The Suspicionless-Searches Argument how to search. They can enter your home, search through your things and your computer. They can arrest you and search your body. Warrants prevent law-enforcement officials from doing these things at their mere whim, for entertainment, because they harbor personal animus toward you, because they’re prejudiced against your race, religion, or ethnicity, because they don’t like your beliefs or what you say, or because they don’t like things you’ve done, or your career, or people you’re friendly with.

This is why warrants are issued in advance. The court knows what the police know. A warrant is kind of like a gamble. The police are saying there’s a decent likelihood they’ll find evidence of a crime, and the judge determines whether the odds are sufficiently good. Nobody knows yet how the bet will pan out. It’s very hard to make the same unbiased call when you know what happened. In psychology, hindsight bias is a well-recognized occurrence. It is sometimes referred to as the “I knew it all along” phenomenon. Countless studies have confirmed it. In a 1991 study, people were asked to predict whether Clarence Thomas would be confirmed to become a justice on the U.S. Supreme Court. Before the Senate vote, 58 percent predicted he’d be confirmed. After he was confirmed, 78 percent claimed to have thought beforehand that he would be confirmed.16 In another study, people were told about a train with toxic chemicals about to embark on a treacherous route through the mountains.

 

pages: 263 words: 75,455

Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors by Wesley R. Gray, Tobias E. Carlisle

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The two pioneers of the field of behavioral finance, Daniel Kahneman and Amos Tversky, suggest that our overconfidence may stem from two other biases, self-attribution bias and hindsight bias.34 Self-attribution bias refers to our propensity to ascribe our successes to our skill, while blaming our failures on bad luck, rather than a lack of skill. For example, the stocks we buy that go up show our great stock picking skills, while those we buy that go down do so because of some outside factor, like Congress changing the law or the Federal Reserve increasing interest rates. If we do it often enough, we are led to the conclusion that we are skillful, which is as pleasant as it is wrong. Hindsight bias is the propensity to believe, after an event has occurred, that we predicted it before it happened. If, after watching some unlikely event unfold, you've ever said, “I knew that would happen,” when your reason for saying so was just some gut-feeling, you were subject to hindsight bias.

If, after watching some unlikely event unfold, you've ever said, “I knew that would happen,” when your reason for saying so was just some gut-feeling, you were subject to hindsight bias. The problem with hindsight bias is that if we think we predicted the past better than we actually did, we tend to believe that we can predict the future better than we actually can. A related bias is neglect of the base case. The bias manifests when we try to answer probabilistic questions like, “What is the probability that object A originates from class B?” or “What is the probability that process A will generate outcome B?” The neglect-of-the-base-case bias is caused by a heuristic called representativeness. It is called the representativeness heuristic because we answer the questions by determining how much A represents—or resembles—B, rather than determining the likelihood of A given B.

Availability bias Ayres, Ian Bachelier, Louis Bailey, Morris Bankruptcy prediction history of improving Batchelor, Roy Beat the Dealer (Thorp) Beat the Market: A Scientific Stock Market System (Thorp & Kassouf) Behavioral errors, quantitative investing's protection against cognitive biases experts' errors value investors'errors Behavioral Investing: A Practitioners Guide to Applying Behavioral Finance (Montier) Benchmarking Beneish, Messod Berk, Jonathan Bogue, Marcus Bonaime, Alice Book value-to-market capitalization ratio Brooks, Chris Buffett, Warren See's Candies acquisition Buybacks Campbell, John Cash flow on assets (CFOA) CGM Focus Fund Chava, Sudheer “The Checklist” (Gawande) The Checklist Manifesto: How to Get Things Right (Gawande) Chuvakhin, Nikolai Cloning Cognitive biases adjustment bias anchoring availability bias hindsight bias neglect of the base case overconfidence self-attribution bias Confirmation bias “Contrarian Investment, Extrapolation, and Risk” (Lakonishok, Schleifer, & Vishny) Cowles, Alfred, III “The Cross-Section of Expected Stock Returns” (Fama & French) Data mining “Decoding Inside Information” (Cohen, Malloy, & Pomorski) “Delisting Returns and Their Effect on Accounting-Based Market Anomalies” (Price, Beaver, & McNichols) Dumb money, paradox of behavioral errors, quantitative investing's protection against cognitive biases experts' errors value investors'errors quantitative value investing, power of value strategies Graham's quantitative Earnings manipulators and frauds, eliminating accruals detecting earnings manipulation PROBMs, predicting Enron Earnings yield Efficient market theory Einhorn, David Enron Enterprise yield (EBITDA and EBIT variations) Expert Political Judgment (Tetlock) Fama, Eugene Financial distress, measuring risk of bankruptcy prediction history of improving calculating universe, scrubbing Financial strength case study: Lubrizol Corporation comparing performance of F_SCORE and FS_SCORE financial strength score (FS_SCORE) current profitability formula and interpretation recent operational improvements stability Piotroski Fundamental Score (F_SCORE) analyzing formula and interpretation Fooled by Randomness (Taleb) Forward earnings estimate Franchises finding economic moats and excess returns persistence pricing power and big, stable margins See's Candies, acquisition by Buffett Fraud.

 

pages: 317 words: 100,414

Superforecasting: The Art and Science of Prediction by Philip Tetlock, Dan Gardner

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The second big barrier to feedback is time lag. When forecasts span months or years, the wait for a result allows the flaws of memory to creep in. You know how you feel now about the future. But as events unfold, will you be able to recall your forecast accurately? There is a good chance you won’t. Not only will you have to contend with ordinary forgetfulness, you are likely to be afflicted by what psychologists call hindsight bias. If you are old enough now to have been a sentient being in 1991, answer this question: Back then, how likely did you think it was that the incumbent president, George H. W. Bush (now known as Bush 41) would win reelection in 1992? We all know Bush 41 lost to Bill Clinton, but you may recall that he was popular after the victory in the Gulf War. So perhaps you thought his chances were pretty good, but, obviously, he also stood a pretty good chance of losing.

In this debate, each candidate heaps praise on his opponents while savaging himself—because Bush 41 was certain to crush whomever he faced. Everyone knew that. It’s why leading Democrats didn’t contest the nomination that year, clearing the way for the obscure governor of Arkansas, Bill Clinton. Once we know the outcome of something, that knowledge skews our perception of what we thought before we knew the outcome: that’s hindsight bias. Baruch Fischhoff was the first to document the phenomenon in a set of elegant experiments. One had people estimate the likelihood of major world events at the time of Fischhoff’s research—Will Nixon personally meet with Mao?—then recall their estimate after the event did or did not happen. Knowing the outcome consistently slanted the estimate, even when people tried not to let it sway their judgment.

So in 1992–93 I returned to the experts, reminded them of the question in 1988, and asked them to recall their estimates. On average, the experts recalled a number 31 percentage points higher than the correct figure. So an expert who thought there was only a 10% chance might remember herself thinking there was a 40% or 50% chance. There was even a case in which an expert who pegged the probability at 20% recalled it as 70%—which illustrates why hindsight bias is sometimes known as the “I knew it all along” effect. Forecasters who use ambiguous language and rely on flawed memories to retrieve old forecasts don’t get clear feedback, which makes it impossible to learn from experience. They are like basketball players doing free throws in the dark. The only feedback they get are sounds—the clang of the ball hitting metal, the thunk of the ball hitting the backboard, the swish of the ball brushing against the net.

 

pages: 327 words: 103,336

Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

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This tendency, which psychologists call creeping determinism, is related to the better-known phenomenon of hindsight bias, the after-the-fact tendency to think that we “knew it all along.” In a variety of lab experiments, psychologists have asked participants to make predictions about future events and then reinterviewed them after the events in question had taken place. When recalling their previous predictions, subjects consistently report being more certain of their correct predictions, and less certain of their incorrect predictions, than they had reported at the time they made them. Creeping determinism, however, is subtly different from hindsight bias and even more deceptive. Hindsight bias, it turns out, can be counteracted by reminding people of what they said before they knew the answer or by forcing them to keep records of their predictions.

But even when we recall perfectly accurately how uncertain we were about the way events would transpire—even when we concede to have been caught completely by surprise—we still have a tendency to treat the realized outcome as inevitable. Ahead of time, for example, it might have seemed that the surge was just as likely to have had no effect as to lead to a drop in violence. But once we know that the drop in violence is what actually happened, it doesn’t matter whether or not we knew all along that it was going to happen (hindsight bias). We still believe that it was going to happen, because it did.3 SAMPLING BIAS Creeping determinism means that we pay less attention than we should to the things that don’t happen. But we also pay too little attention to most of what does happen. We notice when we just miss the train, but not all the times when it arrives shortly after we do. We notice when we unexpectedly run into an acquaintance at the airport, but not all the times when we do not.

The Matthew Effect: How Advantage Begets Further Advantage. New York: Columbia University Press. Robbins, Jordan M., and Joachim I. Krueger. 2005. “Social Projection to Ingroups and Outgroups: A Review and Meta-analysis.” Personality and Social Psychology Review 9:32–47. Rogers, Everett M. 1995. Diffusion of Innovations, 4th ed. New York: Free Press. Roese, Neal J., and James M. Olson. 1996. “Counterfactuals, Causal Attributions, and the Hindsight Bias: A Conceptual Integration.” Journal of Experimental Social Psychology 32 (3):197–227. Rosen, Emmanuel. 2000. The Anatomy of Buzz: How to Create Word-of-Mouth Marketing. New York: Doubleday. Rosenbloom, Stephanie. 2009. “Retailers See Slowing Sales in Key Season.” New York Times, Aug. 15. Rosenzweig, Phil. 2007. The Halo Effect. New York: Free Press. Rothschild, David, and Justin Wolfers. 2008.

 

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Misbehaving: The Making of Behavioral Economics by Richard H. Thaler

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As we drove, Fisch-hoff told me he had completed a PhD in psychology at the Hebrew University in Israel. There he had worked with two guys whose names I had never heard: Daniel Kahneman and Amos Tversky. Baruch told me about his now-famous thesis on “hindsight bias.” The finding is that, after the fact, we think that we always knew the outcome was likely, if not a foregone conclusion. After the virtually unknown African American senator Barack Obama defeated the heavily favored Hillary Clinton for the Democratic Party presidential nomination, many people thought they had seen it coming. They hadn’t. They were just misremembering. I found the concept of hindsight bias fascinating, and incredibly important to management. One of the toughest problems a CEO faces is convincing managers that they should take on risky projects if the expected gains are high enough.

One of the toughest problems a CEO faces is convincing managers that they should take on risky projects if the expected gains are high enough. Their managers worry, for good reason, that if the project works out badly, the manager who championed the project will be blamed whether or not the decision was a good one at the time. Hindsight bias greatly exacerbates this problem, because the CEO will wrongly think that whatever was the cause of the failure, it should have been anticipated in advance. And, with the benefit of hindsight, he always knew this project was a poor risk. What makes the bias particularly pernicious is that we all recognize this bias in others but not in ourselves. Baruch suggested that I might enjoy reading some of the work of his advisors. The next day, when I was back in my office in Rochester, I headed over to the library. Having spent all my time in the economics section, I found myself in a new part of the library.

Although this depiction is often apt, in many cases the real culprit is the boss, not the worker. In order to get managers to be willing to take risks, it is necessary to create an environment in which those managers will be rewarded for decisions that were value-maximizing ex ante, that is, with information available at the time they were made, even if they turn out to lose money ex post. Implementing such a policy is made difficult by hindsight bias. Whenever there is a time lapse between the times when a decision is made and when the results come in, the boss may have trouble remembering that he originally thought it was a good idea too. The bottom line is that in many situations in which agents are making poor choices, the person who is misbehaving is often the principal, not the agent. The misbehavior is in failing to create an environment in which employees feel that they can take good risks and not be punished if the risks fail to pay off.

 

pages: 111 words: 1

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

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If they were made for us to understand things, then we would have a machine in it that would run the past history as in a VCR, with a correct chronology, and it would slow us down so much that we would have trouble operating. Psychologists call this overestimation of what one knew at the time of the event due to subsequent information the hindsight bias, the “I knew it all along” effect. Now the civil servant called the trades that ended up as losers “gross mistakes,” just like journalists call decisions that end up costing a candidate his election a “mistake.” I will repeat this point until I get hoarse: A mistake is not something to be determined after the fact, but in the light of the information until that point. A more vicious effect of such hindsight bias is that those who are very good at predicting the past will think of themselves as good at predicting the future, and feel confident about their ability to do so. This is why events like those of September 11, 2001, never teach us that we live in a world where important events are not predictable—even the Twin Towers’ collapse appears to have been predictable then.

Another phenomenon: the transformation of the author by his own book. As I increasingly started living this book after the initial composition, I found luck in the most unexpected of places. It is as if there were two planets: the one in which we actually live and the one, considerably more deterministic, on which people are convinced we live. It is as simple as that: Past events will always look less random than they were (it is called the hindsight bias). I would listen to someone’s discussion of his own past realizing that much of what he was saying was just backfit explanations concocted ex post by his deluded mind. This became at times unbearable: I could feel myself looking at people in the social sciences (particularly conventional economics) and the investment world as if they were deranged subjects. Living in the real world may be painful particularly if one finds statements more informative about the people making them than the intended message: I picked up Newsweek this morning at the dentist’s office and read a journalist’s discussion of a prominent business figure, particularly his ability in “timing moves” and realized how I was making a list of the biases in the journalist’s mind rather than getting the intended information in the article itself, which I could not possibly take seriously.

For a long time we traders were totally ignorant of the behavioral research and saw situations where there was with strange regularity a wedge between the simple probabilistic reasoning and people’s perception of things. We gave them names such as the “I’m as good as my last trade” effect, the “sound-bite effect,” the “Monday morning quarterback” heuristic, and the “It was obvious after the fact” effect. It was both vindicating for traders’ pride and disappointing to discover that they existed in the heuristics literature as the “anchoring,” the “affect heuristic,” and the “hindsight bias” (it makes us feel that trading is true, experimental scientific research). The correspondence between the two worlds is shown in Table 11.1. I start with the “I’m as good as my last trade” heuristic (or the “loss of perspective” bias)—the fact that the counter is reset at zero and you start a new day or month from scratch, whether it is your accountant who does it or your own mind. This is the most significant distortion and the one that carries the most consequences.

 

Infotopia: How Many Minds Produce Knowledge by Cass R. Sunstein

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When asked what percentage of other people watch television on Saturday night, enjoy Bob Dylan, favor a particular political party, or believe that the latest Brad Pitt movie will win the Oscar, most of us show a bias in the direction that we ourselves favor. But in groups with diverse views, people quickly learn that their own position is not universally held, and hence the bias is reduced. In these cases, group deliberation supplies an important corrective. Or consider the hindsight bias: people’s tendency to believe, falsely but with the benefit of hindsight, that they would have accurately predicted the outcome of an event (an accident, a natural disaster, an illness, a change in the stock market). Compared to individuals, groups are slightly less susceptible to hindsight bias.18 Apparently, group members who are not susceptible to that bias are able to persuade others that it is indeed a bias. But the broader point is that with group discussion, individual errors are usually propagated rather than eliminated, and amplification of mistakes is quite likely.

., “Bias in Judgment,” 691. 14. Ibid., 692 (citing studies). 15. Ibid. 16. Stasser and Dietz-Uhler, “Collective Choice, Judgment, and Problem Solving,” 48. 242 / Notes to Pages 76–79 17. Personal communication with Reid Hastie, University of Chicago Business School (July 24, 2004), who has conducted experiments on this issue for many years. 18. See generally Dagmar Stahlberg et al., “We Knew It All Along: Hindsight Bias in Groups,” Organizational Behavior and Human Decision Processes 63 (1995): 46. 19. MacCoun, “Comparing Micro and Macro Rationality,” 124 (emphasis omitted). 20. See Stasser and Titus, “Hidden Profiles,” 304, 306–13 (discussing hidden profile experiments). 21. Daniel Gigone and Reid Hastie, “The Common Knowledge Effect: Information Sharing and Group Judgments,” Journal of Personality and Social Psychology 65 (1993): 971–73 (explaining hidden profiles by reference to common knowledge effect). 22.

See statistical groups team players, 201, 204 See also deliberation; social pressures groupthink, 12–13, 67, 223–24 Guantánamo Bay detainees, 6 Gulick, Luther, 202–3 Guthrie, Woody, 164, 166 Habermas, Jürgen, 11, 49, 71–72 hackers, 170, 173 Hanson, Robin, 104 Hargittai, Eszter, 190 Hayek, Friedrich, 216, 224 blogs and, 186, 196 dispersed information and, 118– 21, 130, 137 prediction markets and, 17, 118– 27, 130, 135, 137, 141 price system theory of, 14–15, 17, 120–21, 126, 127, 129, 130, 135, 137, 159, 173, 186, 197, 221 Wikipedia project and, 156–57, 159 herd behavior, 129, 141, 224 heuristics, 75–79 Hewitt, Hugh, 182, 183–84 Hewlett-Packard, 112–13, 117, 131, 173 Hicks, Angie, 192 hidden profiles, 124, 163, 224 blogs and, 186, 223 deliberation and, 17, 81–88, 100– 101, 102, 203, 204, 205, 210, 212 hindsight bias, 80 Index / 265 Hollywood Stock Exchange, 111–12 Homeland Security Department, U.S., 214 homogeneity, deliberative group, 46, 55 Hooke, Robert, 217 horse races, 112, 139–40 House of Representatives, U.S., 27 HP. See Hewlett-Packard HSX (Hollywood Stock Exchange), 111–12 hurricane futures market, 118 Hurricane Katrina, 76 Hussein, Saddam, 29 IBM, 173 “ideal speech situation,” 72 identity, group-related, 65, 79, 95– 96 IEM.

 

pages: 654 words: 191,864

Thinking, Fast and Slow by Daniel Kahneman

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This task turns out to be surprisingly difficult. Asked to reconstruct their former beliefs, people retrieve their current ones instead—an instance of substitution—and many cannot believe that they ever felt differently. Your inability to reconstruct past beliefs will inevitably cause you to underestimate the extent to which you were surprised by past events. Baruch Fischh off first demonstrated this “I-knew-it-all-along” effect, or hindsight bias, when he was a student in Jerusalem. Together with Ruth Beyth (another of our students), Fischh off conducted a survey before President Richard Nixon visited China and Russia in 1972. The respondents assigned probabilities to fifteen possible outcomes of Nixon’s diplomatic initiatives. Would Mao Zedong agree to meet with Nixon? Might the United States grant diplomatic recognition to China? After decades of enmity, could the United States and the Soviet Union agree on anything significant?

Further experiments showed that people were driven to overstate the accuracy not only of their original predictions but also of those made by others. Similar results have been found for other events that gripped public attention, such as the O. J. Simpson murder trial and the impeachment of President Bill Clinton. The tendency to revise the history of one’s beliefs in light of what actually happened produces a robust cognitive illusion. Hindsight bias has pernicious effects on the evaluations of decision makers. It leads observers to assess the quality of a decision not by whether the process was sound but by whether its outcome was good or bad. Consider a low-risk surgical intervention in which an unpredictable accident occurred that caused the patient’s death. The jury will be prone to believe, after the fact, that the operation was actually risky and that the doctor who ordered it should have known better.

One group was shown only the evidence available at the time of the city’s decision; 24% of these people felt that Duluth should take on the expense of hiring a flood monitor. The second group was informed that debris had blocked the river, causing major flood damage; 56% of these people said the city should have hired the monitor, although they had been explicitly instructed not to let hindsight distort their judgment. The worse the consequence, the greater the hindsight bias. In the case of a catastrophe, such as 9/11, we are especially ready to believe that the officials who failed to anticipate it were negligent or blind. On July 10, 2001, the Central Intelligence Agency obtained information that al-Qaeda might be planning a major attack against the United States. George Tenet, director of the CIA, brought the information not to President George W. Bush but to National Security Adviser Condoleezza Rice.

 

pages: 624 words: 127,987

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

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Albert Einstein, Atul Gawande, Black Swan, business process, buy low sell high, capital asset pricing model, Checklist Manifesto, cognitive bias, correlation does not imply causation, Credit Default Swap, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, Dean Kamen, delayed gratification, discounted cash flows, double entry bookkeeping, Douglas Hofstadter, en.wikipedia.org, Frederick Winslow Taylor, Gödel, Escher, Bach, high net worth, hindsight bias, index card, inventory management, iterative process, job satisfaction, Johann Wolfgang von Goethe, Kevin Kelly, Lao Tzu, loose coupling, loss aversion, market bubble, Network effects, Parkinson's law, Paul Buchheit, Paul Graham, place-making, premature optimization, Ralph Waldo Emerson, rent control, side project, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, subscription business, telemarketer, the scientific method, time value of money, Toyota Production System, tulip mania, Upton Sinclair, Walter Mischel, Y Combinator, Yogi Berra

Seeking disconfirming evidence will either show you the error of your ways or provide additional evidence for why your position is actually correct—as long as you suspend judgment long enough to learn from the experience. Looking for disconfirming information is uncomfortable, but it’s useful, whatever you ultimately decide. SHARE THIS CONCEPT: http://book.personalmba.com/confirmation-bias/ Hindsight Bias Finish each day and be done with it. You have done what you could. Some blunders and absurdities no doubt crept in; forget them as soon as you can. Tomorrow is a new day; begin it well and serenely and with too high a spirit to be encumbered with your old nonsense. —RALPH WALDO EMERSON, ESSAYIST AND POET How do you feel when you realize that you’ve made a mistake? Hindsight Bias is the natural tendency to kick yourself for things you “should have known.” If you lose your job, you “should have known it was coming.” If the price of a particular stock you own drops 80 percent overnight, you “should have sold it.”

Don’t feel bad about things that you “should have seen” or “should have done.” Changing the past is outside of your Locus of Control (discussed later), so there’s no sense in wasting energy on self-doubt, wondering what might have been. Hindsight Bias becomes destructive if you negatively judge yourself or others for not knowing the unknowable. As the saying goes, “Hindsight is 20-20.” Reinterpret your past mistakes in a constructive light, and focus your energy on what you can do right now to move in a positive direction. SHARE THIS CONCEPT: http://book.personalmba.com/hindsight-bias/ Performance Load If not controlled, work will flow to the competent man until he submerges. —CHARLES BOYLE, FORMER U.S. CONGRESSIONAL LIAISON FOR THE NATIONAL AERONAUTICS AND SPACE ADMINISTRATION (NASA) Being busy is better than being bored, but it’s possible to be too busy for your own good.

Garbage in, garbage out Gas tank, stress and recovery Gates, Bill Generosity, and reciprocation Goals and objectives determining, five-fold methods framing next action and priming throughput as measure Godin, Seth Golden trifecta Goldsmith, Marshall Goleman, Daniel Google Graham, Paul Grandchild rule Greene, Robert Grenny, Joseph Group interaction. See Working with others Growth mind-set Guiding structure, for mental/physical health Gunaratana, Henepola Bhante Guthy-Renker Habits Hansson, David Heinemeier Health and energy cycles guidelines and modern world Hedging Hero’s Journey Hierarchy of funding Hindsight bias HiPPO rules Honesty, analytical Hook, creating Hope Diamond Human drives. See also Drives, human Human mind. See Mind and behavior Human performance, versus scalability Humanization IKEA Improvements and accumulation and amplification innovation versus competition Incentive-caused bias Incremental augmentation Inflows Influence, recommended reading Ingram, Mark Inhibition, mental Initial public offering Innovation, versus competition Insurance business, requirements of defined lifetime value Interdependence, in systems Intermediary distribution Internet as distraction, avoiding and duplication Interpretation, mental Iteration cycle and feedback and incremental augmentation and iteration velocity WIGWAM method Jobs, Steve Jones, Daniel T.

 

pages: 190 words: 53,409

Success and Luck: Good Fortune and the Myth of Meritocracy by Robert H. Frank

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2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, Amazon Mechanical Turk, American Society of Civil Engineers: Report Card, attribution theory, availability heuristic, Branko Milanovic, Capital in the Twenty-First Century by Thomas Piketty, carried interest, Daniel Kahneman / Amos Tversky, David Brooks, deliberate practice, en.wikipedia.org, endowment effect, experimental subject, framing effect, full employment, hindsight bias, If something cannot go on forever, it will stop, income inequality, invisible hand, labor-force participation, labour mobility, lake wobegon effect, loss aversion, minimum wage unemployment, Network effects, Report Card for America’s Infrastructure, Richard Thaler, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Rory Sutherland, side project, sovereign wealth fund, Steve Jobs, The Wealth of Nations by Adam Smith, Tim Cook: Apple, ultimatum game, Vincenzo Peruggia: Mona Lisa, winner-take-all economy

I stress the possibility of effective government in the hope of encouraging skeptics to keep an open mind about my claim that we could easily bequeath a much better society to our children. To accomplish that goal, the steps we need to take are not intrusive, nor do they require additional layers of bureaucracy. But we’ll be unlikely to take those steps if too many people feel certain they can’t work. 2 WHY SEEMINGLY TRIVIAL RANDOM EVENTS MATTER Psychologists use the term “hindsight bias” to describe the human tendency to think that events are more predictable than they are. In the late 1940s, the sociologist Paul Lazarsfeld staged a vivid demonstration of the phenomenon by describing a study purporting to have found that World War II soldiers from rural areas were much better able than their urban counterparts to cope with the demands of military life.1 Just as Lazarsfeld suspected, people who read the results of this study found them completely unsurprising: Of course the grueling lives led by rural men would make them much better equipped to endure wartime stresses!

The twist was that the study Lazarsfeld described was a complete fabrication. The actual study found the reverse: It was the soldiers from urban areas who fared much better in the military. Lazarsfeld’s point was that when you think you already know what happened, it’s easy to invent reasons for why it had to happen. Extending Lazarsfeld’s work, the sociologist Duncan Watts has argued that hindsight bias operates with particular force when people observe unusually successful outcomes.2 The problem, he suggested, is that it’s almost always easy to create a narrative after the fact that portrays such outcomes as having been inevitable. Yet every event is the outcome of a complex and interwoven sequence of steps, each of which depends on those preceding it. If any of those earlier steps had been different, the entire trajectory would almost surely be different, too.

., 51 Gilligan, Vince, 24, 31 Gilovich, Tom, 1, 80, 131 Gladwell, Malcolm, 33, 36 globalization, 54 Godfather, The, 23 Goff, Rick, xiv golden opportunity, 17, 109, 127, 130 Graf, Steffi, 45 Gramlich, Edward M., 27 gratitude, 98–103 Great Recession, the, 124, 134 Gross, Terry, 5 H&R Block, 43 Harvard University, 34, 36, 48, 72, 136 headwinds, 63, 64, 80, 81 height, 8 Hewlett-Packard, 53 High School Baseball Web, 62 high-speed rail, 87 hindsight bias, 21 Homo economicus, 129 hostile takeover litigation, 36 human capital, 40, 66 Huo, Yuezhou, 95 Huxley, Aldous, vii IBM, 34, 35, 51 Ice King, 37 income inequality, 52–55, 112, 113; and bankruptcy rates, 114, 115; and divorce rates, 114, 115; and government stimulus policy, 162, 163; and hours worked, 115; and long commute times, 114, 115; and spending by the wealthy, 165 individual vs. collective incentives, 17, 110, 117, 169 infrastructure, 12, 18, 87, 90, 91, 98, 111, 119, 120, 124, 147, 162 jealousy, 122 Johnson, Harold, 134–41 Journal of Political Economy, 28 JVC, 44 Kahneman, Daniel, 28, 70 Kardashian, Kim, 9 keeping up with the Joneses, 112 Keillor, Garrison, 72 Kildall, Gary, 34–36 Koble, Amy, 102 Koufax, Sandy, 142 Kristof, Nicholas, xiv, xv Krueger, Alan, 8 LaBelle, Patti, 103 Lake Wobegon Effect, 72 Landier, Augustin, 50 Langone, Kenneth, 104 last-name effects, 39 Lazarsfeld, Paul, 21 Leonard, Elmore, 5 Leslie, Ian, 22 Lewis, Michael, xii, xiii, xv, xvi Liar’s Poker, xiii liberals, xi, 17, 83 Little League baseball, 142 Lockdown, 30 Locke, John, 96 Lokkins, Elmer, 106 London School of Economics, 4 Long Tail, The, 47 lost-envelope thought experiment, 130 lottery winners, 69, 72 Louvre, the, 22 Major League Baseball, 62, 141 Manove, Michael, 74 markets for classical music, 46, 47 Marshall, Alfred, 41 Martin, Brett, 31 material living standards, 14, 90 Matthew Effect, 24 Mauboussin, Michael, 69 McCullough, Michael, 102 Mechanical Turk, 95, 137 meritocracy, xi, xii Merton, Robert K., 24 Mialon, Hugo, 14 Microsoft, 34, 35, 44 Milanovic, Branko, 7 Mlodinow, Leonard, 35 Mona Lisa, 9, 22–23 Morocco, 87 motivated cognition, 72 MS DOS, 35 Munger, Charlie, 39 Murphy, Liam, 97 Music Lab, 30, 45 Nagel, Thomas, 97 naïve optimism, 11, 12, 70–72, 75 National Center for Education Statistics, 87 National Institutes of Health, 135 natural selection, 73, 116 natural stupidity, 70 Nepal, 7, 14, 86, 112 Nepotist, The, 30, 49 Netflix, 47 Netherlands, 20 network effects, 43–45, 48 New Orleans, 25 New York City, 107; cost of weddings in, 110; dwelling sizes of the wealthy in, 120; hypercompetitive music scene in, 30; penthouses with sweeping views in, 121 New York Metropolitan Opera, 47 New York Times, xiv, 4, 29 New Yorker, 61, 103 New Zealand, 20 Nixon, Richard, 105 no-free-lunch principle, 109 Nobel Prize, 28 Northeastern University, 98 NPR, 5, 126 numerical simulation, 64 Nunn, Sam, 126 Obama, Barack, 84, 91 Ohio State University, 135 O’Neal, Ryan, 23 Organization for Economic Cooperation and Development, 115 orthodox (or standard, or traditional) economic theories, 13, 69, 70, 112, 115 Our Kids, 144 Pacino, Al, 23 Palomar, 128 Patterson, Tim, 35, 36 Peace Corps, 7, 86 Perkins, Tom, 104 Peruggia, Vincenzo, 22 piano manufacturing, 42 Piketty, Thomas, 55 political polarization, 17 Porsche, 15, 16, 91, 119 positional arms control agreements, 118 positional arms races, 116, 117, 118, 144 positional concerns, 115, 116, 118, 122 positive feedback loops, 9, 44, 51, 104, 105 potholes, 16, 91 poverty, 14 Prince Ali Lucky Five Star, 72 Princeton University, xii, 133 progressive consumption tax, 118–27, 158–71; and consumption by retirees, 164; and regressivity, 160; as a Pigouvian tax, effect on economic growth, 161, 162; as a Pigouvian tax, effect on wealth inequality, 166; transition from the current tax system, 162; treatment of durable purchases, 160; treatment of loans, 159, 160; versus taxes on specific luxuries, 163, 164 public investment (see also infrastructure), 13 Putnam, Robert, 144 Puzo, Mario, 23 QDOS (“quick and dirty operating system”), 35 Rai, Birkhaman, 7, 86 Reagan, Ronald, 90 Reardon, Sean, xv Reddit, 56 Reese, PeeWee, 142 Regan, Dennis, 131 relative purchasing power, 92 Review of Economics and Statistics, 28 Rhodes, Frank H.

 

pages: 1,088 words: 228,743

Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen

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Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, Bernie Madoff, Black Swan, Bretton Woods, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, George Akerlof, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, interest rate swap, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, Long Term Capital Management, loss aversion, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, New Journalism, oil shock, p-value, passive investing, performance metric, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, Robert Shiller, savings glut, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, systematic trading, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond

Thus it enables self-deception rather than accurate self-assessment. Confirmation bias We seek evidence that supports our view and we interpret ambiguous evidence as supportive. Hindsight bias Hindsight makes past outcomes, even major surprises, appear virtually inevitable after the fact. (“The market crash was bound to happen.”) More personally, memories play tricks on us: we often recall that we had assigned a high likelihood to events that subsequently materialized. (“I knew all along that housing was overpriced.”) Hindsight reinforces overconfidence but may also cause regret. By making the past appear more predictable than it really was, hindsight bias fools us into seeing the future as more predictable than it can ever be. Thus the inevitable disappointments that befall investors from time to time are seen, mistakenly, as having been avoidable.

However, I concede from the outset that the magic of view-based alpha generation cannot be conveyed in a book. • Two visual aids—an elephant and a cube—help the reader keep “the big picture” in mind through the book. • Although I present large amounts of empirical evidence about historical returns and forward-looking indicators, as well as numerous theories in an attempt to make sense of the data, I believe it is important to stress humility. Hindsight bias makes us forget how difficult forecasting is, especially in highly competitive financial markets. Expected returns are unobservable and our understanding of them is limited. Even the best experts’ forecasts are noisy estimates of prospective returns. It was six men of Hindostan, To learning much inclined, Who went to see the elephant (Though all of them were blind); That each by observation Might satisfy his mind.

However, my classification mixes in Hirshleifer’s (2001) argument that heuristic simplification and self-deception together provide a unified explanation for most of the judgment and decision biases identified in experimental psychology. On the limits to arbitrage, Shleifer–Vishny (1997) is the classic article, while on psychology and prospect theory the definitive paper is Kahneman–Tversky (1979). Among other works, I allude to Griffin–Tversky (1992) on representativeness/conservatism; Taleb (2001) and Zweig (2008) on hindsight bias; Bordalo–Gennaioli–Shleifer (2010) on salience theory; Shefrin–Statman (1985), Odean (1998), and Frazzini (2006) on disposition effect; Thaler–Johnson (1990) on the house money effect; and Ellsberg (1961) on ambiguity aversion. Turning to applications, besides Shiller’s book Irrational Exuberance, I highlight the writings of Keynes (1936), Minsky (1986), and Soros (2003, 2008). McCulley (2010) has been one of Minsky’s main posthumous ambassadors.

 

pages: 384 words: 118,572

The Confidence Game: The Psychology of the Con and Why We Fall for It Every Time by Maria Konnikova

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attribution theory, Bernie Madoff, British Empire, Cass Sunstein, cognitive dissonance, Daniel Kahneman / Amos Tversky, endowment effect, epigenetics, hindsight bias, lake wobegon effect, libertarian paternalism, Milgram experiment, placebo effect, Ponzi scheme, publish or perish, Richard Thaler, risk tolerance, side project, Skype, Steven Pinker, the scientific method, tulip mania, Walter Mischel

Over time, the psychologists found, memory got even worse: fully 84 percent of those in the three-to-six-month group showed faulty recall. They termed the tendency the hindsight bias. In hindsight, we don’t just say we should have known it. We say we did, in fact, know it. So what could Norfleet do, once his initial money was lost? Either he could admit he’d been wrong, that he’d fallen for the magic wallet scam—one of the oldest in the book—or he could say he’d known there was risk all along, but that he had made the investment because, fundamentally, the plan was sound. And if the latter was true, then why not continue to show support by giving over even more money? In hindsight, he was being daft. In the moment, he was exhibiting a hindsight bias of the strongest kind. Stetson’s show of indignity made it all the stronger: it activated the memory of their shared Masonic bond, of everything that came with fellowship and trust.

We fall for the tale because we want to believe its promise of personal gain—and don’t much feel like recalling any reasons why that promise may be more smoke and mirrors than anything else. In fact, Baruch Fischhoff, a social psychologist at Carnegie Mellon who studies how we make decisions, even has a name for instances of past misdirection: the knew-it-all-along effect or, as it’s more commonly known, hindsight bias. I knew it was a scam the whole time. So the fact that I don’t think that this scheme is a scam now speaks all the more highly for its integrity. The confidence man need not even convince us by this point. We’re quite good at getting over that hurdle ourselves. We don’t see what the evidence says we should see. We see what we expect to see. As Princeton University psychologist Susan Fiske puts it, “Instead of a naïve scientist entering the environment in search of the truth, we find the rather unflattering picture of a charlatan trying to make the data come out in a manner most advantageous to his or her already-held theories.”

., ref1 Demara, Mary McNelly ref1, ref2 determinism, creeping ref1 Deveraux, Jude ref1 De Védrines, Christine ref1 De Védrines, Ghislaine ref1, ref2 “Diddling” (Poe), ref1 disasters ref1 disrupt-then-reframe ref1 Dittisham Lady, ref1, ref2 door-in-the-face ref1, ref2 Drake, Francis ref1, ref2, ref3, ref4, ref5 Dunbar, Robin ref1, ref2, ref3 Dunning, David ref1 Dutch tulip mania ref1 Dylan, Bob ref1 Ebola crisis ref1 Egan, Michael ref1 Eiffel Tower ref1 Ekman, Paul ref1, ref2, ref3 elaboration likelihood model ref1 elder fraud ref1 Elizabeth I, Queen ref1 Emler, Nicholas ref1, ref2 emotions ref1, ref2, ref3, ref4, ref5, ref6, ref7 anticipation of ref1 donations and ref1 stories and ref1, ref2, ref3, ref4, ref5, ref6 endowment effect ref1, ref2 entrapment effect ref1 environment ref1 Epley, Nicholas ref1, ref2, ref3 Epstein, Seymour ref1, ref2 Erdely, Sabrina Rubin ref1 Evans, Elizabeth Glendower ref1 even-a-penny scenario ref1, ref2 exceptionalism ref1, ref2, ref3, ref4 expectancies ref1, ref2 exposure ref1, ref2 Extraordinary Popular Delusions and the Madness of Crowds (Mackay), ref1 Eyal, Tal ref1 Facebook ref1, ref2, ref3, ref4, ref5, ref6, ref7 facial expressions ref1, ref2, ref3 Fallon, James ref1 familiarity ref1, ref2, ref3, ref4 Farms Not Factories ref1 FBI ref1, ref2, ref3 fear ref1 Feldman, Robert ref1 Fenimore, Karin ref1 Festinger, Leon ref1, ref2, ref3 Fetzer, Barbara ref1 Figes, Orlando ref1 Fischhoff, Baruch ref1, ref2 Fiske, Susan ref1 Fitzgerald, Alan and Eilis ref1 Fitzgerald, Elizabeth (Madame Zingara), ref1, ref2 fix ref1 Folt, Carol ref1 football ref1 foot-in-the-door ref1, ref2, ref3, ref4 Frampton, Anne-Marie ref1, ref2, ref3 Frampton, Paul ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9 Frank, Jerome ref1 Franklin, Benjamin ref1, ref2 Franklin Syndicate ref1, ref2, ref3, ref4 Fraser, Scott ref1 Freedman, Ann ref1, ref2, ref3, ref4, ref5, ref6, ref7 Freeman, Jonathan ref1 French, John ref1, ref2 Fund for the New American Century ref1 future ref1 predicting ref1, ref2, ref3, ref4 Galinsky, Adam ref1 gambler’s fallacy ref1, ref2 Gant, Robert ref1 Geis, Florence ref1 genetics ref1 Gerard, Harold ref1 Gerhartsreiter, Christian ref1 Gifford, Adam Lord ref1 Gilbert, Daniel ref1, ref2 Gilligan, Andrew ref1 Gilovich, Thomas ref1 Glass, Stephen ref1, ref2 Goetzinger, Charles ref1 Gondorf, Fred and Charles ref1 Goodrich, Judge ref1 Gordon, John Steel ref1 gorilla experiment ref1 gossip ref1, ref2, ref3 Goya, Francisco ref1 Grazioli, Stefano ref1 Great Imposter, The (Crichton), ref1, ref2, ref3 Green, Melanie ref1, ref2 Green Dot cards ref1 Greg ref1 grifter ref1 grooming ref1 groups, belonging to ref1 Guillotin, Joseph ref1 Gur, Ruben ref1 Gurney, Edmund ref1 Hancock, Jeffrey ref1 Hansen, Chris ref1 Hanson, Robert ref1 happiness ref1, ref2, ref3 Hare, Robert ref1 Harley, Richard ref1 Harlow, E. T., ref1, ref2 Hartzell, Oscar ref1, ref2, ref3, ref4, ref5, ref6, ref7 Haugtvedt, Curtis ref1 Hauser, Marc ref1 health ref1 health products ref1 hedge funds ref1 Heilbroner, Robert ref1 Herbert, David ref1 Herschberg, Jenks ref1 Herschel, John ref1 Herschel, William ref1 Hewitt, Marvin Harold ref1 Hill, Richard ref1 hindsight bias ref1, ref2, ref3 Hines, Kelly Smith ref1 Hobbes, Thomas ref1 Holmes, Oliver Wendell ref1 Hone, Richard ref1 Hopkins, Budd ref1 hot-hand fallacy ref1 Houdini, Harry ref1, ref2, ref3 How the Mind Works (Pinker), ref1 How We Die (Nuland), ref1 Human Knowledge: Its Scope and Its Limits (Russell), ref1 HumInt ref1, ref2 Hunt, Shelby ref1 Hurd, Judge ref1 Hustlers and Con Men (Nash), ref1 Ickes, William ref1 identifiable-victim effect ref1 identity theft ref1, ref2, ref3 immoral behavior ref1 information priming ref1 insects ref1 insider trading ref1, ref2, ref3 intelligence ref1 Internet ref1, ref2, ref3, ref4, ref5 International Foundation for Art Research (IFAR), ref1, ref2 interrupted tasks ref1 investments ref1, ref2, ref3, ref4 Iraq War ref1 IRS and taxes ref1, ref2, ref3 It’s Always Sunny in Philadelphia, ref1 Jacobson, Lenore ref1 Jaeger, Wilf ref1, ref2 Jagatic, Tom ref1 Jahoda, Marie ref1, ref2 Jamal, Karim ref1 James, William ref1, ref2 Jarvik, Murray ref1 Jelly-Schapiro, Joshua ref1, ref2, ref3 Joan ref1 Johns Hopkins Magazine, ref1 Johnson, Paul ref1 Johnson, Samuel ref1 Jones, Robert ref1 Jonke, Eric ref1 Journal of Vibration and Control, ref1 judgments ref1, ref2, ref3 like-dislike ref1, ref2, ref3 about trustworthiness ref1 juries ref1 Kafka, Franz ref1 Kahneman, Daniel ref1, ref2, ref3, ref4, ref5, ref6, ref7 Keating, Caroline ref1 Kelley, Harold ref1 Kipling, Rudyard ref1 Knetsch, Jack ref1 Knight, Alan ref1 Knoedler & Company ref1, ref2 knowledge ref1, ref2 false ref1 of self ref1 Knowles, Eric ref1, ref2 Kramer, Roderick ref1, ref2, ref3 Kube, Jacqueline ref1 Kuhn, Deanna ref1 Kuklinski, Richard ref1 Kunda, Ziva ref1 Kurniawan, Rudy ref1, ref2, ref3 Lagrange, Pierre ref1 Lake Wobegon ref1, ref2 Landmark ref1 land scheme ref1 Langenderfer, Jeff ref1 Langer, Ellen ref1 language ref1 Laplace, Marquis de ref1 Lavoisier, Antoine ref1 Law, John ref1 law of small numbers ref1 Lazare Industries ref1 Lebowitz, Fran ref1 Lee, Blancey ref1, ref2 Lee, Porsha ref1 Lee, Rachel ref1 Lees, Captain ref1 legitimization effect ref1 Lehrer, Jonah ref1, ref2, ref3, ref4 Leslie, Cecil ref1 Levine, Moe ref1 Levy, Jack ref1 Lewis, Milo F., ref1 Life, ref1 liking and disliking ref1, ref2, ref3 limits ref1 Lincoln, Robert Todd ref1 Linn, Jay ref1 Lloyd, Robin ref1, ref2 Locke, Richard Adams ref1 Loewenstein, George ref1, ref2, ref3 London Evening Standard, ref1 Lorenz, Konrad ref1 lotteries ref1, ref2, ref3, ref4, ref5 Louis XIV, King ref1 Louis XVI, King ref1 Lovell, Simon ref1, ref2, ref3 lowball ref1 Lustig, Victor ref1, ref2, ref3 lying ref1, ref2, ref3 white lies ref1, ref2 Lyon, Gary ref1 Machiavellianism ref1 MacGregor, Gregor ref1 Mack, John ref1 Mackay, Charles ref1 Macklin, Rhonda ref1 Madame Zingara ref1, ref2 Madan, Gunish ref1, ref2 Madan, Sandip ref1 Madoff, Bernie ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9, ref10 magazine subscriptions ref1 Magician Among the Spirits, A (Houdini), ref1 magicians ref1, ref2, ref3 Mam, Somaly ref1 manipulation ref1 Marie Antoinette ref1 mark ref1 Markowitz, David ref1 Marks, Rose ref1 Maslow, Abraham ref1 Mathewson, Grover ref1 MatthewPAC ref1 Maurer, David ref1, ref2, ref3, ref4, ref5, ref6 McCann, Madeleine ref1 McCormick, Jim ref1 meaning ref1, ref2 Melville, Herman ref1 memory ref1, ref2, ref3, ref4 mental overload ref1 Mesmer, Franz Friedrich Anton ref1 Meyer, Max ref1 Mielnicki, Tomasz ref1 Milani, Denise ref1, ref2, ref3, ref4 Milgram, Stanley ref1 Milkman, Katherine ref1 Miller, John and Louis ref1 Miller, William Franklin ref1, ref2, ref3, ref4, ref5, ref6, ref7 Millet, Robert ref1 mind perception ref1 mirroring ref1 Mirvish, David ref1 Mississippi Company ref1 Mitchell, Sylvia ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8 Moffitt, Tim ref1 money: paper ref1 thinking about ref1 money box ref1 Montague, Miss St.

 

pages: 322 words: 77,341

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

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asset-backed security, bank run, banking crisis, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black-Scholes formula, Celtic Tiger, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, diversified portfolio, double entry bookkeeping, Exxon Valdez, Fall of the Berlin Wall, financial deregulation, financial innovation, fixed income, George Akerlof, greed is good, hindsight bias, housing crisis, Hyman Minsky, interest rate swap, invisible hand, Jane Jacobs, John Maynard Keynes: Economic Possibilities for our Grandchildren, laissez-faire capitalism, liquidity trap, Long Term Capital Management, loss aversion, Martin Wolf, mortgage debt, mortgage tax deduction, mutually assured destruction, new economy, Nick Leeson, Northern Rock, Own Your Own Home, Ponzi scheme, quantitative easing, reserve currency, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, South Sea Bubble, statistical model, The Great Moderation, the payments system, too big to fail, tulip mania, value at risk

What has had an effect, however, is the work of two Israeli psychologist-economists, Daniel Kahneman and Amos Tversky, who have produced a body of work studying “the susceptibility to erroneous intuitions of intelligent, sophisticated, and perceptive individuals,” in the words of the fascinating autobiography written by Kahneman on the occasion of winning the Nobel Prize in 2002. I have a confession to make about Kahneman and Tversky. I’d never heard of them until Kahneman won the Nobel,* and when I first read about their work, it seemed to me to consist of things which were surprising only to economists. One of their interests was “hindsight bias,” the way in which a random sequence of events is given structure and narrative by the false perspective of looking back over it from its outcome. Another was “loss aversion,” the fact that people place a higher value on not losing money than on gaining it; another was on “the law of small numbers,” referring to people’s tendency to draw overconfident conclusions from small amounts of evidence.

, 32, 214, 220 Haarde, Geir, 12 Haji-Ioannou, Stelios, 227 Haldane, Andrew, 36–37 Halifax, 38, 89 Hamanaka, Yasuo, 51 Harlot’s Ghost (Mailer), 172 health care, 13, 17, 198, 217, 222, 226–27 hedges, hedge funds, 164–66, 171 definition of, 54n–55n LTCM and, 54–56, 80, 142, 162, 164–65, 230–31 risk and, 49–50, 52, 58, 115, 155, 205 hedonic treadmill, 218 heuristics, 137–38 hindsight bias, 137 Hollinger, 59 Home Owners’ Loan Corporation, 99 Hong Kong, 7–8, 13–14 Hongkong and Shanghai Bank, 7, 53 Hoover, Herbert, 98–99 houses, housing, home ownership, 27–29, 40, 82–102, 109–32, 149, 157–60, 163–66, 187 balance sheets and, 27–28, 38 bubbles in, 5, 86–87, 89–90, 92, 101, 115, 159–60, 170, 173–74, 176–78, 216–17, 219, 223 foreclosures on, 83–85, 126–31 in Iceland, 10–11 inflation and, 88, 101, 179–80 in Ireland, 92, 110, 170–71 leverage and, 60–61, 83, 95, 97 liquidity and, 28–29, 90, 96–97 for low-income borrowers, 100, 113, 118, 121–23, 126–27, 130–31, 163 politics and, 87–89, 91, 96–101, 177–78 prices of, 5, 28–29, 37–38, 61, 71, 86–91, 101, 109–11, 113, 115, 125, 157, 160, 164–66, 173–76, 194, 208 and sense of dislocation, 95–97 in U.K., 38, 87–98, 110, 122, 177–78 in U.S., 37, 82–86, 95, 97–101, 109–10, 114–15, 122, 125–31, 157–58, 163 see also mortgages HSBC, HSBC Holding, 36, 53 Hume, David, 147 Hypo Real Estate, 40 IBM, 58, 65, 69 Iceland, Icelanders, 15 economic crisis in, 9–12, 23–24, 40, 170, 216, 223 pots and pans revolution in, 223 Iguchi, Toshihide, 51 illusion of validity, 140 incentives, 206–11, 224, 228 for bankers, 19, 37, 206–8 bond-rating agencies and, 209–11 incomes, 4, 13, 17, 66, 171, 203–4, 212, 221 balance sheets and, 26, 28, 30–31 banking and, 19–20, 37, 206–8, 218 housing and, 60, 90, 93–94, 100, 126, 130–32, 163 inflation and, 92, 179 India, 3–4 industrialization, 96–97 inequality, see equality, inequality inflation, 107, 144, 147, 220–21 asset price, 109–10 housing and, 88, 101, 179–80 incomes and, 92, 179 interest rates and, 102–3, 172–73, 178–80, 221 ING Group, 36 Innumeracy (Paulos), 8 insolvency, see solvency, insolvency interest, interest rates, 11, 24, 58–64 bonds and, 20, 61–63, 103, 107–10, 112, 144 and cost of money, 102–3 credit and, 172–73, 175, 209 derivatives and, 20, 47, 58, 63–64, 66, 69–70, 114, 121–22 government determination of, 102–3, 107–8, 172–80, 221 Greenspan and, 107–8, 165, 173–77 loans and, 59–60, 66, 74, 102, 108, 145, 172–73 mortgages and, 8, 58, 86, 89, 91–92, 95, 100, 102, 108, 110, 112–14, 122, 128, 145–46, 174, 176, 212 risk and, 69–71, 144–45, 165 International Monetary Fund (IMF), 15, 19, 186 International Swaps and Derivatives Association (ISDA), 79–80, 183 investing, investments, investors, 28, 58–63, 101–9, 171–72, 175–77, 181, 187, 213, 221 banks and, 25, 30, 43, 228 blue chip, 106 bonds and, 62–63, 102–3, 107–8, 111, 208–9 of China, 109, 176–77 derivatives and, 54–56, 58, 69–70, 73, 117, 120, 132, 153, 158, 172, 184 diversification of, 146–48 hedge funds and, 54n–55n housing and, 86–88, 97, 101 interest rates and, 102–3 regulation and, 225–26 risk and, 5, 68, 70, 88, 103, 144, 146–53, 158, 165, 184, 190 in stocks, 59, 73, 101–7, 111, 146–52, 158, 175, 192 values and, 60–61, 104, 198 investment trusts, 55n Ireland, 15, 169–71, 177 economic contraction in, 170–71, 222–23 housing in, 92, 110, 170–71 Jacobs, Jane, 82 Japan, Japanese, 18, 51–54, 77 banks of, 43, 51, 229 derivatives and, 51–52, 54 Johnson, Simon, 19–20, 185–86 Jorion, Philippe, 156–57, 162 J.P.

 

pages: 397 words: 110,130

Smarter Than You Think: How Technology Is Changing Our Minds for the Better by Clive Thompson

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3D printing, 4chan, A Declaration of the Independence of Cyberspace, augmented reality, barriers to entry, Benjamin Mako Hill, butterfly effect, citizen journalism, Claude Shannon: information theory, conceptual framework, corporate governance, crowdsourcing, Deng Xiaoping, discovery of penicillin, Douglas Engelbart, Edward Glaeser, en.wikipedia.org, experimental subject, Filter Bubble, Freestyle chess, Galaxy Zoo, Google Earth, Google Glasses, Henri Poincaré, hindsight bias, hive mind, Howard Rheingold, information retrieval, iterative process, jimmy wales, Kevin Kelly, Khan Academy, knowledge worker, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Netflix Prize, Nicholas Carr, patent troll, pattern recognition, pre–internet, Richard Feynman, Richard Feynman, Ronald Coase, Ronald Reagan, sentiment analysis, Silicon Valley, Skype, Snapchat, Socratic dialogue, spaced repetition, telepresence, telepresence robot, The Nature of the Firm, the scientific method, The Wisdom of Crowds, theory of mind, transaction costs, Vannevar Bush, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize, éminence grise

As teenagers, 70 percent said religion was helpful to them; in their forties, only 26 percent recalled that. Fully 82 percent of the teenagers said their parents used corporal punishment, but three decades later, only one third recalled their parents hitting them. Over time, the men had slowly revised their memories, changing them to suit the ongoing shifts in their personalities, or what’s called hindsight bias. If you become less religious as an adult, you might start thinking that’s how you were as a child, too. For eons, people have fought back against the fabrications of memory by using external aids. We’ve used chronological diaries for at least two millennia, and every new technological medium increases the number of things we capture: George Eastman’s inexpensive Brownie camera gave birth to everyday photography, and VHS tape did the same thing for personal videos in the 1980s.

See Gmail search method, 33, 37 Google Blogger, 275 Google Chat, 42 Google Docs, 155 Google Earth, 62, 171 Google Glass, 138, 141–42 Gosling, Sam, 215–16 Graham, Steve, 184 Granovetter, Mark, 227–29 Gray, Brenna Clarke, 56 Great Firewall (China), 250, 271, 273 Greeks, ancient, on writing versus debate, 68–69, 75 Grindr, 81 Guardian, 170 Guardian Project, 274 Gurrin, Cathal, 33–35, 41–42 Gutenberg, Johann, 12, 118–19, 121 Haiti earthquake, 63, 265–66 Hajizada, Adnan, 268–69, 274 Haley, Ben, 209–10 Hamilton, Buffy, 207 Hamilton, Filippa, 108 hand waving, 53–54 Harris, Frances, 205–6 hashtag, development of, 65–66 Hayden, Theresa Nielsen, 79 Heath, Christian, 213 Hein, Ethan, 72–73 Henkin, David, 49 Hersman, Erik, 62 Hickey, Lisa, 215 Hinckl, Andy, 285–86 hindsight bias, 27 Historia Naturalis, 40 history, learning through video games, 199–202 hive mind, 172 Holmes, Sherlock, 172–73 homophily, 230–31, 261, 261–63 Horvitz, Eric, 39 Hydra, 5 hyperlinks, early concept, 123 index, origin of, 121 India, and online dissent, 275–76 Innis, Harold, 8 innovation and discovery eureka moments, 131–32 theory of multiples, 58–66 Instagram, 109–10 Instapaper, 136 Internet censorship, global view, 250 early visionaries on, 122–23 human dependence on, 116 as social observation tool, 153 Internet & American Life Project, 187–88 Iran dissidents, identifying online, 270 media bans in, 267 photomanipulation, use of, 107 Ito, Mizuko, 210–11 Jackson, Maggie, 137 Jacobi, Emily, 261 James, William, 237 Jardin, Xeni, 108 Jcham979, 94–95, 98 Jenkins, Henry, 187, 202 Jennings, Ken, 282, 288 Jeopardy!

See geolocation; mapping Loftus, Elizabeth, 24–25 Logo, 190–93 Logo Microworlds, 192 LOLcat-crafting, 108–9 Looxcie, 41 Los Angeles Times wikitorial, 159 Lost (TV show), 96 Lostpedia, 187 Luff, Paul, 213 Lunsford, Andrea, 66–68 Luria, Alexandr, 40 Luther, Martin, 249 McCain, John, 88 McIntosh, Jonathan, 100 MacKinnon, Rebecca, 270, 276 McLuhan, Marshall, 8, 102 McPherson, Sam, 187 Mad Libs, 191 MadV, 101 Magna Carta, 276 Maher, Ahmed, 255 Mahfouz, Asmaa, 259 MakerBot, 111–12 maker movement, 103 Malebranche, Nicolas, 119–20 Manjoo, Farhad, 261 Mann, Steve, 266–67 Many Eyes, 91–92 mapping electoral districts, tool for, 84–86 Haiti earthquake relief, 265–66 Ushahidi, development of, 62–63 Marconi, Guglielmo, 59 Marcus, Gary, 14 Maree, Daniel, 265 marginalia, 82 Mario Kart (video game), 37 Mark, Gloria, 135–36, 137 Mark, Kevin, 79 Martin, Trayvon, 264–65 mash-up videos, 100 math digital instruction, 175–78, 181–83, 191 learning difficulty related to, 189 “Mathematical Creation” (Poincaré), 131–32 Maverick, Augustus, 6 Mayer-Schönberger, Viktor, 42, 241 Mechanical Turk, 1 media convergence, 111 medical diagnosis supercomputer, 284–85 meditation, 137–38 Meier, Patrick, 266 memex, 123, 143 memorization opponent of, 119–20 proponents of, 132–33 memory. See also forgetting artificial, lifelog as, 29–44 context, importance of, 26 digital aids, 27–28 and digital tools, lack of research in, 134–35 episodic memory, 25–27 hindsight bias, 27 knowledge, incorporating, 129, 133 limitations of, 24–27 loss, as popular topic, 23–24 for meaning over details, 129, 133–34 search, efficiency of, 32 semantic memory, 116 social memory, 124 spaced retention, 144–45 transactive memory, 124–31 writing, benefits for, 57 versus written word, historical view, 117–20 Menger Sponge, 113 Mercury Grove, 217 Merton, Robert, 60 Mesopotamian writing, 116–17 metamemory, 124–25 microblogging, forms of, 76–77 micro-celebrity, 238 microfilm, 123 Mill, John Stuart, 133 Milli, Emin, 268–69 “Million Follower Fallacy, The” (Cha), 234–35 Million Hoodie March, 265 Miloševic, Slobodan, 267 mindfulness, 14, 137–38, 232 Minority Report (film), 105 Minsky, Marvin, 72 Miyamoto, Shigeru, 149 Mnemosyne, 133 Moholy-Nagy, László, 110 Momus, 238 Montaigne, Michel de, 120 Moodscope, 90 Moore’s Law, 90 Morozov, Evgeny, 270 Morse, Samuel, 95 moving image.

 

pages: 387 words: 120,155

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

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Affordable Care Act / Obamacare, availability heuristic, carbon footprint, Cass Sunstein, centre right, choice architecture, cognitive dissonance, collaborative consumption, correlation does not imply causation, Daniel Kahneman / Amos Tversky, endowment effect, happiness index / gross national happiness, hindsight bias, illegal immigration, job satisfaction, Kickstarter, libertarian paternalism, market design, meta analysis, meta-analysis, Milgram experiment, nudge unit, peer-to-peer lending, pension reform, presumed consent, quantitative easing, randomized controlled trial, Richard Feynman, Richard Thaler, Ronald Reagan, Rory Sutherland, Simon Kuznets, skunkworks, the built environment, theory of mind, traffic fines, World Values Survey

Prior to this, David was Chief Analyst in the Prime Minister’s Strategy Unit (2001–2007), and has held academic positions at the Universities of Cambridge, Oxford and Harvard. To the elected FOREWORD ONE OF THE most powerful and pernicious of the many cognitive biases that have been uncovered by behavioural scientists is ‘hindsight bias’, first investigated by Baruch Fischhoff when he was a graduate student studying at the Hebrew University with Daniel Kahneman and Amos Tversky. Simply put, hindsight bias is the phenomenon that after the fact, we think we knew it all along. Would America elect an African-American as President before a woman? Sure, we all thought that could happen. Did we think in 2000 that fifteen years later most of us would be carrying powerful computers in our pockets that could keep us up-to-date with email, answer nearly any factual question just by speaking to it, and get us anywhere without getting lost?

(page numbers in italics refer to illustrations) advertising: and alcohol 100–1 and humour 100 and shock 98–100, 100 and smoking 99, 100 airport expansion 98 alcohol 100–1, 127 and calories 100 and pregnancy 126–7 Alexander, Danny 281 anaesthetics 17 ‘animal spirits’ 207, 210, 211 Aos, Steve 282 Ariely, Dan 96–7, 134, 325 Aristotle 221, 240 Armstrong, Hilary 34 Asch, Solomon 26 ASH (Action on Smoking and Health) 189 Ashford, Maren 57, 83 attentional spotlight 83–4 Ayres, Ian 142 Bazerman, Max 134, 325 Beales, Greg 36 Behavioural Insights Team (BIT) (see also nudging): arguments lost by 212–14 becomes social-purpose company 350 beginnings of x–xi, 50–8, 56, 58, 341 current numbers employed by xiii, 341 current trials by 341 expansion of xiii governments follow 11 initial appointments to 56–7, 56 initial scepticism towards 9 most frequent early criticisms of 333 naming of x–xi, 52–3 objectives of 54–5 and transparency, efficacy and accountability, see under nudging and webpage design 275–9, 276 World Bank’s request to 125 year of scepticism experienced by 274 behavioural predators 312–13 Benartzi, Shlomo 64 benefits, see welfare benefits Bentham, Jeremy 221–2 BIG lottery 283 ‘Big Society’ 43, 50, 142, 250 BIT, see Behavioural Insights Team Blair, Tony 151, 225 and behavioural approaches in government 302 Brown takes over from 36, 260–1 review into tenure of 34 Strategy Unit of 31 Tories’ admiration of 50 Bogotá 135, 146 Bohnet, Iris 123 Britton, John 188 Brown, Gordon 34 becomes PM 36, 260–1 Byrne, Liam 47 Cameron, David 151 BIT set up by 8 and Coalition Agreement 38 and data transparency 159 Hilton appointed by 43 and randomised controlled trials 274 and response to notes 186 and smoking 194 and well-being 225–8, 227, 250 car tax 3, 91, 92, 275–8 carrier bags 23 Centre for Ageing Better 282 Centre for Local Economic Growth (LEG) 282, 288 Chand, Raj 146 charities 116–20, 142–4, 144 and reciprocity 116 Chetty, Raj 64 childbirth, see pregnancy and childbirth Cialdini, Robert 34–6, 47, 107–8, 109, 113, 121–2, 308, 312 Clegg, Nick, and Coalition Agreement 38 Cochrane, Dr Archie 269–71, 295, 297 Cochrane Collaboration 271 cocktail-party effect 86 cognitive dissonance 21 cognitive psychology 27–9, 28 Colbourne, Tim 215 College of Policing 282, 289 Collins, Kevan 283, 285 Community First 254–5 commuting 219–20, 263–4 conflict and war 20–1, 27, 87, 344–5 consumer feedback 161–9, 167 improvements driven by 168–9 in public sector 163–9, 167 cooling-off periods 77 Council Tax 95 crime prevention (see also theft): ‘scared straight’ approach to 266–8, 267 and ‘What Works’ institutes 289 Darley, J. 27, 110 data transparency 153–84 and better nudges 179–80 and consumer feedback 161–9, 167 improvements driven by 168–9 in public sector 163–9, 167 and food labelling 172, 178 and machine-readable code 154, 157, 159 and RACAP 157 in restaurants 178 and understandable information 176–9 on cancer 178–9 on car safety 177–8 on financial products 177 and utility suppliers 154–60, 155 Davey, Ed 157 Deaton, Angus 243 decision fatigue 141 Deep Blue 7 Diener, Ed 231 disability-adjusted life years (DALYs) 272 discontinuity design 161–2 doctors’ handwriting 72, 72 Dolan, Paul 47–8, 220 Down, Nick 113 drivers’ behaviour 18, 18 Duckworth, Angela 247 Dunn, Elizabeth 220, 237, 250, 256 Durand, Martine 243 Dweck, Carol 343 e-cigarettes 188–97, 193, 215 estimated years of life saved by 195, 216 and non-smokers 193–4 and quit rates 192–3, 193 by socio-economic grouping 195 Early Intervention Foundation (EIF) 282 EAST (Easy, Attractive, Social, Timely) framework 10, 60, 149, 349 Attractive 80–105, 81, 85, 94 Easy 62–79, 68, 72, 73 and jobcentres 200 Social 106–25, 115, 118, 120, 122 (see also social influence) Timely 126–49, 129 Easterlin, Richard 238 eating habits 139, 171, 307 (see also obesity/weight issues) and choice 306–7 and food pyramid/plate illustrations 41, 41 and food tax 301–2 and healthy/unhealthy food 41, 82, 101–2, 216, 302 ‘mindless’ 171 Economic and Social Research Council 283 economy, UK 205–12 econs 6–7, 178, 223 education 137, 282 financial 64 further 146–7 and timely intervention 146–7 and ‘What Works’ institutes 283–7, 284, 286 Educational Endowment Foundation (EEF) 282, 283–7, 284, 286 Effectiveness and Efficiency (Cochrane) 295 endowment effect 140 Energy Performance Certificate 179 energy ratings 135 energy and utility suppliers, see utility suppliers Enterprise Bill 159 Epley, Nick 260–1 established behaviour, see habits ethnicity, and recruitment 137–9, 344 experimental government 266–98, 270, 272, 276 and crime prevention 266–8, 267 ethics of 325–8 (see also nudging: and accountability) and growth vouchers 279–80 and organ donation 275–9, 276 and overseas health-aid programmes 273 and radical incrementalism 291 and ‘What Works’ institutes 281–90, 292–4 Centre for Ageing Better 282 Centre for Crime Reduction 289 Centre for Local Economic Growth (LEG) 282, 288 Early Intervention Foundation (EIF) 282, 288 Educational Endowment Foundation (EEF) 282, 283–7, 284, 286 experimental psychology 24–6 farmers 145 ‘fat tax’ 301–2 (see also eating habits) fertiliser 145 Feynman, Richard 296, 297 financial crisis 45, 46, 206, 336 (see also UK economy) financial products 177, 206 fines, collecting 3–4, 52, 89, 90–1 Fischhoff, Baruch ix Fisher, Ronald 291 Fiske, Susan 84, 86, 325, 345 food pyramid/plate illustrations 41, 41 forms, prefilling 73–4 fossils 35 Frederick the Great 15, 16 Freud, Lord 279 Gallagher, Rory 55, 88–9, 158, 197–8, 204, 343, 349 gender equality, and company boards 123 Genovese, Kitty 109–10 Gigerenzer, Gerd 178 Gilbert, Danny 139, 220 Gino, Francesca 347 giving 116–20, 142–4, 144, 250 God Complex 269 Gove, Michael 287 Grant, Adam 347 Green Book 46, 228, 258, 259 Grice, Joe 233 Gross Domestic Product (GDP) 222–4, 255 (see also UK economy) Grove, Rohan 211 growth vouchers 279–80 Gyani, Alex 197–8, 203, 204, 343, 349 habits: and early intervention 128–32 key moments to prompt or reshape 132–9 and tax payments 131 Hallsworth, Michael 48, 113 Hancock, Matthew 279 hand washing 99, 140 happy-slave problem 231 Haynes, Laura 56–7 hearing 25 Heider, Fritz 345 Helliwell, John 226–7, 232 Henry VIII 17 herd instinct 161 Heywood, Sir Jeremy 2, 215, 217, 281 The Hidden Wealth of Nations (Halpern) 44 Highway Code 20 Hillman, Nick 165 Hilton, Steve x, 43–4, 51, 53–4, 159, 190, 214, 215, 225–6, 247, 250 and randomised controlled trials 274 hindsight bias ix HMRC 2–3, 8, 87–8, 89, 113, 115, 118, 120, 181–2 (see also tax payments) BIT member’s secondment to 113 non-tax-related business communications sent via 210–11 and online tax forms 74 and randomised controlled trials 274 Homer, Lin 210 honesty 133–4 honours 98 horses’ behaviour 18–19, 19 hospitals: and doctors’ handwriting 72, 72 and patient charts 72–3, 73 Hume, David 221 Hunt, Stefan 209 Hurd, Nick 250 Hutcheson, Francis 221 hyperbolic discounting 139 imprinting 128–9, 129 infant development 128–30 (see also pregnancy and childbirth) and early mother–child ‘meshing’ 129 (see also imprinting) in geese 128–9, 129 and mother’s depression 129 Influence: The Psychology of Persuasion (Cialdini) 34–5, 312 Inglehart, Ronald F. 229 Inland Revenue, see HMRC Institute for Government 40, 46–50 J-PAL 294 jobcentres 120–1, 197–205, 200, 201, 343, 349 (see also unemployment) John, Peter 96 The Joyless Economy (Scitovsky) 223 judges 140 Kahneman, Daniel 27, 29–30, 32, 48, 220, 226, 230 BIT’s work commended by 11 Kasparov, Garry 7 Kennedy, Robert F. 218, 222 Kettle, Stuart 125 Keynes, John Maynard 210, 211–12 King, Dom 48, 72 Kirkman, Elspeth 121, 146 knife crime 122 Kuznets, Simon 222 Laibson, David 64–5, 245, 307 Latene, B. 27, 110 Layard, Richard 225, 242, 248 Lazy Town 82 Legatum Institute 242–3 letters/messages, simplifying 71–3 and handwriting 72 in hospitals 72–3, 73 and prefilled forms 73–5 Letwin, Oliver 213, 217, 281, 295 Life satisfaction (discussion paper) 225 (see also well-being) Linos, Elizabeth 137, 344 List, John 286 litter 23, 35, 94, 107–8, 114 Loewenstein, George 307, 324, 345 loft/wall insulation 3, 75–6 Lorenz, Konrad 128–9, 129 lotteries, as incentive 94–6 Luca, Michael 161–2, 166, 177 Lyard, Richard 238 Lyons, Michael 250 MacFadden, Pat 34 Mackenzie, Polly 51, 215 Major, John 46 Manzi, James 295–6 Marcel, Anthony 136 Martin, Steve 113 Matheson, Jill 227 Mayhew, Pat 66 Mazar, Nina 347 Meacher, Michael 224 mental health 246–9 Merkel, Angela 243 midata, see data transparency Milgram, Stanley 26, 327 Miliband, Ed 34 military recruitment advertising 87 Milkman, Katherine 323 Mill, John Stuart 221 MINDSPACE framework 49–50, 50, 60, 72 motorcycle helmets 66–7 Mulgan, Geoff 225, 301–2 Mullainathan, Sendhil 343 National Citizenship Service (NCS) 251–2, 251 National Institute for Health and Care Excellence (NICE) 195, 271, 281, 290 Nesta 350 Nguyen, Sam 55, 197–8, 343 The Nicomachean Ethics (Aristotle) 240 nicotine-replacement therapy (NRT) 193, 193 (see also smoking) 9/11 28 Norton, Mike 256, 347 Nudge (Thaler, Sunstein) ix–x, 6–7, 39, 157, 234 Nudge Unit, see Behavioural Insights Team nudging (see also Behavioural Insights Team; EAST framework): and accountability 324–5 and experimentation, ethics of 325–8 and the public voice 328–32, 329 defined and discussed 22–4 and efficacy 304, 315–24 and familiarity with approach 319–24 relative 318–19 improving, with better data 179–80 rediscovery of 13 and subconscious priming 136 and transparency 304–15 and behavioural predators 312–13 and choice 306, 314–15 and effective communication vs propaganda 307–11, 311 Nurse Family Partnership 129 Obama, Barack 39–40, 254 acceptance speech of 38 Obama, Michelle 101 obesity/weight issues 101, 170–3, 307 (see also eating habits) in children, levelling of 173 and food labelling 172 and ‘mindless’ eating 171 O’Donnell, Sir Gus (later Lord) 45–6, 47, 57, 225, 227, 227, 242, 258 OECD 293, 340 Office of War Information (US) 21 Olds, David 130 online shopping 109 Ord, Toby 273 organ donation 9, 37, 52, 275–9 Orwell, George 309, 311 Osborne, George 45 and data transparency 159 O’Shaughnessy, James 247 Overman, Henry 288 Paley, William 221 paternalism x, 33, 51, 316 Pelenur, Marcos 135 pensions xii, 9, 62–5, 331 and choice 307 PMSU’s paper on 33 people’s parliaments 332 perception 24–5, 25 Personality responsibility and behaviour change (discussion paper) 301–2 police, ethnic recruits into 137–9, 344 potato consumption 15–16 pregnancy and childbirth 126–7 (see also infant development) Prescott, John 302 Prime Minister’s Strategy Unit (PMSU) 31–3, 47, 53, 225, 337 and Personality responsibility and behaviour change paper 301–2 psychological operations (PsyOps) 30, 308–9, 333 Putnam, Robert 253 radical incrementalism 291 randomised controlled trials (RCTs) 8, 113, 132, 182, 252, 270, 274–5, 283, 297–8, 339 and HMRC 274 Raseman, Sophie 157 RECAP 157 recycling 35 Red Tape Challenge 57 Reeves, Richard 51 Revenue and Customs, see HMRC road fuel 23 road traffic, see vehicles Roberto, Christine 101, 178 Rogers, Todd 146, 321 Rolls-Royce 208 Roosevelt, Franklin D. 21 Ruda, Simon 125, 137, 214, 344 Sainsbury, Lord (David) 46–7 Sanders, Michael 57, 116, 119, 142–3, 146 Scheving, Magnús 81, 82–3 Scitovsky, Tibor 223 Scott, Stephen 247 Seligman, Marty 232, 247 Sen, Amartya 231 Service, Owain 2, 56, 69 Sesame Street 101 Shadbolt, Sir Nigel 158 Shafir, Eldar 343, 345 sight 24–5, 25 Silva, Rohan x–xi, 43–5, 51, 53–4, 159 Singer, Tania 345 small businesses 205–9 passim (see also UK economy) smart disclosure 157 smoke detectors 99 smoking 9, 23, 99, 100, 138 and e-cigarettes 188–97, 193, 215 estimated years of life saved by 195, 216 and non-smokers 193–4 and nicotine-replacement therapy (NRT) 193 and pregnancy 126–7 prevalence of 189 and quit rates 192–3, 193 by socio-economic grouping 195 SNAP framework 48 social influence 26–7, 106–25 and bystander intervention 110 dark side of 109–10 and litter 107–8, 114 norms of: descriptive vs injunctive 108 picking apart 107–11 in policy 111–15 and online shopping 109 and personal touch 119–21 and reciprocity 115–17 social psychology 107 Soman, Dilip 337 Southern Cross station staircase 85 speed bumps 76–7 Sportacus 81–3, 81 Stanford Prison 26–7 Steinberg, Tom 254 stickk.com 142 subconscious priming 136 suicide 67–9, 68, 77 Sunstein, Cass ix–x, 6–7, 22, 39–42, 44, 57, 73, 305, 307, 314 and RACAP 157 supermarkets 80–1, 84, 86, 171–2 and food labelling 173, 178 Sutherland, Rory 187–8 tailored defaults. 307 tax payments 3, 8, 23, 52, 87–8, 88, 89, 112–14, 118, 120, 131, 181–2 in Central America 125 Council Tax 95 and habits 131 and lottery incentive 96–7 and online tax forms 74–5 and randomised controlled trials 274 road duty 3, 91, 92, 275–8 social-norm-based approach to 113, 115 Tetlock, Philip 192 Thaler, Richard 6–7, 22, 39, 44, 50, 51, 53, 57, 305 and BIT’s name 53 and RACAP 157 theft (see also crime prevention): mobile phones 173–6, 174, 175 and target-hardening 78, 214 vehicles: cars 169–70 motorcycles 66–7 time, perception of 128 time-inconsistent preferences 128, 139–45 Times 301–2 tobacco, see smoking Turner Lord (Adair) xii, 33, 331 Tversky, Amos 27, 29, 230 UK economy 205–12, 215, 216 (see also financial crisis; Gross Domestic Product) unemployment 120–1, 122, 197–205, 200, 201, 216, 343, 349 (see also jobcentres) and well-being 255–6 utilitarianism 221–2 utility suppliers: and data transparency 154–60 switching among 153–4, 155–6, 155, 160, 213 vehicles 18–20 safety of 177–8 and speeding 76–7, 92–5, 100 varied penalties for 147 thefts of: cars 169–70 motorcycles 66–7 Victoria, Queen 17 visas 132 Vlaev, Ivo 48 Volpe, Kevin 320 voter registration 95–6 Walsh, Emily 123 Wansink, Brian 171, 306 war 20–1 war and conflict 20–1, 27, 87, 344–5 weight, see obesity/weight issues welfare benefits 8 and conditional cash transfers 135, 145 and timing of payments 135 well-being 218–65 and community 249–55, 251 and commuting 219–20, 263–4 by country 229, 238, 243 drivers of 235–41 material factors 237–9 social factors 239–41 (see also well-being: and community) sunny disposition 235–7 early concepts of 220–2 and GDP 222–4, 255 and governance and service design 258–62 and happy-slave problem 231 and income, work and markets 255–7 and Life satisfaction paper 225 measuring 222–4 big questions concerning 231–3 subjective 228–31 and mental health 246–9 and National Citizenship Service programme 251–2, 251 by occupation 244 and policy 242–3, 258 subjective 224, 228–31 and giving 250 (see also giving) by occupation 244–5 and prostitutes 231–2 UK government’s programme on 226–8, 233–5, 234, 240 unemployment’s effects on 255–6 and utilitarianism 221–2 What Works institutes 281–90, 292–4, 340 Centre for Ageing Better 282 Centre for Crime Reduction 289 Centre for Local Economic Growth (LEG) 282, 288 Early Intervention Foundation (EIF) 282, 288 Educational Endowment Foundation (EEF) 282, 283–7, 284, 286 When Harry Met Sally 160–1 ‘wicked problems’ 170 Willetts, David 165 World Bank 125, 293, 309, 340 World Values Survey (WVS) 229 yelp.com 161–2 Young, Lord 279 ACKNOWLEDGEMENTS THERE ARE MANY people who deserve thanks and credit for the work and results of the Behavioural Insights Team that this book describes, and a rather shorter list for the writing and editing of the book itself.

 

pages: 411 words: 108,119

The Irrational Economist: Making Decisions in a Dangerous World by Erwann Michel-Kerjan, Paul Slovic

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Andrei Shleifer, availability heuristic, bank run, Black Swan, Cass Sunstein, clean water, cognitive dissonance, collateralized debt obligation, complexity theory, conceptual framework, corporate social responsibility, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-subsidies, Daniel Kahneman / Amos Tversky, endowment effect, experimental economics, financial innovation, Fractional reserve banking, George Akerlof, hindsight bias, incomplete markets, invisible hand, Isaac Newton, iterative process, Loma Prieta earthquake, London Interbank Offered Rate, market bubble, market clearing, moral hazard, mortgage debt, placebo effect, price discrimination, price stability, RAND corporation, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, statistical model, stochastic process, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, ultimatum game, University of East Anglia, urban planning

This could be due to an attempt to reduce cognitive dissonance, for self-justification, or simply to misremembering. It may also be a variant of hindsight bias, in which knowing the outcome alters an individual’s assessment of how likely it was to have occurred. For example, in a 1975 study by psychologist Baruch Fischhoff, who is also a contributor to this book, subjects were given passages to read about the Gurkha raids on the British in the early 1800s. Some were told how the conflict ended, and others were not. When asked what the probability of occurrence of each outcome was, those who knew the outcome gave it a much higher probability. With such “secondary hindsight bias,” individuals are unaware that the occurrence of an event influences what they believe ex post that they would have estimated ex ante.

 

pages: 336 words: 113,519

The Undoing Project: A Friendship That Changed Our Minds by Michael Lewis

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Albert Einstein, availability heuristic, Cass Sunstein, choice architecture, complexity theory, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Hofstadter, endowment effect, feminist movement, framing effect, hindsight bias, John von Neumann, loss aversion, medical residency, Menlo Park, Murray Gell-Mann, Nate Silver, New Journalism, Richard Thaler, Saturday Night Live, statistical model, Walter Mischel, Yom Kippur War

To combat the endowment effect, he forced his scouts and his model to establish, going into the draft, the draft pick value of each of their own players. The next season, before the trade deadline, Morey got up before his staff and listed on a whiteboard all the biases he feared might distort their judgment: the endowment effect, confirmation bias, and others. There was what people called “present bias”—the tendency, when making a decision, to undervalue the future in relation to the present. There was “hindsight bias”—which he thought of as the tendency for people to look at some outcome and assume it was predictable all along. The model was an antidote to these vagaries of human judgment, but, by 2012, the model seemed to be approaching a limit to the informational edge it would give the Rockets in valuing players. “Every year we talk about what to take out and what to put in the model,” said Morey. “And every year it gets a little more depressing.”

They all believed that they had assigned higher probabilities to what happened than they actually had. They greatly overestimated the odds that they had assigned to what had actually happened. That is, once they knew the outcome, they thought it had been far more predictable than they had found it to be before, when they had tried to predict it. A few years after Amos described the work to his Buffalo audience, Fischhoff named the phenomenon “hindsight bias.”† In his talk to the historians, Amos described their occupational hazard: the tendency to take whatever facts they had observed (neglecting the many facts that they did not or could not observe) and make them fit neatly into a confident-sounding story: All too often, we find ourselves unable to predict what will happen; yet after the fact we explain what did happen with a great deal of confidence.

 

pages: 407 words: 114,478

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

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asset allocation, Bretton Woods, British Empire, buy low sell high, carried interest, corporate governance, cuban missile crisis, Daniel Kahneman / Amos Tversky, Dava Sobel, diversification, diversified portfolio, Edmond Halley, equity premium, estate planning, Eugene Fama: efficient market hypothesis, financial independence, financial innovation, fixed income, German hyperinflation, high net worth, hindsight bias, Hyman Minsky, index fund, invention of the telegraph, Isaac Newton, John Harrison: Longitude, Long Term Capital Management, loss aversion, market bubble, mental accounting, mortgage debt, new economy, pattern recognition, quantitative easing, railway mania, random walk, Richard Thaler, risk tolerance, risk/return, Robert Shiller, Robert Shiller, South Sea Bubble, transaction costs, Vanguard fund, yield curve

Investors in tumultuous Germany, Japan, Argentina, and India were not so lucky; they obtained far smaller rewards. Thus, it is highly misleading to rely on the investment performance of history’s most successful nations and empires as indicative of your own future returns. At first glance, it might appear that the above list of winners and losers contradicts the relationship between risk and return. This is an excellent example of “hindsight bias”; in 1913 it was by no means obvious that the U.S., Canada, Sweden, and Switzerland would have the highest returns, and that Germany, Japan, Argentina, and India, the lowest. Going back further, in 1650 France and Spain were the mightiest economic and military powers in Europe, and England an impoverished upstart torn by civil war. The interest rate bottom of 4% reached in Rome is particularly relevant to the modern audience.

., 57 Emergencies, saving for, 240 Emerging markets, 31, 37, 38, 72, 94, 95, 124, 125, 156, 188, 255, 257, 268, 272, 274, 276, 283 England (See Britain) Enron, 161 Entertainment, investment as, 171–172, 183-184 Equities (See Stocks) ETFs (exchange-traded funds), 216, 217, 254, 255 eToys, 57 Euphoria, and bubbles, 136 European interest rates, historical perspective, 8–13 Exchange-traded funds (ETFs), 216, 217, 254, 255 Expected returns growth stocks, 173–175 long-term, 55, 70, 71 myopic risk aversion, 172-173, 184-185 overconfidence, 167–169, 181–183 vs. real returns, 68–69 Expense ratio (ER) in mutual fund costs, 94–95 Expenses (See Fees and expenses) Extraordinary Popular Delusions and the Madness of Crowds (Mackay), 151 Fair value of stock market, 47-53 Fama, Eugene, 37, 88-89, 120-121, 186, 257 Federal Reserve Bank, 146, 152, 159, 176 Fee-only financial advisors, 294 Fees and expenses, 401(k), 211–213 Fees and expenses, mutual funds differences in funds, 209–211 Forbes Honor Roll, 222 front load, 207 index funds, 245, 250, 254 load, 79, 203–205, 216 management fees, 206 no-load, 205–206, 215 Fidelity Capital Fund, 83 Fidelity Dividend Growth Fund, 207 Fidelity Magellan, 91–93, 97 Fidelity mutual funds, 205, 207–209, 210 Fidelity Select Technology Fund, 207–209 Fidelity Spartan funds, 216 Fiduciary responsibility of broker (lack of), 192 Financial Analysts Journal, 244 Financial calculator, 230, 237 Financial goals, 229, 239–240 First Quadrant, 88 Fisher, Irving, 43–48, 56, 229 Folios, 102 A Fool and His Money (Rothchild), 224 Forbes, Malcolm, 87–88 Forbes Honor Roll, 222 Forecasting Cowles and, 76-79, 87 investment newsletters and, 77, 78, 86, 87 Foreign stocks and returns asset allocation in portfolios, 116–120, 255–257, 256 growth vs. value stocks, 36–37 stability, societal, 29–32 tax efficiency of, 264 Fortune, 213, 221 Fouse, William, 95-97 French, Kenneth, 33–34, 35–37, 120 Fuller, Russell, 174 Galbraith, John Kenneth, 148 Gambling, 171–172 Garzarelli, Elaine, 169 GDP (gross domestic product) and technological diffusion, 132-133 GE (General Electric), 33, 244 General Electric (GE), 33, 244 General Motors, 65 Gibson, Roger, 225 Gillette, 151 Glass-Stegall Act, 193 Glassman, James, 53, 264 Global Investing (Brinson and Ibbotson), 225 Global stocks (See Foreign stocks) GNMA fund, Vanguard, 216 Go-Go years (1960-1970), 83, 148–151 Goetzmann, William, 30 Gold, (precious metals stocks), 123–124, 155 Gold mining, 69 Gold standard, 16–18, 145–146 Goldman Sachs Corporation, 147–148, 169 Goldman Sachs Trading Corporation, 148 Gordon Equation, 53–62, 192 Government securities, 259–260 Graham, Benjamin Depression-era mortgage bonds, 185 Hollerith Corporation, later IBM, 78 on income production, 44 on investor’s chief problem, 165 pre-1929 stock bubble, 57 Security Analysis, 157–158 Graham, John, 87 Grant, James, 224–225 Great company/great stock fallacy, 173–175, 185 Great Depression fear of short-term losses, 172–173 Fisher’s gaffe, 43 Graham on, 157–158 impact of, 5–6 manias, history of, 145–148 societal stability and DR, 64–65 Great Man theory, 95–96 Greenspan, Alan, 246 Gross domestic product (GDP) and technological diffusion, 132–134 Growth stocks (“good” companies) asset allocation, 247, 248–255, 251–253 earnings expectations of, 173–175 Graham on, 158 returns of, 34-38 “Gunning the Fund,” 207-209 Halley, Edmund, 138 Hammurabi, 7 Hard currency (gold), 16-20 Harrison, John, 142–143 Harvey, Campbell, 87 Hassett, Kevin, 53, 264 Hedge funds, 178–179 Herd mentality and overconfidence, 166-176, 181, 182 Hewlett-Packard, 158 High-quality corporate bonds, 260 High Yield bonds, 69–70 “Hindsight bias,” 8 History of investing and returns (Pillar 2), 127–162 about, xi, 296 ancient, 6–9 bonds, 13–22 European, middle ages to present, 9–13 on risk, 11-13, 22-29, 38-39 stocks investing in U.S., 4–6 outside U.S., 29–32 prior to twentieth century, 20 twentieth century, 20–22 summary on risk and return, 38-39 Treasury bills in twentieth century, 20–22, 23 Hollerith Inc., later IBM, 78 House, saving for, 240 Hubbard, Carl M., 231 IAI, 211 Ibbotson, Roger, 225 IBM (International Business Machines), 78, 83, 150, 151 Immediate past as predictive, behavioral economics, 170–171 “Impact cost,” mutual funds, 84, 85, 92, 94, 208, 211 Impatience, societal, and discounted dividend model (DDM), 46 “In-Between Ida,” asset allocation example, 269-271 Income production and discounted dividend model [discounted dividend model (DDM)], 43–73 Index fund advantages of, 95-105 bonds, 257–263, 258–259 defined, 97 exchange-traded funds (ETFs), 216, 217, 254, 255 performance and efficient market hypothesis, 95–98, 102–104 vs. performance of top 10% funds, 81 sectors in portfolio building, 122–124, 250, 251–253 tax efficient, 99 INEPT (investment entertainment pricing theory), 172 Inflation bond performance, 16-20 and gold standard, 16–18 government response to, 19–20 inflation risk, 13 and stocks, 20, 24 Inflation-adjusted returns earnings growth, 60 stocks, bonds and bills, 19, 20–22 young savers, 237–239 Inflation risk, 13 Information speed of transmission, 131 and stock prices, 89–90 Initial public offering (IPO), 134, 172 In Search of Excellence (Peters), 64 Instant gratification and discounted dividend model (DDM), 46 The Intelligent Asset Allocator (Bernstein, W.), vii, 110 Interest-rate risk, 13 Interest rates in ancient world, 6-8 annuity pricing, 10-12, 13 and bond yields, 10, 16-20 bonds and currency, changes from gold to paper (1900-2000), 17–19 as cultural stability barometer, 8–9 European, 8-13 Fisher’s discount rate (DR), 46–47 historic perspective on bills and bonds, 9-15 risk, 13 International Business Machines (IBM), 78, 83, 150, 151 Internet Capital Group, 152 Internet/dot-com as bubble, 151–152, 153, new investment paradigm, 56–58 Invesco mutual funds, 205 Investment vs. purchase, 45 vs. saving, 134, vs. speculation, 44, 157 Investment advisors (See Advisors, investment) Investment and Speculation (Chamberlain), 157 Investment Company Act of 1940, 161, 203, 213, 217 Investment entertainment pricing theory (INEPT), 172 Investment newsletters, 77, 78, 87 Ip, Greg, 167 IPO (initial public offering), 134, 172 iShares, 251-253, 257 Japan dominance in late 1970s, 66–67, 181–182 technical progress and diffusion, 132 Jensen, Michael, 78–80, 214 Johnson, Edward Crosby, II, 83, 91 Johnson, Edward Crosby, III (“Ned”), 194, 207, 208, 210 Jorion, Phillippe, 30 Journal of Finance, 80, 225 Journalist coverage, 219–225 JTS (junk-treasury spread), 70 Junk bonds, 69–70, 150n1, 260, 263, 283, 288-289 Junk-treasury spread (JTS), 70 Kahneman, Daniel, 166 Karr, Alphonse, 162 Kassen, Michael, 207, 219 Kelly, Walt, 179 Kemble, Fanny, 143 Kemper Annuities and Life, 205, 210 Kemper Gateway Incentive Variable Annuity, 205 Kennedy, Joseph P., Sr., 147 Keynes, John Maynard, 41-42, 18, 221 Kindleberger, Charles, 136–137 Kmart, 34–35 Ladies Home Journal, 65 Large company stocks asset allocation, 244–255, and Fidelity Magellan Fund, 92 rebalancing, 289–290 returns, 32-34, 38, 72 Law, John, 137–138 Leinweber, David, 88 Leveraged buyouts, 150n1 Leveraged trusts, 147–148 Lipper, Arthur, 83 Litton, 149–150 Load funds fees, mutual funds, 79, 196, 203–205, 216 Long Term Capital Management, 129, 179 Long-term credit (See Bonds) Long-term returns asset classes, 16-39 bonds, in asset allocation, 113–114 expected, in asset classes, 70, 71 Gordon Equation, 53–62, 192 stocks, 20-39 LTV Inc., 83 Lumpers vs. splitters in asset mix, 247, 248–255, 251–253 Lynch, Peter, 91–93 Mackay, Charles, 151 Malkiel, Burton, 55, 224 Management fees, mutual funds, 206, 209-211 Manhattan Fund, 83–84 Manias, 129–152 about, 129–130 bubbles (See Bubbles) identification, 153 Internet, 151–152, 153 Minsky’s theory of, 136, 140 new technology, impact of, 130–134 1960-1970 (Go-Go years), 148–151 railroads, 143-145, 158, 159–160 Roaring Twenties, 145–148, 153 space race, 149–150 Margin purchases, 147–148 Market bottom, 153–162 about, 153–154 as best time to invest, 66 buying at, 283 “Death of Equities,” 154–157 Graham on Great Depression, 157–161 panic, 161–162 Market capitalization, 33, 123, 245 Market impact, mutual fund costs, 82, 94–95, 208 Market strategists, 87, 169, 176, 186, 219 Market timing, 87–88, 108, 220 Market value formula, 52 McDonald’s, 150, 158 Mean reversion, 170 Mean variance optimizer (MVO), 108 Media, 219–225 Mellon Bank, 96 Mental accounting, 177, 186 Merrill, Charles Edward, 193–194, 213 Merrill Lynch, 88, 193–194, 200 Microsoft, 59, 166, 185 Miller, Merton, 7 “Millionaire,” origin of term, 138 The Millionaire Next Door (Stanley and Danko), 239 Minding Mr.

 

pages: 542 words: 132,010

The Science of Fear: How the Culture of Fear Manipulates Your Brain by Daniel Gardner

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Atul Gawande, availability heuristic, Black Swan, Cass Sunstein, citizen journalism, cognitive bias, cognitive dissonance, Columbine, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, Doomsday Clock, feminist movement, haute couture, hindsight bias, illegal immigration, medical residency, Mikhail Gorbachev, millennium bug, mutually assured destruction, nuclear winter, placebo effect, Ralph Nader, RAND corporation, Ronald Reagan, Stephen Hawking, Steven Levy, Steven Pinker, the scientific method, Tunguska event, uranium enrichment, Y2K, young professional

Literally so: We know, looking back, that this was not the end of the world—when we imagine nineteenth-century Paris, we tend to think of the Moulin Rouge, not plague—and that knowledge removes the uncertainty that was the defining feature of the experience for Heine and the others who lived through it. Simply put, history is an optical illusion: The past always appears more certain than it was, and that makes the future feel more uncertain—and therefore frightening—than ever. The roots of this illusion lie in what psychologists call “hindsight bias.” In a classic series of studies in the early 1970s, Baruch Fischhoff gave Israeli university students detailed descriptions of events leading up to an 1814 war between Great Britain and the Gurkas of Nepal. The description also included military factors that weighed on the outcome of the conflict, such as the small number of Gurka soldiers and the rough terrain the British weren’t used to.

Months after Nixon’s trip, he went back to each student and asked them about each event. Do you think it occurred? And do you recall how likely you thought it was to occur? “Results showed that subjects remembered having given higher probabilities than they actually had to events believed to have occurred,” Fischhoff wrote, “and lower probabilities to events that hadn’t occurred.” The effect of hindsight bias is to drain the uncertainty out of history. Not only do we know what happened in the past, we feel that what happened was likely to happen. What’s more, we think it was predictable. In fact, we knew it all along. So here we are, standing in the present, peering into the frighteningly uncertain future and imagining all the awful things that could possibly happen. And when we look back? It looks so much more settled, so much more predictable.

 

pages: 598 words: 134,339

Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World by Bruce Schneier

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23andMe, Airbnb, airport security, AltaVista, Anne Wojcicki, augmented reality, Benjamin Mako Hill, Black Swan, Brewster Kahle, Brian Krebs, call centre, Cass Sunstein, Chelsea Manning, citizen journalism, cloud computing, congestion charging, disintermediation, Edward Snowden, experimental subject, failed state, fault tolerance, Ferguson, Missouri, Filter Bubble, Firefox, friendly fire, Google Chrome, Google Glasses, hindsight bias, informal economy, Internet Archive, Internet of things, Jacob Appelbaum, Jaron Lanier, Julian Assange, Kevin Kelly, license plate recognition, linked data, Lyft, Mark Zuckerberg, Nash equilibrium, Nate Silver, national security letter, Network effects, Occupy movement, payday loans, pre–internet, price discrimination, profit motive, race to the bottom, RAND corporation, recommendation engine, RFID, self-driving car, Silicon Valley, Skype, smart cities, smart grid, Snapchat, social graph, software as a service, South China Sea, stealth mode startup, Steven Levy, Stuxnet, TaskRabbit, telemarketer, Tim Cook: Apple, transaction costs, Uber and Lyft, urban planning, WikiLeaks, zero day

connect-the-dots metaphor: Spencer Ackerman (13 Dec 2013), “NSA review to leave spying programs largely unchanged, reports say,” Guardian, http://www.theguardian.com/world/2013/dec/13/nsa-review-to-leave-spying-programs-largely-unchanged-reports-say. That doesn’t stop us: When we look back at an event and see all the evidence, we often believe we should have connected the dots. There’s a name for that: hindsight bias. The useful bits of data are obvious after the fact, but were only a few items in a sea of millions of irrelevant data bits beforehand. And those data bits could have been assembled to point in a million different directions. the “narrative fallacy”: Nassim Nicholas Taleb (2007), “The narrative fallacy,” in The Black Swan: The Impact of the Highly Improbable, Random House, chap. 6, http://www.fooledbyrandomness.com.

., 160 fiduciary responsibility, data collection and, 204–5 50 Cent Party, 114 FileVault, 215 filter bubble, 114–15 FinFisher, 81 First Unitarian Church of Los Angeles, 91 FISA (Foreign Intelligence Surveillance Act; 1978), 273 FISA Amendments Act (2008), 171, 273, 275–76 Section 702 of, 65–66, 173, 174–75, 261 FISA Court, 122, 171 NSA misrepresentations to, 172, 337 secret warrants of, 174, 175–76, 177 transparency needed in, 177 fishing expeditions, 92, 93 Fitbit, 16, 112 Five Eyes, 76 Flame, 72 FlashBlock, 49 flash cookies, 49 Ford Motor Company, GPS data collected by, 29 Foreign Intelligence Surveillance Act (FISA; 1978), 273 see also FISA Amendments Act Forrester Research, 122 Fortinet, 82 Fox-IT, 72 France, government surveillance in, 79 France Télécom, 79 free association, government surveillance and, 2, 39, 96 freedom, see liberty Freeh, Louis, 314 free services: overvaluing of, 50 surveillance exchanged for, 4, 49–51, 58–59, 60–61, 226, 235 free speech: as constitutional right, 189, 344 government surveillance and, 6, 94–95, 96, 97–99 Internet and, 189 frequent flyer miles, 219 Froomkin, Michael, 198 FTC, see Federal Trade Commission, US fusion centers, 69, 104 gag orders, 100, 122 Gamma Group, 81 Gandy, Oscar, 111 Gates, Bill, 128 gay rights, 97 GCHQ, see Government Communications Headquarters Geer, Dan, 205 genetic data, 36 geofencing, 39–40 geopolitical conflicts, and need for surveillance, 219–20 Georgia, Republic of, cyberattacks on, 75 Germany: Internet control and, 188 NSA surveillance of, 76, 77, 122–23, 151, 160–61, 183, 184 surveillance of citizens by, 350 US relations with, 151, 234 Ghafoor, Asim, 103 GhostNet, 72 Gill, Faisal, 103 Gmail, 31, 38, 50, 58, 219 context-sensitive advertising in, 129–30, 142–43 encryption of, 215, 216 government surveillance of, 62, 83, 148 GoldenShores Technologies, 46–47 Goldsmith, Jack, 165, 228 Google, 15, 27, 44, 48, 54, 221, 235, 272 customer loyalty to, 58 data mining by, 38 data storage capacity of, 18 government demands for data from, 208 impermissible search ad policy of, 55 increased encryption by, 208 as information middleman, 57 linked data sets of, 50 NSA hacking of, 85, 208 PageRank algorithm of, 196 paid search results on, 113–14 search data collected by, 22–23, 31, 123, 202 transparency reports of, 207 see also Gmail Google Analytics, 31, 48, 233 Google Calendar, 58 Google Docs, 58 Google Glass, 16, 27, 41 Google Plus, 50 real name policy of, 49 surveillance by, 48 Google stalking, 230 Gore, Al, 53 government: checks and balances in, 100, 175 surveillance by, see mass surveillance, government Government Accountability Office, 30 Government Communications Headquarters (GCHQ): cyberattacks by, 149 encryption programs and, 85 location data used by, 3 mass surveillance by, 69, 79, 175, 182, 234 government databases, hacking of, 73, 117, 313 GPS: automobile companies’ use of, 29–30 FBI use of, 26, 95 police use of, 26 in smart phones, 3, 14 Grayson, Alan, 172 Great Firewall (Golden Shield), 94, 95, 150–51, 187, 237 Greece, wiretapping of government cell phones in, 148 greenhouse gas emissions, 17 Greenwald, Glenn, 20 Grindr, 259 Guardian, Snowden documents published by, 20, 67, 149 habeas corpus, 229 hackers, hacking, 42–43, 71–74, 216, 313 of government databases, 73, 117, 313 by NSA, 85 privately-made technology for, 73, 81 see also cyberwarfare Hacking Team, 73, 81, 149–50 HAPPYFOOT, 3 Harris Corporation, 68 Harris Poll, 96 Hayden, Michael, 23, 147, 162 health: effect of constant surveillance on, 127 mass surveillance and, 16, 41–42 healthcare data, privacy of, 193 HelloSpy, 3, 245 Hewlett-Packard, 112 Hill, Raquel, 44 hindsight bias, 322 Hobbes, Thomas, 210 Home Depot, 110, 116 homosexuality, 97 Hoover, J. Edgar, attempted intimidation of King by, 98, 102–3 hop searches, 37–38 HTTPS Everywhere, 215, 216 Huawei, 74, 86, 182 Human Rights Watch, 96, 178 IBM, 104, 122 iCloud, 58 ICREACH, 67 identification, anonymity and, 131–33 identity theft, 116–19 iMacs, 58 imperfection, systemic, resilience and, 163–64 IMSI-catchers, 68, 165–66 independence, oversight and, 162–63, 169, 177–78 India, 76 individuals, data rights of, 192–93, 200–203, 211, 232 data storage by, 18–19 see also mass surveillance, individual defenses against inferences, from data mining, 34–35, 258, 259 and correlation of data sets, 40–42 error rates in, 34, 54, 136–37, 269 information fiduciaries, 204–5 information middlemen: Internet’s empowering of, 57–58 monopolistic nature of, 57 Information Technology and Innovation Foundation, 121–22 InfoUSA, 53 Initiate Systems, 41 Instagram, 58 intelligence community, US, 67 budget of, 64–65, 80 fear and, 228 international partnerships of, 76–77 private contractors in, 80, 228 revolving door in, 80 see also specific agencies Internal Revenue Service, US (IRS), 137, 159 International Association of Privacy Professionals, 124 International Principles on the Application of Human Rights to Communications Surveillance, 167, 168–69 International Telecommunications Union, 106, 187 Internet: anonymity on, 43–44, 131–33 benefits of, 8 commons as lacking on, 188–89 cyberattacks on, see cyberwarfare deliberate insecurity of, 7, 146–50, 182 early history of, 119 fee-based vs. ad-based business model of, 50, 56, 206 freedom of, 107, 188 government censorship and control of, 94–95, 106–7, 187–88, 237 identification and, 131–33 information middlemen and, see information middlemen international nature of, 6–7, 187–88, 209, 220–21 laws and, 220–21 as media source, 15 physical wiring of, 64 privacy and, 203–4, 230–31 traditional corporate middlemen eliminated by, 56–57 trust and, 181–82 Internet companies, transparency reports of, 207–8 Internet Movie Database, 43 Internet of Things, 15–17 Internet searches, NSA collection of data on, 22 Internet surveillance, 47–51 advertising and, see advertising, personalized cable companies and, 48–49 cookies and, 47–48, 49 global, 69–71 NSA and, 62, 64–65, 78, 122, 149–50, 188, 207 ubiquity of, 32 see also mass surveillance, corporate iPads, 58 iPhones, 31, 42, 58 Iran: government surveillance in, 71–72 Stuxnet cyberattack on, 75, 132, 146, 150 Iraq War, 65 IRC, 119 Israel: mass surveillance by, 182 Stuxnet cyberattack by, 75, 132, 146, 150 US intelligence data shared with, 77 Israeli assassination team, identification of, 43 ISS (Intelligence Support Systems) World, 81 iTunes store, 57 Jawbone, 16 Jay-Z, 48 Joint Terrorism Task Forces, 69 journalists, government surveillance and, 96 JPMorgan Chase, 116 judiciary, surveillance and, 168, 170, 179–80 justice, as core American value, 230 Justice Department, US, 184, 186 Kerry, John, 101 keyboard loggers, 25 key escrow, 120–21 keyword searches, 28, 261 Kindle, 28, 59 King, Martin Luther, Jr., 237 Hoover’s attempted intimidation of, 98, 102–3 Kinsey, Alfred, database of, 44 Klein, Mark, 250, 288 Kunstler, James, 206 Kurds, 76 Lanier, Jaron, 201 Lavabit, 83–84, 209 law enforcement, state and local: abuse of power by, 135, 160 IMSI-catchers used by, 68 location data and, 2, 243 militarization of, 184 predictive algorithms used by, 98–99, 100, 137, 159 racism in, 184 secrecy of, 100, 160 transparency and, 170 lawyers, government surveillance and, 96 legal system: as based on human judgment, 98–99 government surveillance and, 168, 169 secrecy and, 100 Lenddo, 111, 113 Level 3 Communications, 85 Levison, Ladar, 84 liberty: commons and, 189 as core American value, 230 social norms and, 227 liberty, government surveillance and, 6, 91–107, 184 abuses of power in, 101–5, 160, 234–35 anonymity and, 133 censorship and, 94–95, 106–7, 187–88 and changing definition of “wrong,” 92–93, 97–98 discrimination and, 103–4 fear and, 4, 7, 95–97, 135, 156–57, 171, 182–83, 222, 226, 227–30 Internet freedom and, 106–7, 188 political discourse and, 97–99 secrecy and, 99–101 security and, 135, 157–59, 361–62 ubiquitous surveillance and, 92, 97 Library of Congress, 199 Libya, 81 license plate scanners, 26–27, 40 storage of data from, 36 lifelogging, 16 Lincoln, Abraham, 229 Little Brother (Doctorow), 217 location data, 1–3, 28, 39, 62, 243, 339 advertising and, 39–40 de-anonymizing with, 44 embedded in digital photos, 14–15, 42–43 selling of, 2 Locke, John, 210 Los Angeles Police Department, 160 LOVEINT, 102, 177 Lower Merion School District, 104 LulzSec hacker movement, 42 MAC addresses, 29 MacKinnon, Rachel, 210, 212 Madrid Privacy Declaration (2009), 211–12 Magna Carta, information age version of, 210–12 manipulation, surveillance-based, 113–16 Manning, Chelsea, 101 marijuana use, 97 MARINA, 36 Mask, The, 72 Massachusetts Group Insurance Commission, 263 mass surveillance: algorithmic-based, 129–31, 159, 196 as automated process, 5, 129–31 dangers of, 4–5, 6 economic harms from, 6–7, 121–22, 151 false positives in, 137, 138, 140, 323–24 fatalism and, 224–25 lack of consent in, 5, 20, 51 metadata in, 20–23 minimum necessary, 158–59, 176, 211 moratorium urged on new technologies of, 211 noticing, 223 security harmed by, 7, 146–50 social norms and, 226–38 society’s bargains with, 4, 8–9, 47, 49–51, 58–59, 60–61, 158, 226, 235–38 speaking out about, 223–24 targeted surveillance vs., 5, 26, 139–40, 174, 179–80, 184, 186 transparency and, 159–61, 169, 170–71, 176 ubiquity of, 5, 26–28, 32, 40, 53, 92, 97, 224, 233 urgency of fight against, 233–35 see also data collection; data mining mass surveillance, corporate, 46–61, 86–87 advertising and, see advertising, personalized business competitiveness and, 119–24 cost of, to US businesses, 123–24 customers as products in, 53, 58 customer service and, 47 data brokers and, see data brokers discrimination and, 109–13 error rates in, 54 feudal nature of, 58–59, 61, 210–12 free services and convenience exchanged for, 4, 49–51, 58–59, 60–61, 226, 235–36 growth of, 23–24 harms from, 108–18 lobbying and, 233 manipulation and, 113–16 manipulation through, 6 market research and, 47 privacy breaches and, 116–18, 142, 192, 193–95 secrecy and, 194 see also mass surveillance, public-private partnership in mass surveillance, corporate, solutions for, 7, 190–212 accountability and liability in, 192, 193–95, 196–97, 202 data quality assurance and, 181, 192, 194, 202 government regulation in, 192, 196–99, 210 individual participation and, 192 and limits on data collection, 191, 192, 199–200, 202, 206 and limits on data use, 191, 192, 194, 195–97, 206 lobbying and, 209, 222–23 and resistance to government surveillance, 207–10 and respect for data context, 202 rights of individuals and, 192, 200–203, 211 salience and, 203–4 security safeguards and, 192, 193–95, 202, 211 specification of purpose and, 192 transparency and, 192, 194, 196, 202, 204, 207–8 mass surveillance, government, 5–6, 62–77 chilling effects of, 95–97 in China, 70, 86, 140, 209 cloud computing and, 122 corporate nondisclosure agreements and, 100 corporate resistance to, 207–10 cost of, 91 cost of, to US businesses, 121–23 democracy and, 6, 95, 97–99 discrimination and, 4, 6, 93 encryption technology and, 119–23 fear-based justification for, 4, 7, 95–97, 135, 156–57, 171, 182–83, 222, 226, 227–30, 246 fishing expeditions in, 92, 93 in France, 79 fusion centers in, 69, 104 gag orders in, 100, 122 geopolitical conflicts and, 219–20 global, 69–71 growth of, 24–25 hacking in, 71–74 as harmful to US global interests, 151 as ineffective counterterrorism tool, 137–40, 228 international partnerships in, 76–77, 169 lack of trust in US companies resulting from, 122–23, 181–83 liberty and, see liberty, government surveillance and location data used in intimidation and control by, 2 mission creep and, 104–5 oversight and accountability in, 161–63, 169 in Russia, 70, 187, 188, 237 mass surveillance, government ( continued) secrecy of, 99–101, 121, 122 subversion of commercial systems in, 82–87 in UK, 69, 79 US hypocrisy about, 106 see also mass surveillance, public-private partnership in; specific agencies mass surveillance, government, solutions for, 7, 168–89 adequacy and, 168 and breakup of NSA, 186–87 due process and, 168, 184 illegitimate access and, 169, 177 integrity of systems and, 169, 181–82 international cooperation and, 169, 180, 184 judicial authority and, 168, 179–80 legality and, 168, 169 legitimacy and, 168 limitation of military role in, 185–86 lobbying and, 222 “Necessary and Proportionate” principles of, 167, 168–69 necessity and, 168 oversight and, 169, 172–78 proportionality and, 168 separation of espionage from surveillance in, 183–84 targeted surveillance and, 179–80, 184, 186 transparency and, 169, 170–71, 176 trust and, 181–83 user notification and, 168 whistleblowers and, 169, 178–79 mass surveillance, individual defenses against, 7, 213–25 avoidance in, 214 blocking technologies in, 214–17 breaking surveillance technologies, 218–19 distortion in, 217–18 fatalism as enemy of, 224–25 political action and, 213, 222–24, 237–38 mass surveillance, public-private partnership in, 6, 25, 78–87, 207 government subversion of commercial systems in, 82–87 nondisclosure agreements and, 100 privately-made technology in, 81–82, 100 sale of government data in, 79–80 and value neutrality of technology, 82 material witness laws, 92 McCarthyism, 92–93, 229, 234 McConnell, Mike, 80 McNealy, Scott, 4 media: fear and, 229 pre-Internet, 15 medical devices, Internet-enabled, 16 medical research, collection of data and, 8 Medtronic, 200 memory, fallibility of, 128, 320 Merkel, Angela, 151, 160–61, 183, 184 metadata, 216 from cell phones, see cell phone metadata data vs., 17, 23, 35, 251 from Internet searches, 22–23 in mass surveillance, 20–23, 67 from tweets, 23 Michigan, 2, 39 Microsoft, 49, 59–60, 84, 148, 221, 272, 359 customer loyalty to, 58 government demands for data from, 208, 359 increased encryption by, 208 transparency reports of, 207 Mijangos, Luis, 117 military, US: ban on domestic security role of, 185–86 Chinese cyberattacks against, 73 “Don’t Ask Don’t Tell” policy of, 197 drone strikes by, 94 see also Army, US; Cyber Command, US; Defense Department, US MINARET, 175 Minority Report (film), 98 mission creep, 104–5, 163 Mitnick, Kevin, 116 Moglen, Eben, 95, 318 money transfer laws, 35–36 Monsegur, Hector, 42 Mori, Masahiro, 55 MS Office, 60 Multiprogram Research Facility, 144 Muslim Americans, government surveillance of, 103–4 MYSTIC, 36 Napolitano, Janet, 163 Narent, 182 narrative fallacy, 136 Nash equilibrium, 237 Natanz nuclear facility, Iran, 75 National Academies, 344 National Counterterrorism Center, 68 National Health Service, UK, 79 National Institute of Standards and Technology (NIST), proposed takeover of cryptography and computer security programs by, 186–87 National Reconnaissance Office (NRO), 67 National Security Agency, US (NSA): backdoors inserted into software and hardware by, 147–48 Bermuda phone conversations recorded by, 23 “Black Budget” of, 65 cell phone metadata collected by, 20–21, 36, 37, 62, 138, 339 “collect” as defined by, 129, 320 “collect it all” mentality of, 64–65, 138 COMSEC (communications security) mission of, 164–65, 346 congressional oversight of, 172–76 “connect-the-dots” metaphor of, 136, 139 cost to US businesses of surveillance by, 121–22, 151 counterterrorism mission of, 63, 65–66, 184, 222 counterterrorism successes claimed by, 325 cryptanalysis by, 144 cyberattacks by, 149–50 drug smugglers surveilled by, 105 economic espionage by, 73 encryption programs and, 85–86, 120–21 encryption standards deliberately undermined by, 148–49 expanding role of, 24, 165 FISA Amendments Act and, 174–75, 273 foreign eavesdropping (SIGINT) by, 62–63, 76, 77, 122–23, 164–65, 186, 220 Germany surveilled by, 76, 77, 122–23, 151, 160–61, 183, 184 Gmail user data collected by, 62 historical data stored by, 36 history of, 62–63 inadequate internal auditing of, 303 innocent people surveilled by, 66–67 insecure Internet deliberately fostered by, 146–50, 182 international partnerships of, 76–77 Internet surveillance by, 22, 62, 64–65, 78, 86–87, 122–23, 149–50, 188, 207 keyword searches by, 38, 261 legal authority for, 65–66 location data used by, 3, 339 Multiprogram Research Facility of, 144 Muslim Americans surveilled by, 103 parallel construction and, 105, 305 Presidential Policy Directives of, 99–100 PRISM program of, 78, 84–85, 121, 208 proposed breakup of, 186–87 QUANTUM program of, 149–50, 329–30 relationship mapping by, 37–38 remote activation of cell phones by, 30 secrecy of, 99–100, 121, 122 SIGINT Enabling Project of, 147–49 Snowden leaks and, see Snowden, Edward SOMALGET program of, 65 Syria’s Internet infrastructure penetrated by, 74, 150 Tailored Access Operations (TAO) group of, 72, 85, 144, 149, 187 UN communications surveilled by, 102, 183 National Security Agency, US (NSA) ( continued) Unitarian Church lawsuit against, 91 US citizens surveilled by, 64, 66, 175 US global standing undermined by, 151 Utah Data Center of, 18, 36 vulnerabilities stockpiled by, 146–47 National Security Letters (NSLs), 67, 84, 100, 207–8 Naval Criminal Investigative Service, 69 Naval Research Laboratory, US, 158 Nest, 15–16 Netcom, 116 Netflix, 43 Netsweeper, 82 New Digital Age, The (Schmidt and Cohen), 4 newsgroups, 119 New York City Police Department, 103–4 New York State, license plate scanning data stored by, 36 New York Times, Chinese cyberattack on, 73, 132, 142 New Zealand, in international intelligence partnerships, 76 Nigeria, 81 9/11 Commission Report, 139, 176 Nineteen Eighty-Four (Orwell), 59, 225 NinthDecimal, 39–40 NIST, see National Institute of Standards and Technology Nixon, Richard, 230 NOBUS (nobody but us) vulnerabilities, 147, 181 Nokia, 81 nondisclosure agreements, 100 North, Oliver, 127–28 Norway, 2011 massacre in, 229–30 NSA, see National Security Agency, US Oak Ridge, Tenn., 144 Obama, Barack, 33, 175 NSA review group appointed by, 176–77, 181 Obama administration: Internet freedom and, 107 NSA and, 122 whistleblowers prosecuted by, 100–101, 179 obfuscation, 217–18 Occupy movement, 104 Ochoa, Higinio (w0rmer), 42–43 OECD Privacy Framework, 191–92, 197 Office of Foreign Assets Control, 36 Office of Personnel Management, US, 73 Off the Record, 83, 215 Olympics (2014), 70, 77 Onionshare, 216 openness, see transparency opt-in vs. opt-out consent, 198 Orange, 79 Orbitz, 111 Organized Crime Drug Enforcement Task Forces, 69 Orwell, George, 59, 225 oversight, of corporate surveillance, see mass surveillance, corporate, solutions for, government regulation in oversight, of government surveillance, 161–63, 169, 172–78 Oyster cards, 40, 262 packet injection, 149–50 PageRank algorithm, 196 Palmer Raids, 234 Panetta, Leon, 133 panopticon, 32, 97, 227 panoptic sort, 111 parallel construction, 105, 305 Pariser, Eli, 114–15 Parker, Theodore, 365 PATRIOT Act, see USA PATRIOT Act pen registers, 27 Peoria, Ill., 101 personalized advertising, see advertising, personalized personally identifying information (PII), 45 Petraeus, David, 42 Petrobras, 73 Pew Research Center, 96 PGP encryption, 215, 216 photographs, digital, data embedded in, 14–15, 42–43 Pirate Party, Iceland, 333 Placecast, 39 police, see law enforcement, state and local police states, as risk-averse, 229 political action, 7, 213, 222–24, 237–38 political campaigns: data mining and, 33, 54 personalized marketing in, 54, 115–16, 233 political discourse, government surveillance and, 97–99 politics, politicians: and fear of blame, 222, 228 technology undermined by, 213 Posse Comitatus Act (1878), 186 Postal Service, US, Isolation Control and Tracking program of, 29 Presidential Policy Directives, 99–100 prices, discrimination in, 109–10 PRISM, 78, 84–85, 121, 208 privacy, 125–33 algorithmic surveillance and, 129–31, 204 as basic human need, 7, 126–27 breaches of, 116–18, 192, 193–95 as fundamental right, 67, 92, 126, 201, 232, 238, 318, 333, 363–64 of healthcare data, 193 Internet and, 203–4, 230–31 loss of, 4, 7, 50–51, 96, 126 and loss of ephemerality, 127–29 “nothing to hide” fallacy and, 125 and proposed Consumer Privacy Bill of Rights, 201, 202 security and, 155–57 social norms and, 227, 230–33 third-party doctrine and, 67–68, 180 as trumped by fear, 228 undervaluing of, 7–8, 50, 156, 194, 203–4 Privacy and Civil Liberties Oversight Board, 176, 177 privacy enhancing technologies (PETs), 215–16, 217 Privacy Impact Notices, 198, 211 probable cause, 184 Protect America Act (2007), 275 public-private partnership, see mass surveillance, public-private partnership in Qualcomm, 122 QUANTUM packet injection program, 149–50, 329–30 radar, high-frequency, 30 “ratters,” 117 Reagan, Ronald, 230 redlining, 109 Red October, 72 Regulation of Investigatory Powers Act (UK; 2000), 175 relationships, mapping of, 37–38 remote access Trojans (RATs), 117 resilience, systemic imperfections and, 163–64 retailers, data collected by, 14, 24, 51–52 revenge porn, 231 RFID chips, 29, 211 Richelieu, Cardinal, 92 rights, of consumers, see consumer rights risk, police states as averse to, 229 risk management, 141–42 Robbins, Blake, 104 robotics, 54–55 Rogers, Michael, 75 Roosevelt, Franklin D., 229, 230 Rousseff, Dilma, 151 RSA Security, 73, 84 rule of law, 210, 212 Russia: cyberwarfare and, 180 mandatory registration of bloggers in, 95 mass surveillance by, 70, 187, 188, 237 salience, 203–4 San Diego Police Department, 160 Sarkozy, Nicolas, 96 Saudi Arabia, 76, 187, 209 Saudi Aramco, 75 Schmidt, Eric, 4, 22, 57, 86, 125 schools, surveillance abuse in, 104 Schrems, Max, 19, 200 search engines, business model of, 113–14, 206 secrecy: corporate surveillance and, 194 of government surveillance, 99–101, 121, 122, 170–71 legitimate, transparency vs., 332–33 security, 135–51 airplane, 93, 158 attack vs. defense in, 140–43 balance between civil liberties and, 135 complexity as enemy of, 141 cost of, 142 data mining as unsuitable tool for, 136–40 and deliberate insecurity of Internet, 146–50 encryption and, see encryption fear and, 4, 7, 95–97, 135, 156–57, 171, 182–83, 222, 226, 227–30 hindsight and, 136 mass surveillance as harmful to, 7, 146–50 and misguided focus on spectacular events, 135 narrative fallacy in, 136 privacy and, 155–57 random vs. targeted attacks and, 142–43 risk management and, 141–42 social norms and, 227 surveillance and, 157–59 vulnerabilities and, 145–46 security cameras, see surveillance technology self-censorship, 95 Senate, US, Intelligence Committee of, 102, 172, 339 Sensenbrenner, Jim, 174 Sense Networks, 2, 40 September 11, 2001, terrorist attacks, 63, 65, 136, 156, 169, 184, 207, 227, 229 SHAMROCK, 175 Shirky, Clay, 228, 231 Shutterfly, 269 Siemens, 81 SIGINT (signals intelligence), see National Security Agency, US, foreign eavesdropping by SIGINT Enabling Project, 147–49 Silk Road, 105 Skype, 84, 148 SmartFilter, 82 smartphones: app-based surveillance on, 48 cameras on, 41 as computers, 14 GPS tracking in, 3, 14, 216–17 MAC addresses and Bluetooth IDs in, 29 Smith, Michael Lee, 67–68 Snowden, Edward, 177, 178, 217 e-mail of, 94 Espionage Act and, 101 EU Parliament testimony of, 76 NSA and GCHQ documents released by, 6, 20, 40–41, 62, 65, 66, 67, 72, 74, 78, 96, 99–100, 121, 129, 144, 149, 150, 160–61, 172, 175, 182, 207, 223, 234, 238 Sochi Olympics, 70, 77 Socialists, Socialism, 92–93 social networking: apps for, 51 customer scores and, 111 customer tracking and, 123 data collected in, 200–201 government surveillance of, 295–96 see also specific companies social norms: fear and, 227–30 liberty and, 227 mass surveillance and, 226–38 privacy and, 227, 230–33 security and, 227 software: security of, 141, 146 subscription vs. purchase models for, 60 Solove, Daniel, 93 SOMALGET, 65 Sophos, 82 Sotomayor, Sonia, 95, 342 South Korea, cyberattack on, 75 spy gadgets, 25–26 SSL encryption, 85–86 SSL (TLS) protocol, 215 Standard Chartered Bank, 35–36 Staples, 110 Stasi, 23 Steinhafel, Gregg, 142 strategic oversight, 162, 172–77 StingRay surveillance system, 100, 165 Stross, Charles, 128 Stuxnet, 75, 132, 146 collateral damage from, 150 Supreme Court, US, 26, 180, 361–62 third-party doctrine and, 68 surveillance: automatic, 31–32 benefits of, 8, 190 as business model, 50, 56, 113–14, 206 cell phones as devices for, 1–3, 14, 28, 39, 46–47, 62, 100, 216–17, 219, 339 constant, negative health effects of, 127 cost of, 23–26 espionage vs., 170, 183–84 government abuses of, 101–5 government-on-government, 63, 73, 74, 75, 76, 158 hidden, 28–30 legitimate needs for, 219–20 as loaded term, 4 mass, see mass surveillance oversight and accountability in, 161–63, 169, 172–78 overt, 28, 30 perception of, 7–8 personal computers as devices for, 3–4, 5 politics and, 213 pre-Internet, 64, 71 principles of, 155–66 targeted, see targeted surveillance transparency and, 159–61, 169, 170–71, 176 surveillance technology: cameras, 14, 17, 31–32 cost of, 25–26 shrinking size of, 29 Suspicious Activity Reports (SAR), 138 Sweeney, Latanya, 44, 263–64 SWIFT banking system, 73 Swire, Peter, 160 Syria, 81 NSA penetration of Internet infrastructure in, 74, 150 System for Operative Investigative Measures (SORM; Russia), 70 tactical oversight, 162, 177–79 Tailored Access Operations group (TAO), 72, 85, 144, 149, 187 Taleb, Nassim, 136 Target, 33, 34, 55 security breach of, 142, 193 targeted advertising, see advertising, personalized targeted surveillance: mass surveillance vs., 5, 26, 139–40, 174, 179–80, 184, 186 PATRIOT Act and, 174 tax fraud, data mining and, 137 technology: benefits of, 8, 190–91 political undermining of, 213 privacy enhancing (PETs), 215–16, 217 see also surveillance technology telephone companies: FBI demands for databases of, 27, 67 historical data stored by, 37, 67 NSA surveillance and, 122 transparency reports of, 207–8 see also cell phone metadata; specific companies Teletrack, 53 TEMPORA, 79 Terrorism Identities Datamart Environment, 68, 136 terrorists, terrorism: civil liberties vs., 135 government databases of, 68–69 as justification for mass surveillance, 4, 7, 170–71, 226, 246 mass surveillance as ineffective tool for detection of, 137–40, 228 and NSA’s expanded mission, 63, 65–66 terrorists, terrorism ( continued) overly broad definition of, 92 relative risk of, 332 Uighur, 219, 287 uniqueness of, 138 see also counterterrorism; security; September 11, 2001, terrorist attacks thermostats, smart, 15 third-party doctrine, 67–68, 180 TLS (SSL) protocol, 215 TOM-Skype, 70 Tor browser, 158, 216, 217 Torch Concepts, 79 trade secrets, algorithms as, 196 transparency: algorithmic surveillance and, 196 corporate surveillance and, 192, 194, 196, 202, 207–8 legitimate secrecy vs., 332–33 surveillance and, 159–61, 169, 170–71, 176 Transparent Society, The (Brin), 231 Transportation Security Administration, US (TSA), screening by, 136, 137, 159, 231, 321 Treasury, US, 36 Truman, Harry, 62, 230 trust, government surveillance and, 181–83 truth in lending laws, 196 Tsarnaev, Tamerlan, 69, 77, 139 Turkey, 76 Turla, 72 Twitter, 42, 58, 199, 208–9 metadata collected by, 23 Uber, 57 Uighur terrorists, 219, 287 Ukraine, 2, 39 Ulbricht, Ross (Dread Pirate Roberts), 105 “uncanny valley” phenomenon, 54–55 Underwear Bomber, 136, 139 UN High Commissioner on Human Rights, 96 Unit 8200, 77 United Kingdom: anti-discrimination laws in, 93 data retention law in, 222 GCHQ of, see Government Communications Headquarters in international intelligence partnerships, 76 Internet censorship in, 95 license plate scanners in, 27 mission creep in, 105 Regulation of Investigatory Powers Act (2000) of, 175 United Nations: digital privacy resolution of, 232, 363–64 NSA surveillance of, 102, 183 United States: data protection laws as absent from, 200 economic espionage by, 73 Germany’s relations with, 151, 234 intelligence budget of, 64–65, 80 NSA surveillance as undermining global stature of, 151 Stuxnet cyberattack by, 75, 132, 146, 150 Universal Declaration of Human Rights, 232 USA PATRIOT Act (2001), 105, 221, 227 Section 215 of, 65, 173–74, 208 Section 505 of, 67 US Cellular, 177 Usenet, 189 VASTech, 81 Verint, 2–3, 182 Verizon, 49, 67, 122 transparency reports of, 207–8 Veterans for Peace, 104 Vigilant Solutions, 26, 40 Vodafone, 79 voiceprints, 30 vulnerabilities, 145–46 fixing of, 180–81 NSA stockpiling of, 146–47 w0rmer (Higinio Ochoa), 42–43 Wall Street Journal, 110 Wanamaker, John, 53 “warrant canaries,” 208, 354 warrant process, 92, 165, 169, 177, 180, 183, 184, 342 Constitution and, 92, 179, 184 FBI and, 26, 67–68 NSA evasion of, 175, 177, 179 third-party doctrine and, 67–68, 180 Watson, Sara M., 55 Watts, Peter, 126–27 Waze, 27–28, 199 weapons of mass destruction, overly broad definition of, 92, 295 weblining, 109 WebMD, 29 whistleblowers: as essential to democracy, 178 legal protections for, 162, 169, 178–79, 342 prosecution of, 100–101, 178, 179, 222 Wickr, 124 Wi-Fi networks, location data and, 3 Wi-Fi passwords, 31 Wilson, Woodrow, 229 Windows 8, 59–60 Wired, 119 workplace surveillance, 112 World War I, 229 World War II, 229 World Wide Web, 119, 210 writers, government surveillance and, 96 “wrong,” changing definition of, 92–93 Wyden, Ron, 172, 339 XKEYSCORE, 36 Yahoo, 84, 207 Chinese surveillance and, 209 government demands for data from, 208 increased encryption by, 208 NSA hacking of, 85 Yosemite (OS), 59–60 YouTube, 50 Zappa, Frank, 98 zero-day vulnerabilities, 145–46 NSA stockpiling of, 146–47, 180–81 ZTE, 81 Zuckerberg, Mark, 107, 125, 126 Praise for DATA AND GOLIATH “Data and Goliath is sorely needed.

 

pages: 589 words: 147,053

The Age of Em: Work, Love and Life When Robots Rule the Earth by Robin Hanson

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8-hour work day, artificial general intelligence, augmented reality, Berlin Wall, bitcoin, blockchain, brain emulation, business process, Clayton Christensen, cloud computing, correlation does not imply causation, demographic transition, Erik Brynjolfsson, ethereum blockchain, experimental subject, fault tolerance, financial intermediation, Flynn Effect, hindsight bias, job automation, job satisfaction, Just-in-time delivery, lone genius, Machinery of Freedom by David Friedman, market design, meta analysis, meta-analysis, Nash equilibrium, new economy, prediction markets, rent control, rent-seeking, reversible computing, risk tolerance, Silicon Valley, smart contracts, statistical model, stem cell, Thomas Malthus, trade route, Turing test, Vernor Vinge

For example, we might like a story about a future where people work very few hours per week, as a way to indirectly comment on current changes in work hours. But as most events described here are not projections of current trends, this book is less useful for this purpose. Considering what our best theories suggest about future societies can also help us to test these theories. Today, we social scientists too easily succumb to hindsight bias and assume that the patterns we see around us are clearly implied by our theories of how society works. Thinking about future societies where such patterns are much less visible can force us to consider more carefully what our theories about how the world works actually imply. Such a thought experiment can help us to calibrate the confidence we should place in these theories, and to spot theoretical holes that we might work to fill.

In such a simulation, almost everything else about the situation could be held constant. Spurs could also be used to test for biases. Today, psychologists show common biases by randomly splitting experimental subjects into subgroups that are given different prompts. For example, a question might be worded two different ways, resulting in different answers on average. Or an “I knew it all along” hindsight bias might be shown via telling different subgroups different outcomes, and asking subjects what chance they would have assigned before to seeing their chosen outcome. Because of random fluctuations that influence individual decisions, however, such experiments today usually require large groups of experimental subjects to see subtle effects. In contrast, em spurs could directly demonstrate such biases in individuals, and not just in large groups.

 

pages: 410 words: 114,005

Black Box Thinking: Why Most People Never Learn From Their Mistakes--But Some Do by Matthew Syed

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Alfred Russel Wallace, Arthur Eddington, Atul Gawande, Black Swan, British Empire, call centre, Captain Sullenberger Hudson, Checklist Manifesto, cognitive bias, cognitive dissonance, conceptual framework, corporate governance, credit crunch, deliberate practice, double helix, epigenetics, fear of failure, fundamental attribution error, Henri Poincaré, hindsight bias, Isaac Newton, iterative process, James Dyson, James Hargreaves, James Watt: steam engine, Joseph Schumpeter, Lean Startup, meta analysis, meta-analysis, minimum viable product, quantitative easing, randomized controlled trial, Silicon Valley, six sigma, spinning jenny, Steve Jobs, the scientific method, Thomas Kuhn: the structure of scientific revolutions, too big to fail, Toyota Production System, Wall-E, Yom Kippur War

See also medicine adoption rate and, 56–57 cognitive dissonance and, 87–90, 103–7 complexity and, 137 culture of, 16, 49–50, 53, 54–55, 57, 58–59, 105–6 error and, 16–19, 31–32, 49–52, 87–90 nurse unit administration, and blame, 226–27, 230–31 scientific approach to learning from failure and, 49–52 Hearst Foundation, 55–56 Henderson, Mark, 157 heroism, 39, 40 Hidden, Anthony, 232n hierarchy, 25, 28–29, 30, 49–50, 103–7 Hilbert, David, 202 Hilfiker, David, 17, 106 hindsight bias, 232n HIV, 147–49 Holtz-Eakin, Douglas, 97 House of Commons Public Administration Select Committee, 55 Houston, Drew, 138, 142–43, 145 Hume, David, 44 Hungry Ghosts, Mao’s Secret Famine (Becker), 110 iatrogenic injury, 17 illusion of design, 129 incentive, 53–54 Incognito: The Secret Lives of Brains (Eagleman), 200 Industrial Revolution, 132–33 inferiority complex, 43–44 inflation, 95–96 initiation experiment, 75–76, 86–87 Innocence Project, 69, 77–78, 81, 84, 85 reforms and, 115, 116–17 innovation.

*One issue that was never fully resolved with Libyan Arab Airlines Flight 114 is why, according to the pilot of one of the Israeli Phantoms, all the window shades were down. It seems almost certain that, with pressure high and time limited, the pilot did not notice that some of the shades were, in fact, up. *As estimated by how often the nursing units were intercepting errors before they became consequential, and other key variables governing self-correction and learning. *“Hindsight bias,” another well-studied psychological tendency, also plays a role here. Once we know the outcome of an event—a patient has died, a plane has crashed, an IT system has malfunctioned—it is notoriously difficult to free one’s mind from that concrete eventuality. It is tough to put oneself in the shoes of the operator, who is often acting in high-pressure circumstances, trying to reconcile different demands, and unaware of how a particular decision might pan out.

 

pages: 272 words: 19,172

Hedge Fund Market Wizards by Jack D. Schwager

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asset-backed security, backtesting, banking crisis, barriers to entry, Bernie Madoff, Black-Scholes formula, British Empire, Claude Shannon: information theory, cloud computing, collateralized debt obligation, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, diversification, diversified portfolio, family office, financial independence, fixed income, Flash crash, hindsight bias, implied volatility, index fund, James Dyson, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, quantitative easing, quantitative trading / quantitative finance, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Sharpe ratio, short selling, statistical arbitrage, Steve Jobs, systematic trading, technology bubble, transaction costs, value at risk, yield curve

For example, it makes sense that price-derived data series, such as volatility or price acceleration, might provide important information. The list of secondary variables derived from price is the part I built manually. Then I have a framework for combining the secondary variables in all sorts of combinations to see what works. I wanted to hand that work off to the computer, but I knew how important it was to have the hindsight bias and overfit problem figured out. As an aside, I am still trying to reverse engineer some of the models that we have come up with that are so interesting and amazing. What do these patterns say about the psychology of the marketplace? Frankly, I’m not sure yet. You are constructing models by selecting combinations of secondary variables formed from a list of hundreds of possible secondary variables.

Although data mining techniques can uncover patterns in data that would be impossible for humans to find empirically or by prior hypotheses, it can also identify meaningless patterns that are nothing more than chance occurrences or the product of flaws in the analytical process. When searching very large numbers of combinations of past price data for patterns, it is easy to come up with many patterns that worked well in the past simply by chance, but have no predictive value. This common pitfall of applying data mining to price data is the reason why the term often has derogatory connotations in reference to trading systems. 6To avoid hindsight bias error in developing trading systems, the available past data is segmented into seen data (i.e., “in-sample”) that is used for system development and unseen data (i.e., “out-of-sample”) that is used for system testing. Any results on the in-sample data are ignored because they are hindsight-biased. Although segmenting the data to reserve unseen data for testing is a necessary condition to avoid misleading results, it is not a sufficient condition as Woodriff goes on to explain. 7In 2011, QIM changed the exact calculation it used to reduce leverage during periods of poor performance, but the new formulation was similar in both conceptual and practical terms.

 

pages: 309 words: 95,495

Foolproof: Why Safety Can Be Dangerous and How Danger Makes Us Safe by Greg Ip

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Affordable Care Act / Obamacare, Air France Flight 447, air freight, airport security, Asian financial crisis, asset-backed security, bank run, banking crisis, Bretton Woods, capital controls, central bank independence, cloud computing, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, Daniel Kahneman / Amos Tversky, diversified portfolio, double helix, endowment effect, Exxon Valdez, financial deregulation, financial innovation, Financial Instability Hypothesis, floating exchange rates, full employment, global supply chain, hindsight bias, Hyman Minsky, Joseph Schumpeter, Kenneth Rogoff, London Whale, Long Term Capital Management, market bubble, moral hazard, Network effects, new economy, offshore financial centre, paradox of thrift, pets.com, Ponzi scheme, quantitative easing, Ralph Nader, Richard Thaler, risk tolerance, Ronald Reagan, savings glut, technology bubble, The Great Moderation, too big to fail, transaction costs, union organizing, Unsafe at Any Speed, value at risk

She passionately believes that the culture of how mistakes are dealt with is critical to safety. In aviation, the fear of disaster is a powerful motivator, she says, quoting a Japanese peer: “If you think you are safe, you are dangerous. If you think you are dangerous, you are safe.” Near-miss reporting is qualitatively different from accident reporting. Since by definition no accident occurred, it is free of “hindsight bias,” the tendency to assume a certain explanation since you already know the outcome. Near misses also occur much more frequently than accidents and thus are more likely to generate patterns worthy of action. Anonymity is central to the system; incident reporters need to know their candor will not get them disciplined or sued. Thus, every report that comes into ASRS is examined by at least two investigators, who are usually retired pilots or controllers.

 

pages: 261 words: 86,905

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

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asset allocation, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamonds, Bretton Woods, BRICs, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collective bargaining, credit crunch, Credit Default Swap, crony capitalism, Dava Sobel, David Graeber, disintermediation, double entry bookkeeping, en.wikipedia.org, estate planning, financial innovation, Flash crash, forward guidance, Gini coefficient, global reserve currency, high net worth, High speed trading, hindsight bias, income inequality, inflation targeting, interest rate swap, Isaac Newton, Jaron Lanier, joint-stock company, joint-stock limited liability company, Kodak vs Instagram, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, margin call, McJob, means of production, microcredit, money: store of value / unit of account / medium of exchange, moral hazard, neoliberal agenda, New Urbanism, Nick Leeson, Nikolai Kondratiev, Nixon shock, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, Plutocrats, plutocrats, Ponzi scheme, purchasing power parity, pushing on a string, quantitative easing, random walk, rent-seeking, reserve currency, Richard Feynman, Richard Feynman, road to serfdom, Ronald Reagan, Satoshi Nakamoto, security theater, shareholder value, Silicon Valley, six sigma, South Sea Bubble, sovereign wealth fund, Steve Jobs, The Chicago School, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, trickle-down economics, Washington Consensus, working poor, yield curve

The fact that people don’t always behave rationally may not come as news in the wider world, but the intellectual challenge provided to conventional economics by behavioral economics is big and important. It’s also a field that offers useful takeaways for the ordinary person, because you can catch yourself doing some of the things described by behavioral economists, such as loss aversion and “hindsight bias,” i.e., the tendency to explain things that happened in terms of how they turned out, rather than how they seemed at the time. Some practical applications of behavioral economics are in fields such as the “nudge,” which involves prompting individuals to behave in a certain way. The prompting is usually on the part of businesses or governments. Some of this is benign, some less so. Example: a famous-to-economists finding in behavioral economics concerns pricing, and the fact that people have a provable bias towards the middle of three prices.

 

pages: 412 words: 115,266

The Moral Landscape: How Science Can Determine Human Values by Sam Harris

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Albert Einstein, banking crisis, cognitive bias, endowment effect, energy security, experimental subject, framing effect, hindsight bias, impulse control, John Nash: game theory, loss aversion, meta analysis, meta-analysis, out of africa, pattern recognition, placebo effect, Ponzi scheme, Richard Feynman, Richard Feynman, risk tolerance, stem cell, Stephen Hawking, Steven Pinker, the scientific method, theory of mind, ultimatum game, World Values Survey

This result fits very well with our own, as the uncertainty provoked by our stimuli would have taken the form of “ambiguity” rather than “risk.” 36. There are many factors that bias our judgment, including: arbitrary anchors on estimates of quantity, availability biases on estimates of frequency, insensitivity to the prior probability of outcomes, misconceptions of randomness, nonregressive predictions, insensitivity to sample size, illusory correlations, overconfidence, valuing of worthless evidence, hindsight bias, confirmation bias, biases based on ease of imaginability, as well as other nonnormative modes of thinking. See Baron, 2008; J. S. B. T. Evans, 2005; Kahneman, 2003; Kahneman, Krueger, Schkade, Schwarz, & Stone, 2006; Kahneman, Slovic, & Tversky, 1982; Kahneman & Tversky, 1996; Stanovich & West, 2000; Tversky & Kahneman, 1974. 37. Stanovich & West, 2000. 38. Fong et al., 1986/07. Once again, asking whether something is rationally or morally normative is distinct from asking whether it has been evolutionarily adaptive.

 

pages: 416 words: 118,592

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

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accounting loophole / creative accounting, Albert Einstein, asset allocation, asset-backed security, backtesting, Bernie Madoff, BRICs, capital asset pricing model, compound rate of return, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, Elliott wave, Eugene Fama: efficient market hypothesis, experimental subject, feminist movement, financial innovation, fixed income, framing effect, hindsight bias, Home mortgage interest deduction, index fund, invisible hand, Isaac Newton, Long Term Capital Management, loss aversion, margin call, market bubble, mortgage tax deduction, new economy, Own Your Own Home, passive investing, pets.com, Ponzi scheme, price stability, profit maximization, publish or perish, purchasing power parity, RAND corporation, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, short selling, Silicon Valley, South Sea Bubble, The Myth of the Rational Market, The Wisdom of Crowds, transaction costs, Vanguard fund, zero-coupon bond

Two behavioral economists, Terrance Odean and Brad Barber, examined the individual accounts at a large discount broker over a substantial period of time. They found that the more individual investors traded, the worse they did. And male investors traded much more than women, with correspondingly poorer results. This illusion of financial skill may well stem from another psychological finding, called hindsight bias. Such errors are sustained by having a selective memory of success. You remember your successful investments. And in hindsight, it is easy to convince yourself that you “knew Google was going to quintuple right after its initial public offering.” People are prone to attribute any good outcome to their own abilities. They tend to rationalize bad outcomes as resulting from unusual external events.

 

pages: 467 words: 154,960

Trend Following: How Great Traders Make Millions in Up or Down Markets by Michael W. Covel

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Albert Einstein, asset allocation, Atul Gawande, backtesting, Bernie Madoff, Black Swan, buy low sell high, capital asset pricing model, Clayton Christensen, commodity trading advisor, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, fiat currency, fixed income, game design, hindsight bias, housing crisis, index fund, Isaac Newton, John Nash: game theory, linear programming, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, mental accounting, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Renaissance Technologies, Richard Feynman, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, systematic trading, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, volatility arbitrage, William of Occam

Abraham Trading Company Annual Performance Breakdown Yearly Statistics Year Return Drawdown 2008 28.77% 2007 19.20% –7.24% 2006 8.93% –9.03% 2005 –10.95% –26.80% (continues) 347 Much of what happens in history comes from “Black Swan dynamics,” very large, sudden, and totally unpredictable “outliers”… Our track record in predicting those events is dismal; yet by some mechanism called the hindsight bias, we think that we understand them… Why are we so bad at understanding this type of uncertainty? It is now the scientific consensus that our risk-avoidance mechanism is not mediated by the cognitive modules of our brain, but rather by the emotional ones. This may have made us fit for the Pleistocene era. “Our risk machinery is designed to run away from tigers; it is not designed for the information-laden modern world.”

 

pages: 471 words: 124,585

The Ascent of Money: A Financial History of the World by Niall Ferguson

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Admiral Zheng, Andrei Shleifer, Asian financial crisis, asset allocation, asset-backed security, Atahualpa, bank run, banking crisis, banks create money, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, BRICs, British Empire, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, collateralized debt obligation, colonial exploitation, Corn Laws, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, deglobalization, diversification, diversified portfolio, double entry bookkeeping, Edmond Halley, Edward Glaeser, Edward Lloyd's coffeehouse, financial innovation, financial intermediation, fixed income, floating exchange rates, Fractional reserve banking, Francisco Pizarro, full employment, German hyperinflation, Hernando de Soto, high net worth, hindsight bias, Home mortgage interest deduction, Hyman Minsky, income inequality, interest rate swap, Isaac Newton, iterative process, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, Kenneth Rogoff, knowledge economy, labour mobility, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, means of production, Mikhail Gorbachev, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, mortgage tax deduction, Naomi Klein, Nick Leeson, Northern Rock, pension reform, price anchoring, price stability, principal–agent problem, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, quantitative hedge fund, RAND corporation, random walk, rent control, rent-seeking, reserve currency, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, seigniorage, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spice trade, structural adjustment programs, technology bubble, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, too big to fail, transaction costs, value at risk, Washington Consensus, Yom Kippur War

A loss has about two and a half times the impact of a gain of the same magnitude.10 This ‘failure of invariance’ is only one of many heuristic biases (skewed modes of thinking or learning) that distinguish real human beings from the homo oeconomicus of neoclassical economic theory, who is supposed to make his decisions rationally, on the basis of all the available information and his expected utility. Other experiments show that we also succumb too readily to such cognitive traps as:1. Availability bias, which causes us to base decisions on information that is more readily available in our memories, rather than the data we really need; 2. Hindsight bias, which causes us to attach higher probabilities to events after they have happened (ex post) than we did before they happened (ex ante); 3. The problem of induction, which leads us to formulate general rules on the basis of insufficient information; 4. The fallacy of conjunction (or disjunction), which means we tend to overestimate the probability that seven events of 90 per cent probability will all occur, while underestimating the probability that at least one of seven events of 10 per cent probability will occur; 5.

 

pages: 687 words: 189,243

A Culture of Growth: The Origins of the Modern Economy by Joel Mokyr

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Andrei Shleifer, barriers to entry, Berlin Wall, clockwork universe, cognitive dissonance, Copley Medal, David Ricardo: comparative advantage, delayed gratification, deliberate practice, Deng Xiaoping, Edmond Halley, epigenetics, Fellow of the Royal Society, financial independence, framing effect, germ theory of disease, Haber-Bosch Process, hindsight bias, income inequality, invention of movable type, invention of the printing press, invisible hand, Isaac Newton, Jacquard loom, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, John Harrison: Longitude, Joseph Schumpeter, knowledge economy, labor-force participation, land tenure, law of one price, Menlo Park, moveable type in China, new economy, phenotype, price stability, principal–agent problem, rent-seeking, Republic of Letters, Ronald Reagan, South Sea Bubble, statistical model, the market place, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, transaction costs, ultimatum game, World Values Survey, Wunderkammern

It is perhaps telling that although a considerable number of Chinese inventions and techniques found their way to the West in one form or another, there are comparatively fewer instances of Chinese propositional knowledge (not to mention science proper) being adopted in Europe. As noted, the growing consensus that characterized Enlightenment Europe was a mechanistic view of the universe. There were fixed and clear rules by which nature operated, and humankind’s challenge was to discover these knowable rules and take advantage of them. Yet the view that these differences somehow handicapped the Chinese and caused a “failure” can be criticized as an example of the hindsight bias that just because Europe created what became known as “modern science,” this was the only way that technological progress and economic growth could have occurred. Evolutionary theory suggests that the actual outcomes we observe are but a small fraction of the outcomes that are feasible, and we simply have no way of imagining how Chinese useful knowledge would have evolved in the long run had it not been exposed to Western culture and whether it would not have produced a material culture comparable to the one produced by the European Industrial Enlightenment.

 

pages: 701 words: 199,010

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini

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affirmative action, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bernie Madoff, Black-Scholes formula, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, labour mobility, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low skilled workers, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, Northern Rock, Occupy movement, oil shock, price stability, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

The examiner had documents from Lehman and from numerous third parties and government agencies, including the Department of the Treasury, the SEC, the Federal Reserve, FRBNY, the Office of Thrift Supervision, the SIPA Trustee, Ernst & Young, J.P. Morgan, Barclays, Bank of America, HSBC, Citibank, Fitch, Moody’s, S&P, and others. In total, the examiner collected more than five million documents and interviewed many of the principal people at Lehman, the Fed, the Treasury, and other institutions. Unfortunately, many of these interviews were done ex-post, so the material may have hindsight bias. 15. Lehman Brothers had subsidiaries that focused on mortgage origination, such as BNC Mortgage Inc. and Aurora Loan Services, LLC. 16. “Double down” comes from the gambling game blackjack. In blackjack, after receiving the first card, a player can double the initial bet by committing to accept only one more card. This is a typical move for a player who has a card with a face value of 10 showing.