Reduction of Heuristics and Biases
Consistent reporting of prediction market predictions and trends could demystify the sports pages, reducing events that require much attention today to mere blips on a market chart. Some prediction markets on TradeSports, for example, predict not the result of individual games but the outcome of entire seasons. For example, before the beginning of the Major League Baseball season, a contract is issued for each baseball team, to pay off only if the team ultimately wins the World Series. The prices at which these contracts are traded thus provide estimates of the probabilities that individual teams will win. Observing the line reflecting the price at which any team trades provides a snapshot of the season as a whole, but the lines may suggest less drama than fans might expect. Sports reporting tantalizes readers with suggestions that a dramatic comeback victory or unexpected collapse might be turning points in a season, but to prediction markets, these are often ho-hum. This provides at least informal support for the proposition that prediction markets might help overcome heuristics and biases that ordinarily might distort human probability estimates. When a team has a winning streak, each of the wins matters in the standings, and one’s estimate of the team’s quality should correspondingly increase. But some fans might believe that streaks are more significant than they are. Conventional wisdom in sports, after all, has long supported the notion that a player with a “hot hand” will be more likely to succeed than the player’s season and career numbers would suggest, but economists appear to have debunked this with statistical research.30 The belief in the hot hand, as well as the related overemphasis among fans of the significance of a streak, is an example of what is sometimes known as the “clustering illusion,”31 a tendency among some humans to see a pattern where none exists, where really there is nothing more than a sequence of independent events. The relative stability of prediction market prices over time provides informal evidence that in part the market overcomes the clustering illusion. The clustering illusion is one of a number of biases that may affect individual probability estimates. Among the most famous is the hindsight bias, the tendency of individuals to see actual outcomes as more likely ex ante than they in fact were.32 Other biases include the primary and recency effects, the tendencies to weigh initial and recent events more than other events. Another fallacy to which sports fans might be particularly susceptible is the illusory correlation bias, the tendency to infer causation from a correlation, for example, giving a new coach credit for a team’s changing fortunes. Many of these biases may be related to what is perhaps the most important bias, the availability error,33 the tendency to place too much weight on events that are easily accessible in memory. A baseball fan, for example, is more likely to remember a player’s dramatic home run than the many instances in which the player grounded out to the shortstop. As a result, the fan might overestimate the probability of another home run. Although a fan knows that a player with a .350 batting average and a .400 on-base percentage is more likely than not to make an out in any given plate appearance, it does not seem that way. Overcoming biases afflicting sports fans might not promote the welfare of news consumers. But overcoming biases can be of greater importance. Timur Kuran and Cass Sunstein have argued that the availability error leads to bad policy consequences and that the media play a major role.34 For example, Kuran and Sunstein claim that the publicity surrounding Journalists generally admit to having at least one bias: a preference for stories with news to stories without news. The local evening news thus reports on several incidents of fire and crime but rarely on people who emerged from a given day comparatively unscathed. The evidence for the availability heuristic suggests that the public as a result will overestimate the probability and prevalence of the events that tend to lead to news reports. The media, of course, sometimes might counteract this tendency by reporting hard facts. Sometimes hard facts will make a less vivid impact than more sensational news stories, but they tend to move individual probability assessments in the right direction. There are not always hard facts to report, however, especially when the media report on the possibility of future risks. If prediction markets help overcome individual cognitive biases, consistent reporting on prediction market predictions might help. The empirical question is the extent to which prediction markets do overcome cognitive biases. Early evidence suggests that prediction markets do not succeed altogether in vanquishing them. Paul Tetlock, for example, analyzed pricing in TradeSports markets to determine whether the market overcame the favorite–long shot bias. He also tested for what is known as the reverse favorite–long shot bias, an observed tendency of baseball and hockey wagering markets to underprice teams that are not favored to win.36 He found both. The favorite–long shot bias dominated for very unlikely events, and the reverse bias occurred in middle probability ranges.37 He also found evidence of “return reversals.”38 That is, after the market initially moves in a particular direction following the appearance of new information, it is likely to move partway in the opposite direction afterwards. This indicates overreaction to new information, which might be attributable to the availability heuristic. Such evidence disproves any claims that prediction markets are completely free of cognitive biases. Nonetheless, it is possible that other methods of deriving consensus probability estimates–such as asking sports fans their opinions–might be more prone to error. There is at least a theoretical reason to believe that prediction markets should help alleviate biases. Individuals with more sophisticated models for assessing probabilities generally have greater self-confidence about their predictions and thus greater willingness to place bets on prediction markets when those models suggest that current prices are inaccurate. Indeed, some sophisticated market players might focus consciously on the specific types of cognitive errors that are likely to produce poor probability assessments and trade against them. Tetlock’s analysis suggests a particular betting strategy to take advantage of the reverse favorite–long shot bias that, he estimates, would produce phenomenal positive returns of 10 percent after commissions. The strategy requires buying contracts that have recently experienced bad news and selling those that have recently experienced good news. If Tetlock is correct, then someone who is persuaded by his analysis will implement this betting strategy, and prediction market pricing should improve. This may not happen overnight, because every trader must worry that others are already trading using the strategy, but it seems likely to occur eventually. If prediction markets do help counteract biases, it will be a long time before media reporting of the results of prediction markets will significantly affect public policy. For starters, only a relatively small number of prediction markets currently exist, at least outside the area of sports. These may do little to correct important public misperceptions. The many markets predicting the results of elections, for example, may have little public value aside from testing prediction market mechanisms. Consumers of media might invest a great deal of time reading about election contests, but biases in predictions of the outcomes of such contests likely have relatively little consequence for the world. Ultimately, if the media are to play an important democratic function, it cannot be merely by telling the public who will win elections; it must be also by telling the public what it can expect in the future. Prediction markets forecasting the probabilities of different possible news events might be considerably more useful if the public learned to understand both their abilities and their limitations. For example, figure 1.6 reports the trading prices in prediction markets used to predict whether weapons of mass destruction would be found in More important, at any given time, both the graph of trading prices and the current trading price tells a concise story. A reader, to be sure, might wish to read all of the publicly available information in order to develop an independent prediction of the probability that weapons would be found, and a reader willing to devote a great deal of effort to the task might expect to be able systematically to earn profits by trading on the information on the market. But some readers, if convinced that prediction markets provide at least approximate probability estimates, might prefer to consider the prediction market summary rather than all of the underlying information. When partisans on all sides of contested issues can be expected to seek to sway public opinion by offering confident predictions about the future, the media often have trouble objectively indicating what the expert consensus on the issue is, leaving readers to sort through an awesome quantity of data. These readers will be subject to the usual assortment of heuristics and biases, and they may therefore make systematic errors. By seeking objective means of aggregating expert views, the media can help overcome such errors. Reporting of prediction market results in particular will leave readers not only with probability estimates that in general should be more accurate than their own deductions but also with more time to read analyses of appropriate policy responses.
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