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Judgment and Decision Making, Vol. Psychologists typically measure beliefs and preferences using self-reports, whereas economists are much more likely to infer them from behavior. Prediction markets appear to be a victory for the economic approach, having yielded more accurate probability estimates than opinion polls or experts for a wide variety of events, all without ever asking for self-reported beliefs. We conduct the most direct comparison to date of prediction markets to Victory is mine surprisingly self-reports using a within-subject de. Our participants traded on the likelihood of geopolitical events.

Each time they placed a trade, they first had to report their belief that the event would occur on a 0— scale. When ly validated aggregation algorithms were applied to self-reported beliefs, they were at least as accurate as prediction-market prices in predicting a wide range of geopolitical events.

Furthermore, the combination of approaches was ificantly more accurate than prediction-market prices alone, indicating that self-reports contained information that the market did not efficiently aggregate. Combining measurement techniques across behavioral and social sciences may have greater benefits than ly thought.

Behavioral and social scientists have long disagreed over how best to measure mental states. While psychologists clearly value behavioral measures, they quite often measure beliefs and preferences by simply asking people to self-report them on a numerical scale. Perhaps the most impressive demonstration of the power of using revealed beliefs is the resounding success of prediction markets.

Prediction markets create contracts that pay a fixed amount if an event occurs, and then allow people to trade on the contract by submitting buying or selling prices in a manner similar to the stock market. When market participants have some intrinsic interest in trying to predicteven markets with modest incentives or no incentives have been shown to be effective.

As examples, small markets using academics as participants predict which behavioral science experiments will successfully replicate e. Because these probability forecasts are obtained without ever asking anyone to self-report their beliefs, the success of prediction markets appears to be a victory of the economic approach and a repudiation of relying on self-reports.

The classic explanation for why prediction markets are so successful is that they are efficient mechanisms for integrating information useful to making predictions. To see why, suppose that someone had information that suggested an event was much more likely to occur than the current market price suggested.

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That person would now have the incentive to buy the contract because its expected value would greatly exceed its cost. The balance of such beliefs would eventually push the price up. Others might have pieces of information that suggest the event is unlikely, motivating them to sell and putting downward pressure on the price. In the end, the market price will tend to reflect the balance of information that participants have.

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In theory, there should be no information in their self-reports that is not already reflected in the market price. The success of prediction markets appears to support that theory. It is difficult, however, to draw clean inferences from in-the-wild comparisons of prediction markets and self-reports for several reasons.

Traders in markets are necessarily exposed to information from others in the market, such as historical prices, the last price at which shares traded, and the current buy and sell orders. In this way, traders may be working with more information than poll respondents. Further, prediction markets aggregate opinions in a unique way. The market price is thus a marginal opinion that is not simply an average or a vote Victory is mine surprisingly Forsythe et al.

It could be that the magic of prediction markets lies largely in superior aggregation methods rather than superior quality or informativeness of responses. Finally, selection issues could be serious when comparing market participants with poll respondents.

Participation in prediction markets is nearly always self-selected and people who choose to trade might be different by having more intrinsic interest or knowledge or better analytic skills. We had a unique opportunity to compare methods in an experiment that addressed all of these issues and put both approaches on a level playing field. Each time participants wanted to place an order, they were first asked to report their beliefs that an event would occur on a 0 to probability scale.

We then aggregated these self-reports in a pre-determined fashion using best practices gleaned from earlier years of the tournament, such as extremizing the aggregate, weighting recent opinions more heavily than older ones, and weighting forecasters with a good track record more heavily. When aggregated this way, simple self-reports were at least as accurate as market prices for predicting a variety of geopolitical events.

Perhaps more importantly, a combination of prices and self-reports was ificantly better than prices alone, indicating that self-reports contained incrementally useful information that market prices alone did not capture.

But proper aggregation can have only limited benefits if the responses do not contain information. Our suggest that self-reports not only contained useful information, but information that was not efficiently captured by the market price.

Prior research that is perhaps most similar to ours was done by Goel et al.

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Across thousands of American football games, betting markets were found to have only a tiny edge over opinion polls. Across multiple domains, very simple statistical models approached the accuracy of prediction markets, suggesting diminishing returns to information; nearly all predictive power was captured by 2 or 3 parameters.

These studies, however, were not experimental. Different people participated in the polls and markets, raising the inferential problems noted above. Further, it is unclear how general the comparisons between markets and polls were, because they involved American football predictions. Fans of American football are inundated with statistical information and betting lines, and therefore their opinions might be highly correlated with betting markets, which would naturally lead to similar accuracy. Here, we employ different geopolitical Victory is mine surprisingly, usually lasting months and ranging from typical prediction market domains e.

The full list of prediction questions is included in the Appendix. We find a similar increase in accuracy when we combine aggregated self-reports and prediction market prices, which are themselves determined by individual bids. In the prediction market, participants could buy or sell shares of events at prices between 0 andwhere 0 represented the closing value of the shares if the event did not occur and represented the value of the shares if the event did occur. Before they could complete their buy or sell orders, participants also had to report their belief in the probability of the event occurring on a 0 to scale.

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Our experimental de thus permits a better comparison between self-reports and prediction markets because the participants saw the same information — the last trading price and the bid and asking prices in the market — when making trades and self-reporting beliefs. This de also eliminates self-selection concerns because the market participants were randomly ased to the prediction market and other conditions from a larger pool.

Lastly, the same group of participants both made trades and judged probabilities. We recruited forecasters into the larger participant pool from professional societies, research centers, alumni associations, science blogs, and word of mouth. Participants were U. Questions with more than two outcomes raised complications because participants were not asked to give a probability for each outcome, just the one they were betting on.

Security Council before 1 March ? For each question, participants saw the prices and of shares requested for the six highest buy orders and the six lowest sell orders. They could bid or ask for shares at the price that they specified. Whenever participants entered an order, they stated their belief that the event would occur on a 0 certain it will not occur to certain it will occur probability scale before their order could be confirmed.

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Participants were encouraged to return to the Web site and update their predictions at any time until the question closed. Throughout the year, participants could trade on any questions they wished until either the events were resolved or the trading year closed.

Our data included 46, market orders and self-reported beliefs. Of those, we focus primarily on the 37, of those orders that were matched into trades, because those orders contributed directly to market prices. The aggregate was formed using an algorithm Atanasov et al.

The algorithm had three features. First, more recent self-reports were given priority over older ones because questions were open for some time and older self-reports become outdated as new information becomes available. Second, greater weight was ased to the beliefs of forecasters who had a track record of accuracy. For all questions that had resolved as of the date of the opinion, participants were scored for the accuracy of their self-reported beliefs. Their opinions were then given weights ranging from. Third, the aggregate was extremized toward 0 or 1 because measurement error pushes individual estimates toward the middle of a probability scale and because individual estimates neglect the information that is in the other estimates see Baron et al.

We used Baron et al.

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We used an elastic net technique to avoid overfitting. For a more detailed description of this aggregation procedure and the logic behind it, see Atanasov et al. Because participants were primarily recruited to participate in a prediction market and were not incentivized to give accurate self-reports of their beliefs, we first looked for s that these judgments were taken seriously.

This result suggests that the self-report question was taken seriously, but also that the two modes of answering could potentially yield different information, since there is still a substantial amount of unshared variance between the two. If we are to take self-reported probability judgments seriously, one would expect these profit margins to be positive. For example, if a forecaster placed a buy order at a price that was higher than her judged belief e. This pattern suggests that some of these negative-profit trades simply reflect risk aversion on the part of participants who pay a premium to take profit on a trade that has already proven successful relative to the current market price.

Generally, then, we conclude that stated beliefs at least pass the surface test of coherence.

Finally, we can assess the quality of self-reported beliefs by examining their relationship to trading success. All else equal, we would expect that participants whose reported probabilities proved more accurate would also have better trading success. We calculated total earnings from all closed questions after the market season closed. The rank conversion ensured that the distributions were well-behaved in the presence of outliers.

For forecasters who made trades on at least 25 events i. Our goal was to compare the accuracy of prices and self-reported beliefs.

We calculated the Brier score measure of accuracy for each method Brier, For questions with binary outcomes, Brier scores range from 0 to 2, where 0 is best and 2 is worst. The Brier score would be calculated as. On the other hand, if the event did not occur, the Brier score would be.

We averaged scores over days and questions for each method. Table 1 compares Brier scores for Prices and Beliefs. A simple, unweighted average of self-reports yielded a mean Brier score of. We resist this conclusion for a few reasons.