Preface
“Wanna bet on that?” This playground challenge attests to the sincerity of the questioner. Acceptance of the challenge attests to the sincerity of the target as well. A refusal to bet, meanwhile, suggests insincerity. In the absence of any information about the merits of the dispute, at least, such a refusal gives onlookers some reason to favor the challenger’s claim. The actions of others who hear the challenge can also give information to onlookers. An onlooker who has information about the matter could intervene, offering to take the bet. Should that not occur, then it will be apparent to all that one person in the group projects greater confidence than anyone else. It may still be that this confidence is a bluff or that it is built on misinterpretation and mistake. But it would be difficult to construct a better procedure for identifying the most sincere belief. And over the run of questions and challenges, sincere beliefs tend to be more accurate than insincere ones. Prediction markets, sometimes called information markets, idea futures, or virtual stock markets, are elaborations of this simple device for identifying sincere belief. By insisting that individuals back their views with money, they eliminate cheap talk. At bottom, the simplest form of prediction market is a forum for repeated challenges of this type. Someone who thinks that an event has more than a 75 percent chance of occurring offers to place three quarters against the single quarter of any challenger. Someone else who thinks that an event has less than a 25 percent chance of occurring would have a financial incentive to take that bet. The acceptance of such a bet indicates the apparent existence of genuine disagreement. Still more useful than such bets in conveying probability estimates to the outside world are the challenges that go unanswered. Suppose that no one responds to the challenge described above. Meanwhile, someone who thinks that the event has a less than 80 percent chance of occurring offers to place $0.20 on the event’s not occurring against anyone else’s $0.80. No one responds to this offer, either, not even the first challenger. There are then two unanswered challenges. No one is sufficiently confident that the event has a less than 75 percent chance or a more than 80 percent chance of occurring to take either of the bets. One could infer no one sincerely believes that the probability is outside the 75 percent to 80 percent range. At the least, anyone who initially would have guessed the probability to be outside that range does not think it sufficiently clearly outside that range to be worth accepting a challenge. The actual probability, based on information known to potential bettors, thus may be between 75 percent and 80 percent. A probability of 77.5 percent might be a good guess, if a precise number is required. Prediction markets do not merely show that those who make bets are sincere but identify the most committed predictors in a group. At any time, these are the predictors who have made forecasts that have not yet been challenged. Admittedly, the most committed challenger will not always be the most sincere, but at least he or she will appear to others to be the most sincere. For if it is obvious that a challenge is bluster, perhaps intended to influence a decision or simply to make a point, others can be expected to counter the challenge. This is especially so for prediction markets in which anyone is permitted to participate. If, after repeated challenges, no one agrees to accept a bet and meet a challenge, then even those who initially had different views must have developed sufficient doubt that they no longer think it in their interest to bet. In part, prediction market participants revise their views on the basis of the willingness of others to advance contrary positions. In a recent book titled The Wisdom of Crowds James Surowiecki identifies a variety of contexts, including prediction markets, in which crowds perform better than their best members.1 The strongest case for a prediction market, however, is not that it simply aggregates knowledge by averaging beliefs. Prediction markets do tend to allow individuals within a crowd to learn about the beliefs of others, but this will not always be important. There are some contexts in which the errors of individual intuition tend to cancel each other out, but there are others in which errors are correlated and the masses are wrong. What a prediction market excels at is identifying the wisdom in crowds, identifying the individual challenger (or group) that is so committed to a particular view that no one remains willing to accept a challenge. This will not always be the individual who in fact has the most wisdom and knowledge. But the procedures of prediction markets generally do a better job of identifying that person than do the alternatives. This book argues that organizations in the private and the public sectors ought to use this procedure, as well as more sophisticated procedures building on this basic design. Prediction markets can serve as general-purpose prediction mechanisms, and although one can debate how they best should be designed, they generally are superior to alternative procedures for generating predictions not involving direct financial incentives. A counterargument is that someone who learns of the results of a prediction market might still be able to improve on the prediction by scrutinizing the evidence directly, and so institutions, too, ought to make a prediction market forecast just one of many decision-making inputs. Someone who does independent research, however, could participate in a market and change its forecasts in that way. A refusal to do so suggests lack of sincerity, especially if the market is heavily subsidized to make predictions attractive. Even if there is a plausible alternative explanation for such a refusal–perhaps someone with information has a low tolerance for risk–there are reasons to prefer market predictions to those of individuals. In general, individuals who are asked to make predictions as part of a decision-making process have many conflicting incentives. One incentive might be to preserve one’s reputation by announcing predictions that turn out to be correct, but other incentives might skew the announcement of a prediction. The predictor may alter a prediction in order to make a desired consequence more likely, modify a genuine prediction to please a third party such as a boss or a friend, or make a prediction on the basis of hopes and fears rather than hard analysis. A prediction market forces someone making a prediction to bear the financial consequences of allowing extrinsic factors such as these to influence a prediction. What is more, it gives third parties incentives to assess the sincerity of the predictor, bringing to bear all their knowledge about the incentives that might be driving that individual. One might still argue that some individuals can be trusted to make accurate predictions yet somehow cannot be induced to participate in a prediction market, and so we might obtain better predictions by relying on these individuals. But developing reliable mechanisms for selecting them is not easy. And even if we could do so, we could induce these individuals to announce predictions and then allow third parties to take these predictions into account in their own prediction market bets. To prefer predictions that are untested by the competition of a market is to believe that only by granting power to designated decision makers can we achieve uncorrupted analysis. A prediction market, then, is a decision-making device for excluding from consideration predictions that no one appears willing to back with money. Only with the development of the Internet has creation of prediction markets become economical, and regulatory obstacles have slowed their development. Nonetheless, as this book documents, prediction markets are becoming increasingly common. Preliminary empirical evidence confirms that they generally make accurate predictions, and if later research confirms this, there is every reason to believe that they will gradually replace alternative means of making predictions. Moreover, prediction markets are sufficiently cheap that they can be used to make predictions that are not now made. Many decisions are made without explicit quantification of various relevant projections. Prediction markets can improve decision making by creating a useful procedure for making those projections explicit. It might appear that prediction (including the implicit kind) is but a fringe element of public and private decision making. This book argues, however, that prediction is central. This is perhaps more obvious in a private entity such as a corporation, where many decisions are aimed at achieving concrete goals, most notably (though not exclusively) making money. Public decision making, in contrast, involves a balancing of various private rights and interests, rather than straightforward maximization of specified variables. Even so, representative government itself can be viewed as an implicitly predictive exercise. We delegate power to legislatures, agencies, and other institutions largely because we believe that the decisions that they reach will accord more closely than alternatives with the decisions that the citizenry as a whole would reach, if only the citizenry had the time to become fully informed about particular issues. We know, however, that the impossibility of all voters’ becoming fully informed on every issue means that the democratic process often fails to achieve this modest goal of aggregating hypothetically informed preferences. Sometimes it fails because relatively uninformed people are given too much of a voice. Policy becomes the voice of the mob. At other times it fails because the public’s lack of information means that appointed decision makers are able to advance their own interests at the expense of the public’s. Policy becomes the voice of special interests. Governmental design can be seen in large part as reflecting attempts to balance these and other problems, to steer decision making to the institutions and individuals who seem likely to come closest to reflecting a hypothetical majority sentiment. More populist institutions, such as juries and referenda, decrease the risk that decision makers will act against the will of the people, but they are costly and increase the risk that decisions will not reflect the best available information and analysis. This helps explain and justify the use of legislative committees, administrative agencies, and courts. By channeling decisions to a relatively small number of individuals with a comparatively large amount of expertise, we ensure that decisions are made by those in the best position to assess the consequences of those decisions. The law of small numbers, however, means that their decisions are not always representative. Institutional design faces a fundamental trade-off between representativeness and expertise. The project of government and political science has proceeded on the reasonable assumption that there is no objective way of ascertaining what the people would decide if only they were fully informed. The technology of prediction markets changes this assumption. Prediction markets can be used to predict, for example, the probability that a randomly selected person, after having an opportunity to consider an issue fully, will conclude that a proposed policy would be beneficial. If market participants do not know who will be selected, they will try to divine what an average decision maker would conclude. The market will help to identify the most sincere predictor, likely to be someone who has a great deal of information about both the underlying issue and the underlying population from which the randomly selected individual will come. Prediction markets do not eliminate the tradeoff between representativeness and expertise altogether; we must still decide, for example, whether to pick a person at random from the population as a whole, or from a smaller body such as the legislature or the judiciary. But relative to alternatives, they enable decisions that are both relatively representative and relatively informed. We will also see that prediction markets can enable more principled decisions. Someone announcing how that person would resolve an issue may be more inclined to consider higher-order principles, such as fairness to minorities and consistency with precedent, when the decision will merely resolve a prediction market, in the sense of determining payouts, than when the decision will actually affect policy. Prediction market evaluations of policy alternatives can thus be faithful to a society’s highest ideals. My aim, of course, is not to suggest that we should discard institutions developed over hundreds of years in favor of unproven alternatives. We should not. But nor should we close our minds to the possibilities of democratic institutions structured in radically different ways from the ones that we now have. If these alternative institutions have advantages, then they can provide useful contributions in particular contexts. This book seeks to describe some relatively modest possible reforms using prediction markets, but it also envisions the most radical possible reforms. Only by comparing the most central of our existing institutions to prediction market alternatives can we identify most clearly the strengths and limitations of prediction markets. For today, this can and should be only an academic exercise; many of the ideas developed are theoretical and untested, although the book also discusses empirical evidence of existing prediction market designs. Much more experimentation would be necessary to refine these designs. We cannot identify alternatives to complex institutions merely by repeated use of playground challenges. This book will show, however, how bets can serve as a foundational alternative to votes in the construction of institutions. For example, a principal goal of many institutions is to encourage deliberation that may sway the votes of, for example, jurors or senators. We will see how prediction markets can similarly encourage deliberation by requiring individuals to bet on whether they will be able to persuade others of their views. Similarly, we will see how combined series of bets can be used to produce not merely numeric predictions but also consensus “texts,” much as governmental procedures lead to the creation of statutes, regulations, and judicial opinions. We will also see how prediction markets can be combined into a kind of “web” so that every assumption and calculation that underlies a prediction or policy evaluation can be explicitly identified and challenged. The prediction market designs described in this book build on one another, with more complex designs generally introduced later in the book than more basic designs. Each chapter focuses primarily on a different institution or group of individuals that could benefit from prediction markets, with hypothetical prediction markets replacing the central institutions of government in the later chapters. The final chapter imagines predictocracy, a form of government in which prediction markets serve as the foundation for all decision making. My intent is not to endorse this form of government, and I occasionally note the dangers associated with abandoning the status quo. Prediction markets offer the possibility of a new way of thinking about structuring decision making, and this approach can be used for problems large and small.
7 Responses to “Preface”
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January 30th, 2008 at 6:48 pm
Shouldn’t this passage:
“Someone who thinks that an event has more than a 75 percent chance of occurring offers to place three quarters against the single quarter of any challenger. Someone else who thinks that an event has less than a 25 percent chance of occurring would have a financial incentive to take that bet. The acceptance of such a bet indicates the apparent existence of genuine disagreement.”
read
“… Someone else who thinks that an event has less than a 75 percent chance of occurring would have a financial incentive to take that bet. …”
instead?
While notion that “one who thinks that event have less then 25% chance of occuring would want to accept the bet” is obviously correct, too, it seems a bit misleading, though
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????????????? A refusal to bet, meanwhile, suggests insincerity…..
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