Predictocracy

Market Mechanisms for Public and Private Decision MakingBy MICHAEL ABRAMOWICZ

Yale University Press

Copyright © 2007 Yale University
All right reserved.
ISBN: 978-0-300-11599-4

Contents

Preface…………………………………ixAcknowledgments………………………….xvii1 The Media……………………………..12 Policy Analysts………………………..343 Businesses…………………………….654 Committees…………………………….1005 Regulatory Bodies………………………1376 Administrative Agencies…………………1627 Public Corporations…………………….1948 Courts………………………………..2279 Legislative Bodies……………………..25510 Predictocracy…………………………282Afterword……………………………….311Notes…………………………………..315Index…………………………………..343

Chapter One

The Media

If democratic institutions are tools for translating public opinion into public policy, then the most important institution might be the one that contributes most to forming public opinion in the first place: the media. Constitutional protections for free speech and a free press such as the First Amendment to the U.S. Constitution seem to reflect a view that allowing uncensored speech will promote truth and, indirectly, good governance. Perhaps the most articulate expression of this view is Oliver Wendell Holmes’s famous statement, “The best test of truth is the power of the thought to get itself accepted in the competition of the market.” The metaphor of the marketplace draws its appeal from the premise that competition in the economic sphere generally works, with superior products emerging from the choices of consumers. Yet even those who accept the general claims of such market enthusiasts as Adam Smith and Friedrich Hayek recognize the existence of market failures. Markets for ideas can fail, too, and the result can be broader failures in democratic governance.

Why might markets for ideas fail? A simple answer is that many members of the public will generally be ignorant about particular issues and underlying theoretical frameworks, often rationally so. Acquisition of information requires time and money. Just as economic markets can lead to suboptimal results because of imperfect information,5 so, too, can limited background knowledge make accurate evaluation of new claims about the world extraordinarily difficult. So it is not surprising that surveys indicate that the public’s views about issues often differ from the views of those who might be considered experts. For example, survey evidence suggests that economists generally believe that trade agreements between the United States and other countries have helped create more jobs in the United States, whereas the public generally believes that such agreements have cost American jobs. The direction of this discrepancy should not be surprising. Stories about people losing jobs are more engaging than stories about people gaining jobs from trade, and so some doses of media may make people more ignorant than they would be in the absence of any media.

The media, of course, cannot be expected to fix all cognitive errors among the public. When the media accurately report information, the public may pay little attention. For example, although the media presumably accurately report the identities of congressional candidates, in many elections the vast majority of voters cannot name a single congressional candidate in their district, let alone give information about voting patterns. And if the media could succeed in informing the public with facts of this type, they cannot hope to replace the educational system in providing background knowledge and skills necessary for making relatively informed voting decisions. All is not lost, and some political scientists argue that voters still are able to make rational decisions. Nonetheless, it should not be too idealistic to hope that the media, though constrained by the need to entertain, would generally seek to improve the knowledge of the viewing and reading public.

The media, however, may be hesitant to provide information about the consensus views of experts concerning particular issues because there may be controversy about what the consensus is. Ironically, a desire to promote objectivity sometimes may interfere with attempts to provide accurate information. When an issue is contested, a media outlet perhaps can best achieve the goal of objectivity by presenting both sides of the issue. The public, however, often cannot identify which argument is more true and compelling. Suppose, for example, that a news program about crime features one expert who contends that crime rates in a particular city will soon rise and another who contends that crime rates will soon fall. Even a criminologist or a statistician might need to spend hours evaluating the literature to assess the relative strength of the competing arguments. The public might benefit from a statement by the news program that three-quarters of the experts contacted subscribed to one view. Understandably, however, journalists are reluctant to report the results of such unscientific polls, yet lack the resources to conduct more sophisticated polls of experts, assuming that they could find an objective definition of who should count as an expert.

With regard to some issues, “just the facts” may be all the public needs, but in many cases an important fact may be what the consensus opinion about the issue is or whether such a consensus exists. This chapter suggests that prediction markets can provide objective gauges of expert consensus for the media to pass along to the public. My claim is not that prediction markets provide the best possible predictions, overcoming all irrationality. Indeed, individual experts sometimes might be able to beat markets. But experience suggests that prediction markets are generally fairly accurate, and at least they provide incentives for those who could beat the market to push predictions in a sensible direction.

I begin by describing a relatively simple structure for a prediction market that can be used to estimate the probability that a particular event will occur. After recounting the experiences of perhaps the most famous prediction market, the nonprofit Iowa Electronic Markets (IEM), I describe a more general type of numeric prediction market that the IEM also uses. Although it has focused primarily on predicting the outcomes of elections, prediction markets conceivably can be used to predict virtually any outcome, and the for-profit Web site Tradesports.com has used them to predict the results of athletic contests and a range of other events. A prediction market is not the only possible prediction technology-pari-mutuel wagering has long been shown to produce predictions about horse racing-but this chapter argues that prediction markets offer considerable advantages. They also present unique challenges, and I assess the danger that they might be manipulated. Finally, I suggest how the media might use prediction markets to provide better information to readers and viewers.

THE PROBABILITY ESTIMATE PREDICTION MARKET

Prediction markets may take many forms, and some of the designs for prediction markets that this book considers might not merit being described as markets at all. The term prediction market, however, is descriptively accurate at least as applied to the design most prevalent today, which I call the “probability estimate prediction market.” As the name suggests, its purpose is to produce a certain type of information, in particular, an estimate of the probability of a designated event. In most markets goods or services are exchanged for money, and the price provides a kind of information, specifically about the value of the good being exchanged relative to the value of other goods. The real purpose of the market, however, is to facilitate the exchange, with the price simply a byproduct of the actions of market participants. In a prediction market, by contrast, the exchanges themselves will generally have no economic function (except in some cases for hedging purposes; see Chapter 3). A prediction market’s purpose is to produce a price that the creator of the prediction market finds useful.

Consider, for example, a subject that recently was of great import: whether the actress Angelina Jolie and the actor Brad Pitt will marry. Suppose that Us Weekly wished to provide its readers with the most accurate information possible about the probability that a marriage would result by January 1, 2010. Of course, Us Weekly presumably wants to present its readers with a range of relevant information, such as reports of supposed friends and analyses by body language experts who scrutinize photographs of celebrities as they seek to escape from the paparazzi. But it might also want to provide a concrete number, an estimate of those in the know of the chance that the marriage really will happen. This would be useful both for readers in a hurry and for readers who are unsure of their own ability to weigh the different pieces of evidence. Of course, Us Weekly might simply make up a number, but such numbers might seem arbitrary or sensationalistic. If it wanted to produce a more credible estimate, it might launch a probability estimate prediction market.

Here is how such a prediction market might work, placing aside for now concerns about whether creation of such a market in the United States would be legal (see Chapter 2): Us Weekly would sell two different kinds of tradable contract to the public, a marriage tradable contract and a no-marriage tradable contract. For example, it might hold an on-line auction for one hundred of each type of share. It would promise to issue a fixed payoff, say, one dollar, on January 1, 2010, depending on whether Jolie and Pitt in fact become married by that date. So, should they become the Jolie-Pitts by that date, each marriage tradable contract would be redeemable for one dollar on that date, but the no-marriage tradable contract would be worthless. On the other hand, should Jolie and Pitt not be married to each other by that date, then the no-marriage tradable contract would be redeemable for one dollar on that date, but the marriage tradable contract would be worthless. In announcing the prediction market, Us Weekly presumably would seek to limit the possibility of ambiguities, such as whether a marriage followed by a divorce would count. But if there were ambiguities, then Us Weekly would resolve them either after or before the payoff date.

The initial auction of such tradable contracts itself would produce some information that could help assess the tradable contracts. For example, suppose that the marriage tradable contracts sold for an average of about sixty cents, and the no-marriage tradable contracts sold for an average of about thirty cents. That would seem to indicate that the marriage share is considerably more likely than the no-marriage share to be redeemed for one dollar. Hazarding a specific probability from such information would be difficult, however. Perhaps the purchasers of the marriage tradable contracts are sentimentalists, willing to indulge their romantic notions for a collective total of sixty dollars. This is not a fully satisfactory answer, for it does not explain why the no-marriage tradable contracts did not sell for more. After all, if marriage seemed unlikely, at thirty cents each, the no-marriage shares would have been a bargain, and someone should have had an incentive to bid more for them. But given the inherent uncertainty of celebrity love lives, playing in the market carries some risk and demands some transactions costs, which include at least the time it takes to purchase and redeem shares. These factors can explain why the combined auction prices might be less than one dollar. Meanwhile, the possibility that individuals might obtain pleasure from holding tradable contracts independent of their financial value will tend to push prices up.

Whether the initial tradable contracts are distributed by auction or by some other means, the key to the prediction market is that holders of tradable contracts can sell them. Typically, an on-line exchange serves to facilitate such transactions. Someone interested in purchasing the marriage tradable contract could submit a “bid price,” the price the potential purchaser would be willing to pay for that tradable contract. Someone interested in selling it could submit an “ask price,” the price at which an owner of the tradable contract would be willing to sell it. Whenever a bid or an ask is submitted, the exchange would seek to pair the offer with another. For example, if I offer to sell a marriage share for sixty cents, and then you offer to buy a marriage share for up to sixty-five cents, the on-line exchange could then complete a transaction, presumably at sixty cents, since the offer to sell came first. When an offer does not immediately find a match, it is placed in a queue, with the best offers placed at the front of the queue. At any given time, the on-line exchange maintains a “bid queue” and an “ask queue.” At the front of the bid queue is the most generous offer to buy, and at the front of the ask queue is the most generous offer to sell. The ask price will always be greater than the bid price, because when a new offer matches an existing one, a transaction is immediately completed. In economics, this arrangement is known as a continuous double auction.

Just as the auction prices provide some indication of the probability of the Jolie-Pitt union, so, too, can the bid and ask prices provide some hint. For example, suppose that the bid price is fifty-five cents and the ask price is sixty cents. That would indicate that no one is willing to sell the tradable contract for less than sixty cents but that there are people willing to buy the tradable contract for fifty-five cents. If the event were viewed as having a significantly more than 60 percent chance of occurring, then one would think that someone would be eager to purchase the tradable contract for more than sixty cents. If the event were viewed as having a significantly less than 55 percent chance of occurring, then one would think that someone would be willing to sell for less than fifty-five cents. The midpoint of the bid and ask prices might thus provide at least a rough probability estimate, and this estimate may be averaged with the corresponding estimate from the no-marriage tradable contract. An alternative approach to deriving a probability estimate would be to base it on the most recent transaction or possibly on an average of several recent transactions. Either way, a significant advantage of a prediction market is that it will produce data that change over time as new information becomes available.

THE THEORETICAL CASE FOR PREDICTION MARKETS

Risk and transactions costs might affect not only the amount that individuals will be willing to pay at auction but also their bid and ask prices in the subsequent market. But there is a strong theoretical reason to believe that prices derived from market activity will provide more reliable probability estimates than prices derived from the result of auctions. In an auction, sentimental considerations might easily bid up the marriage tradable contract, but such sentiment is unlikely to last over the long term in a market. If some market participants are merely unsophisticated sentimentalists, then more sophisticated players will bet against them, placing them at a disadvantage. Even if the sentimentalists have purchased all the Jolie-Pitt shares, the market could allow a third party to offer to sell additional shares. Such a third party would merely need to show the ability to pay one dollar per new share if in fact the shares are redeemed. Eventually, the unsophisticated parties will run out of money or, more likely, reach the limits of their celebrity affection. It is one thing to lose a few dollars, another to pour one’s life savings into a bet against eager speculators. The result is that after a while, the bid and ask prices are increasingly likely to be determined by the actions of informed, rather than uninformed, parties.

This reflects a key aspect of prediction markets. Prices do not simply reflect an average assessment by a group but also reflect the degree of confidence that different members of the group have in their estimates. Simply taking the average estimate of a group may work well for some problems but not for others. For example, Francis Galston studied a competition in which contestants guessed the weight of an ox; the average guess of the 787 contestants, 1,197 pounds, was only one pound short of the actual weight. But when Cass Sunstein, a law professor, asked his colleagues to estimate the weight of the fuel that powers space shuttles, they gave a median answer of two hundred thousand pounds, far short of the actual answer of four million pounds. People may on average systematically misestimate certain numbers, and even if a few people in a group know the correct answer, those people may have only a small impact on the average.

(Continues…)

Excerpted from Predictocracyby MICHAEL ABRAMOWICZ Copyright © 2007 by Yale University. Excerpted by permission.
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