FROM UNCERTAINTY TO RISK?
PROBLEMS WITH USING PRICES IN PREDICTION
MARKETS TO IMPROVE DECISION-MAKING

Ryan Bubb

January 2005

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ii. Policy

An application with potentially significant social welfare ramifications is 
in government decision-making. Government decision-makers must make a vast 
number of decisions under uncertainty, and they typically utilize a number 
of techniques to reduce uncertainty and improve decision-making under 
uncertainty, such as employing intelligence services to gather and analyze 
information, commissioning studies by experts, and requiring administrative 
agencies to follow rules in their decision-making process that allow 
interested parties an opportunity to furnish relevant information. Hanson 
(2004) argues that decision markets can be used to improve estimates of the 
consequences of adopting proposed policies. Consider a policy P that a 
public official is considering adopting. Suppose a critical criterion for 
the decision whether to adopt P is the policy’s expected effect on some 
outcome variable that can be measured by a statistic O (normalized to lie in 
[0, 1]). Thus, the public official would like to estimate the treatment 
effect”: E[O | P] – E[O | not P]. Hanson proposes that this effect be 
measured using the prices of contingent claims traded in a prediction 
market. In particular, consider the following sets of securities, each of 
which pays $1 with certainty:

1. [Pays $1 if P] and [Pays $1 if not P] 
2. [Pays $O if P], [Pays $(1-O) if P], [Pays $O if not P], and 
   [Pays $(1-O) if not P]

Each set of securities could be sold as a bundle by the market-maker for $1 
to seed the market. Then, a market could be created that would allows 
traders to exchange the security [Pays $O if P] for a fraction of the 
security [Pays $1 if P]. The fraction is the market price and represents the 
market estimate for E[O | P]. Similarly, another market could be established 
that would allow traders to exchange [Pays $O if not P] for a fraction of 
[Pays $1 if not P], and the fraction at which contracts trade is the market 
estimate of E[O | not P]. Such an approach, Hanson argues, would effectively 
aggregate information relevant to the estimate and provide an unbiased 
estimate that is likely to be more accurate than conventional approaches.

A recent attempt to employ prediction markets by the U.S. government ended 
in a public relations debacle. The Defense Advanced Research Projects Agency 
(DARPA) funded a program to develop prediction markets that would provide 
forecasts related to various geopolitical issues, such as the likelihood of 
a bioweapons attack against Israel, and the stability of Saudi Arabia 
conditional on a pull-out of U.S. troops from the country. National 
security-related decision-making has been under particular scrutiny in the 
U.S. following the failure of the U.S. intelligence community to anticipate 
and prevent the attacks of September 11, 2001, and the failure of U.S. 
intelligence related to WMD in Iraq.9 Despite this perceived need for 
intelligence reform, prediction markets were judged beyond the pale, and the 
program was shutdown in July 2003 amid outcries from Congress that the 
program allowed people to bet on tragedy.

iii. Business

Prediction markets could also potentially be used by managers to improve 
business decision-making under uncertainty. Chen and Plott (2002) report on 
the use of a prediction market by Hewlett-Packard Corporation to make sales 
projections. Another potential business use is by securities analysts to 
make predictions about events relevant to the valuation of securities, such 
as whether a particular drug will be approved by the FDA.

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