Abstract

The market mechanisms proposed in Predicting Crime offer many virtues. The authors describe several of these-unbiased information collection; incentives that encourage disclosure; opinions weighted by conviction; information aggregation; instantaneous and continuous feedback-and convincingly argue that these structural features stand to help prediction markets outperform alternative institutions in forecasting the interplay of crime rates and crime polices. In that, Predicting Crime adopts an economic point of view and speaks in terms of practical experience. After all, similar structural features have already appeared in other successful prediction markets, such as those offering trading in claims about the weather, flu outbreaks, or box office returns. By contrast, this Comment adopts a legal point of view and speaks about as-yet theoretical disputes. 

Part I briefly recaps the problematic legal status of prediction markets-especially real-money markets open to the general public-under state and federal law in the United States. Part II explains why, thanks to their substance rather than to their structure, the sort of market described in Predicting Crime would be an especially good candidate for judicial protection from regulatory interference. In contrast to most other prediction markets, prediction markets that forecast crime rates and the success or failure of changes to criminal policy evaluate one of the core functions of government: protecting citizens from criminal activity. The information that can be gleaned from these markets makes them a powerful means of praising or criticizing the performance of government officials. That unique aspect of prediction markets in crime rates and policies gives them comparatively strong claims to First Amendment protection from political interference. Part III of this Comment concludes, as does Predicting Crime, with a modest proposal: this one describes how Cass Sunstein, as the newly confirmed head of the Office of Information and Regulatory Affairs, could encourage federal agencies to experiment with prediction markets in general and prediction markets in crime rates and policies in particular.

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