Prediction Markets, Again

I am a great fan of the literature on prediction markets and of their potential for policy innovation.  Two recent articles are well worth reading for their insights on prediction markets.  The first is an article from The New York Times: "Betting to Improve the Odds," available here.  The second is a working paper from the University of Chicago Law School: Todd Henderson, Justin Wolfers, & Eric Zitzewitz, "Predicting Crime," available from SSRN here.  Here's an abstract of the Henderson, Wolfers, & Zitzewitz article:

    Prediction markets have been proposed for a variety of public policy purposes, but no one has considered their application in perhaps the most obvious policy area: crime. This paper proposes and examines the use of prediction markets to forecast crime rates and the impact on crime from changes to crime policy, such as resource allocation, policing strategies, sentencing, postconviction treatment, and so on. We make several contributions to the prediction markets and crime forecasting literature.
    First, we argue that prediction markets are especially useful in crime rate forecasting and criminal policy analysis, because information relevant to decisionmakers is voluminous, dispersed, and difficult to process efficiently. After surveying the current forecasting practices and techniques, we examine the use of standard prediction markets—such as those being used to predict everything from the weather to political elections to flu outbreaks—as a method of forecasting crime rates of various kinds.
    Second, we introduce some theoretical improvements to existing prediction markets that are designed to address specific issues that arise in policy-making applications, such as crime rate forecasting. Specifically, we develop the idea of prediction market event studies that can be used to test the influence of policy changes, both real and hypothetical, on crime rates. Given the high costs of changing policies, say issuing a moratorium on the death penalty or lowering mandatory minimum sentences for certain crimes, these markets provide a useful tool for policy makers operating under uncertainty.
    These event studies and the other policy markets we propose face a big hurdle, however, because predictions about the future imbed assumptions about the very policy choices they are designed to measure. We offer a method by which policy makers can interpret market forecasts in a way that isolates or unpacks underlying crime factors from expected policy responses, even when the responses are dependent on the crime factors.
    Finally, we discuss some practical issues about designing these markets, such as how to ensure liquidity, how to structure contracts, and the optimal market scope. We conclude with a modest proposal for experimenting with markets in this policy area. 

TSU