[Michael Abramowicz, guest-blogging, January 31, 2008 at 9:50am] Trackbacks
Deliberating with Prediction Markets:

Prediction markets may seem inadequately deliberative. On the election markets, for example, participants trade, but do not ordinarily explain their trades. Decision makers in deliberative bodies, in contrast, seek to persuade one another.

Group deliberation, however, has its own perils, including the danger that polarization will move a group to extremes, as Cass Sunstein has shown. Sunstein argues in Infotopia that prediction markets might therefore be superior in some contexts to deliberation. A recent study shows better forecasts with prediction markets than with group deliberation.

In some contexts, though, prediction markets might be more useful yet if individual participants explained their forecasts. I’ve proposed a type of prediction market called a deliberative market that can increase incentives that participants have to release information supporting their views. In the deliberative market (see my original paper here and this section of my book), a participant’s profit or loss is determined by the market forecast some time after the participant’s initial prediction, so a participant can earn money only to the extent that others are persuaded in that time frame.

In a post yesterday on the Overcoming Bias blog, Robin Hanson criticizes my argument for not including a robust enough economic model and for allegedly making unrealistic assumptions. In a reply, I maintain that the point is pretty simple, and the math I used was ample to make it. In the comments to my reply, Robin and I come closer to agreeing about the underlying issue of whether the deliberative market increases incentives for information release.

Chris Hibbert, who has developed the robust Zocalo open source prediction market software, meanwhile, makes the sound point that a possible disadvantage of the deliberative approach is that it may stop individuals who are confident of their views but don’t think they can persuade others in the time frame from participating in the market. Sometimes, it might be useful to have both a standard and a deliberative prediction market for the same forecasting problem.

There may be other ways of making prediction markets more transparent. An admittedly more speculative section of the book imagines the “market web,” which can be used to break down problems. For example, an election market might include a node forecasting the possibility of a recession. Changes in this node’s value would automatically affect the value of other nodes, including ultimately the probability that particular candidates would win the election. Such a web could become complicated very quickly, but it could allow a group to produce a consensus model of a complex phenomenon.