[Computational Complexity] Predictions Markets Map of Senate Races
David Pennock, Yiling Chang and I created a Prediction Markets Map of the 2006 US Senates Elections. When you load the map it downloads the latest prices from Tradesports and colors each state appropriately, a mix of red (Republican), blue (democrats) and green (other candidates). We created the map because predictions based on market prices tend to do better than experts or polls. As Wisdom of Crowds author James Surowiecki wrote recently in the New Yorker
In the past few years, a host of prediction markets, as they're usually called, have appeared online, offering people the chance to speculate on subjects ranging from the box-office performance of Hollywood films to the outcome of Presidential elections and the spread of bird flu. These markets' forecasts have proved remarkably accurate—just as bettors collectively do an exceptionally good job of predicting sports results. (In 2004, for instance, Tradesports, a Dublin-based prediction market, called thirty-three out of thirty-four races in the Senate correctly, and called all fifty states correctly in the results for the electoral college.)Scaling colors according to probabilities is a surprisingly hard problem. It relates to human perception, we want our eyes to distinguish colors related to probabilities that are not that close. For the map I used a transformation based on the XYZ color space that seems to give reasonable though far from perfect results. Even small projects like this can really bring up complex issues from outside the theory world.
Posted by Lance to Computational Complexity at 9/28/2006 08:54:00 AM