featured on New York Times Freakanomics Blog

December 23rd, 2009

Please check out Professor Ayres’s post on the New York Times Freakanomics Blog, discussing the wisdom of the crowds and, titled Prediction Markets vs. Super Crunching: Which Can Better Predict How Justice Kennedy Will Vote?

One of the great unresolved questions of predictive analytics is trying to figure out when prediction markets will produce better predictions than good old-fashion mining of historic data. I think that there is fairly good evidence that either approach tends to beat the statistically unaided predictions of traditional experts.

But what is still unknown is whether prediction markets dominate statistical prediction. (Freakonomics co-blogger Justin Wolfers, in a sense, is on both sides of this debate. Justin is one of the best crunchers of historic data, and even more, he is at the cutting edge of exploiting the results of prediction markets).

Thanks to Josh Blackmun [sic], we are about to have a test of these two competing approaches. Blackmun [sic] has organized a cool Supreme Court fantasy league, where anybody can make predictions about how Supreme Court justices will vote on particular cases. The aggregate prediction of the league members is powerful “wisdom of the crowds” information.

Thanks to Josh’s creation, we’ll be able to sit back — paying particular attention to instances of disagreement — and see over time which approach makes the better predictions. This single experiment will not, by itself, resolve the larger “which is better” debate — in part, because I could imagine putting forward stronger market-based and statistical-based predictions. The fantasy league predictions would probably be more accurate if market participants had to actually put their money behind their predictions (as with And the statistical predictions could probably be improved if they relied on more recent data and controlled for more variables.

I am a HUGE Freakanomics fan, and just finished reading Super Freakanomics. This, is really cool.