Over the last few years, Intrade — with headquarters in Dublin, where the gambling laws are loose — has become the biggest success story among a new crop of prediction markets. The world’s largest steel maker, Arcelor Mittal, now runs an internal market allowing its executives to predict the price of steel. Best Buy has started a market for employees to guess which DVDs and video game consoles, among other products, will be popular. Google and Eli Lilly have similar markets. The idea is to let a company’s decision-makers benefit from the collective, if often hidden, knowledge of their employees.
I haven't participated in any "prediction market" but past statistical work tells me that within each such market, you'll find say half the participants whose individual track records will be higher than the average. Thus, you can do better than the market average if you can predict the predictors: figure out which ones would drag down your average.
In other words, averaging opinions is a double-edged sword. While some will provide "hidden" knowledge, others may provide "bad" information, which gets averaged too.
In substance, prediction markets are no different from so-called ensemble predictors which have been studied extensively in the statistical data mining area in recent years. I am of the opinion that such things have proven more useful in increasing the stability of error rates than in improving the average error rates themselves.
Phil's take can be read here.