The Wisdom of a Confused Crowd: Model Based Inference

In this Economics and Computer Science seminar Larry Samuelson, from Yale discusses his paper on The Wisdom of a Confused Crowd: Model Based Inference

Abstract 

"Crowds" are often regarded as ``wiser'' than individuals, and prediction markets are often regarded as effective methods for harnessing this wisdom.  If the agents in prediction markets are Bayesians who share a common model and prior belief, then the no-trade theorem implies that we should see no trade in the market.  But if the agents in the market are not Bayesians who share a common model and prior belief, then it is no longer obvious that the market outcome aggregates or conveys information.  In this paper, we examine a stylized prediction market comprised of Bayesian agents whose inferences are based on different models of the underlying environment.  We explore a basic tension---the differences in models that give rise to the possibility of trade generally preclude the possibility of perfect information aggregation.