Journal of Economic Literature (JEL) Code(s)
“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.
Mailath, George J. and Samuelson, Larry, "The Wisdom of a Confused Crowd: Model-Based Inference" (2019). Cowles Foundation Discussion Papers. 102.