The Wisdom of a Confused Crowd:Model-Based Inference

George Mailath and Larry Samuelson ()
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Larry Samuelson: Department of Economics, Yale University

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

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.

Keywords: Wisdom of the Crowd; Information aggregation; Common prior; NonBayesian updating (search for similar items in EconPapers)
Pages: 58 pages
Date: 2019-01-16
New Economics Papers: this item is included in nep-mic
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Citations: View citations in EconPapers (1)

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Working Paper: The Wisdom of a Confused Crowd: Model-Based Inference (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:19-001

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