Financial markets reveal what investors think about the future, and prediction markets are used to forecast election results. Could markets also encourage people to reveal private information, such as subjective judgments (e.g., “Are you satisfied with your life?”) or unverifiable facts? This paper shows how to design such markets, called Bayesian markets. People trade an asset whose value represents the proportion of affirmative answers to a question. Their trading position then reveals their own answer to the question. The results of this paper are based on a Bayesian setup in which people use their private information (their “type”) as a signal. Hence, beliefs about others’ types are correlated with one’s own type. Bayesian markets transform this correlation into a mechanism that rewards truth telling. These markets avoid two complications of alternative methods: they need no knowledge of prior information and no elicitation of metabeliefs regarding others’ signals.

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Keywords Bayesianism, Economic incentives, Mechanism design, Prediction markets, Truth telling
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Series ERIM Top-Core Articles
Journal Proceedings of the National Academy of Sciences of the United States of America
Baillon, A. (2017). Bayesian markets to elicit private information. Proceedings of the National Academy of Sciences of the United States of America, 114(30), 7958–7962. doi:10.1073/pnas.1703486114