Learning and Disagreement in an Uncertain World

Daron Acemoglu, Victor Chernozhukov and Muhamet Yildiz ()

No 48, Carlo Alberto Notebooks from Collegio Carlo Alberto

Abstract: Most economic analyses presume that there are limited differences in the prior beliefs of individuals, an assumption most often justified by the argument that sufficient common experiences and observations will eliminate disagreements. We investigate this claim using a simple model of Bayesian learning. Two individuals with di.erent priors observe the same infinite sequence of signals about some underlying parameter. Existing results in the liter- ature establish that when individuals know the interpretation of signals, under very mild conditions, there will be asymptotic agreementtheir assessments will eventually agree. In contrast, we look at an environment in which individuals are uncertain about the inter- pretation of signals, meaning that they have non-degenerate probability distributions over the conditional distribution of signals given the underlying parameter. When priors on the parameter and the conditional distribution of signals have full support, we show the following: (1) Individuals will never agree, even after observing the same infinite sequence of signals. (2) Before observing the signals, they believe with probability 1 that their posteri- ors about the underlying parameter will fail to converge. (3) Observing the same (infinite) sequence of signals may lead to a divergence of opinion rather than the typically-presumed convergence. We then characterize the conditions for asymptotic agreement under “approx- imate certainty”–i.e., as we look at the limit where uncertainty about the interpretation of the signals disappears. When the family of probability distributions of signals given the parameter has rapidly-varying tails (such as the normal or the exponential distributions), approximate certainty restores asymptotic agreement. However, when the family of proba- bility distributions has regularly-varying tails (such as the Pareto, the log-normal, and the t-distributions), asymptotic agreement does not obtain even in the limit as the amount of uncertainty disappears. We also discuss how lack of common priors implied by the type of learning in this paper interacts with economic behavior in various different situations, including games of common interest, coordination, asset trading and bargaining.

Keywords: asymptotic disagreement; Bayesian learning; merging of opinions. (search for similar items in EconPapers)
JEL-codes: C11 C72 D83 (search for similar items in EconPapers)
Pages: 57 pages
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Working Paper: Learning and Disagreement in an Uncertain World (2006)
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