Asymptotic Learning with Ambiguous Information

Pëllumb Reshidi, João Thereze and Mu Zhang

American Economic Journal: Microeconomics, 2025, vol. 17, issue 3, 244-88

Abstract: We study asymptotic learning when the decision-maker faces ambiguity in the precision of her information sources. She aims to estimate a state and evaluates outcomes according to the worst-case scenario. Under prior-by-prior updating, we characterize the set of asymptotic posteriors the decision-maker entertains, which consists of a continuum of degenerate distributions over an interval. Moreover, her asymptotic estimate of the state is generically incorrect. We show that even a small amount of ambiguity may lead to large estimation errors and illustrate how an econometrician who learns from observing others' actions may over- or underreact to information.

JEL-codes: D82 D83 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1257/mic.20230142

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