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Asymptotic Learning with Ambiguous Information

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  • Pëllumb Reshidi
  • João Thereze
  • Mu Zhang

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.

Suggested Citation

  • Pëllumb Reshidi & João Thereze & Mu Zhang, 2025. "Asymptotic Learning with Ambiguous Information," American Economic Journal: Microeconomics, American Economic Association, vol. 17(3), pages 244-288, August.
    • Handle: RePEc:aea:aejmic:v:17:y:2025:i:3:p:244-88
    DOI: 10.1257/mic.20230142
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    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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