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Asymptotic Learning with Ambiguous Information

Pëllumb Reshidi et al.

American Economic Journal: Microeconomics2025https://doi.org/10.1257/mic.20230142article
AJG 3ABDC A*
Weight
0.40

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 D82, D83)

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https://doi.org/https://doi.org/10.1257/mic.20230142

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@article{pëllumb2025,
  title        = {{Asymptotic Learning with Ambiguous Information}},
  author       = {Pëllumb Reshidi et al.},
  journal      = {American Economic Journal: Microeconomics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1257/mic.20230142},
}

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Asymptotic Learning with Ambiguous Information

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Evidence weight

0.40

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.