Asymptotic Learning with Ambiguous Information
研究了决策者在信息源精度存在模糊性时如何渐近学习，发现其估计通常有偏，且微小模糊可能导致大误差，对理解观察他人行动时的过度或不足反应有启示。
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.