Asymptotic Learning With Ambiguous Information

Reshidi, P毛llumb; Thereze, Jo茫o; Zhang, Mu

doi:10.1257/mic.20230142

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鈥� actions may over- or underreact to information.

蝌蚪网

蝌蚪网 Journal:
Microeconomics