Rationalizable learning

Authors

Andrew Caplin, New York University
Daniel Martin, University of California, Santa Barbara
Philip Marx, Louisiana State University

Document Type

Article

Publication Date

8-1-2025

Abstract

The central question we address in this paper is: what can an analyst infer from choice data about what a decision maker has learned? The key constraint we impose, which is shared across models of Bayesian learning, is that any learning must be rationalizable. We use our framework to show how identification can be strengthened as one imposes the assumptions behind more restrictive forms of Bayesian learning.

Publication Source (Journal or Book title)

Economic Theory

First Page

171

Last Page

202

Recommended Citation

Caplin, A., Martin, D., & Marx, P. (2025). Rationalizable learning. Economic Theory, 80 (1), 171-202. https://doi.org/10.1007/s00199-024-01627-z