maker has learned? The key constraint we impose, which is shared across models of Bayesian learning, is that any learning … Dean 2015) and the NIAS condition (Caplin and Martin 2015) to allow for arbitrary learning. We apply our framework to show … how identification of what was learned can be strengthened with additional assumptions on the form of Bayesian learning …

We provide semiparametric identification results for a broad class of learning models in which continuous outcomes …

learning and policy recommendation in advisory committees. We quantify the effectiveness of advisory panels, and evaluate …