Events at CERGE-EI

Doron Ravid, Ph.D.

University of Chicago, United States

Authors: Doron Ravid and Tommaso Denti

Abstract: We derive robust predictions in games involving flexible information acquisition, also known as rational inattention (Sims, 2003). These predictions remain accurate regardless of the exact specification of players’ learning abilities. Compared to scenarios where information is predetermined, rational inattention reduces welfare and introduces additional constraints on behavior. We show these constraints generically do not bind; the two knowledge regimes are behaviorally indistinguishable in most environments. Yet, we demonstrate the welfare difference they generate is robust: optimal policy depends on whether one assumes information is given or acquired. We provide the necessary tools for policy analysis in this context.

Full Text: Robust Predictions in Games with Rational Inattention