Rationalizing Dynamic Choices

Abstract

An analyst observes an agent take a sequence of actions. The analyst does not have access to the agent’s information and ponders whether the observed actions could be justified through a rational, Bayesian model. We show that the observed actions cannot be justified if and only if there is a single deviation argument that leaves the agent better off, regardless of the information. The result is then extended to allow for distributions over possible action sequences, thereby characterizing the full empirical content of the Bayesian model. Four applications are presented: monotonicity of rationalization with risk aversion, a test of the Bayesian model as applied in behavioral economics, feasible outcomes in dynamic information design, and partial identification of preferences without assumptions on information.

Keywords: dynamic choice, rationalize, sequential information, deviation rule, dominance

JEL Classification: D8, D9