Rationalizing Dynamic Choices
34 Pages Posted: 27 Feb 2019 Last revised: 24 Apr 2019
Date Written: March 29, 2019
Consider an analyst who observes an agent taking a sequence of actions. The analyst ponders whether the sequence of actions observed could have been taken by a rational, Bayesian agent. Although the analyst observes the chosen actions, he does not have direct access to the agent’s information and must therefore consider a multitude of possibilities. Could some gradual release of information have led the agent to optimally take that sequence of actions?
We show that a sequence of actions cannot be rationalized by any information structure if and only if it can be proved to be dominated via a deviation argument. This argument prescribes a way of deviating that would leave the agent better off in any possible scenario, regardless of the information she might have. As an application of this characterization, we show that an increase in the agent’s risk-aversion leads to less predictive power—more sequences of actions can be rationalized. We also show results that simplify the analyst’s search for a deviation argument and demonstrate how these arguments can be used to partially identify utility parameters without making assumptions on the agent’s information.
Keywords: dynamic choice, rationalize, sequential information, deviation rule, true dominance
JEL Classification: D8, D9
Suggested Citation: Suggested Citation