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 with a known utility function. 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. Four applications are presented: monotonicity of rationalization with risk aversion, a potential rejection of the Bayesian model with observable data, feasible outcomes in dynamic information design, and partial identification of preferences without assumptions on information.
Cite
@article{arxiv.2504.05251,
title = {Rationalizing dynamic choices},
author = {Henrique de Oliveira and Rohit Lamba},
journal= {arXiv preprint arXiv:2504.05251},
year = {2025}
}