Berk-Nash Rationalizability

We introduce Berk--Nash rationalizability, a new solution concept for misspecified learning environments. It parallels rationalizability in games and captures all actions that are optimal given beliefs formed using the model that best fits the data in the agent's misspecified model class. Our main result shows that, with probability one, every \emph{limit action} -- any action played or approached infinitely often -- is Berk--Nash rationalizable. This holds regardless of whether behavior converges. We apply the concept to known examples and identify classes of environments where it is easily characterized. The framework provides a general tool for bounding long-run behavior without assuming convergence to a Berk--Nash equilibrium.