I consider an agent who posits a set of probabilistic models for the payoff‐relevant outcomes. The agent has a prior over this set but fears the actual model is omitted and hedges against this possibility. The concern for misspecification is endogenous: If a model explains the previous observations well, the concern attenuates. I show that different static preferences under uncertainty (subjective expected utility, maxmin, robust control) arise in the long run, depending on how quickly the agent becomes unsatisfied with unexplained evidence. The misspecification concern's endogeneity naturally induces behavior cycles, and I characterize the limit action frequency. I apply the model to monetary policy cycles and choices in the face of complex tax schedules.

We study how an agent learns from endogenous data when their prior belief is misspecified. We show that only uniform Berk–Nash equilibria can be long‐run outcomes, and that all uniformly strict Berk–Nash equilibria have an arbitrarily high probability of being the long‐run outcome for some initial beliefs. When the agent believes the outcome distribution is exogenous, every uniformly strict Berk–Nash equilibrium has positive probability of being the long‐run outcome for any initial belief. We generalize these results to settings where the agent observes a signal before acting.