Dynamic Concern for Misspecification
研究一个决策者如何动态调整对模型设定错误的担忧程度，并解释长期中不同静态偏好如何出现，以及该机制如何导致行为周期。
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.