multiple stable self-confirming equilibria. As an application, we examine the problem of distinguishing between Classical and Keynesian directions of fit in the Phillips Curve (Sargent [199] and King and Watson [1994]), and claim that that the Classical direction of fit is more robust to...

This paper studies adaptive learning with multiple models. An agent operating in a self-referential environment is aware of potential model misspecification, and tries to detect it, in real-time, using an econometric specification test. If the current model passes the test, it is used to...

This paper studies the problem of an agent who wants to prevent the state from exceeding a critical threshold. Even though the agent is presumed to know the model, the optimal policy is computed by solving a conventional robust control problem. That is, robustness is induced here by objectives...

This paper studies the problem of an agent who wants to prevent the state from exceeding a critical threshold. Even though the agent is presumed to know the model, the optimal policy is computed by solving a conventional robust control problem. That is, robustness is induced here by objectives...

This paper studies the following problem. An agent takes actions based on a possibly misspecified model. The agent is 'large', in the sense that his actions influence the model he is trying to learn about. The agent is aware of potential model misspecification and tries to detect it, in...

This paper studies adaptive learning with multiple models. An agent operating in a self-referential environment is aware of potential model misspecification, and tries to detect it, in real-time, using an econometric specification test. If the current model passes the test, it is used to...

This paper derives a formula for the optimal forecast of a discounted sum of future values of a random variable. This problem reflects a preference for robustness in the presence of (unstructured) model uncertainty. The paper shows that revisions of a robust forecast are more sensitive to new...