We introduce an equilibrium framework that relaxes the standard assumption that people have a correctly-specified view of their environment. Players repeatedly play a simultaneous-move game where they potentially face both strategic and payoff uncertainty. Each player has a potentially...

We provide an equilibrium framework for modeling the behavior of an agent who holds a simplified view of a dynamic optimization problem. The agent faces a Markov Decision Process, where a transition probability function determines the evolution of a state variable as a function of the previous...

We provide an equilibrium framework for modeling the behavior of an agent who holds a simplified view of a dynamic optimization problem. The agent faces a Markov decision process, where a transition probability function determines the evolution of a state variable as a function of the previous...

We provide a framework to study dynamic optimization problems where the agent is uncertain about her environment but has (possibly) an incorrectly specified model, in the sense that the support of her prior does not include the true model. The agent's actions affect both her payoff and also what...

We study Markov decision problems where the agent does not know the transition probability function mapping current states and actions to future states. The agent has a prior belief over a set of possible transition functions and updates beliefs using Bayes' rule. We allow her to be misspecified...

We introduce a solution concept in the context of large elections with private information by embedding a model of boundedly rational voters into an otherwise standard equilibrium setting. A retrospective voting equilibrium (RVE) formalizes the idea that voters evaluate alternatives based on...

We introduce an equilibrium framework that relaxes the standard assumption that people have a correctly-specified view of their environment. Players repeatedly play a simultaneous-move game where they potentially face both strategic and payoff uncertainty. Each player has a potentially...

We provide an equilibrium framework for modeling the behavior of an agent who holds a simplified view of a dynamic optimization problem. The agent faces a Markov Decision Process, where a transition probability function determines the evolution of a state variable as a function of the previous...