Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/253497
Year of Publication: 
2021
Citation: 
[Journal:] Theoretical Economics [ISSN:] 1555-7561 [Volume:] 16 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2021 [Pages:] 717-757
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
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 state and the agent's action. The agent is uncertain about the true transition function and has a prior over a set of possible transition functions; this set reflects the agent's (possibly simplified) view of her environment and may not contain the true function. We define an equilibrium concept and provide conditions under which it characterizes steady-state behavior when the agent updates her beliefs using Bayes' rule.
Subjects: 
Misspecified model
Markov decision process
equilibrium
JEL: 
C61
D83
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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