Equilibrium in misspecified Markov decision processes
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...
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| Veröffentlicht in: | Theoretical economics 2021-05, Vol.16 (2), p.717-757 |
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| container_title | Theoretical economics |
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| creator | Esponda, Ignacio Pouzo, Demian |
| description | 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. |
| doi_str_mv | 10.3982/TE3843 |
| format | Article |
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| language | eng |
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| subjects | Agents Analysis C61 Cost control D83 Economic theory Equilibrium Growth models Macroeconomics Markov decision process Markov processes Misspecified model Optimization |
| title | Equilibrium in misspecified Markov decision processes |
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