Equilibrium in misspecified Markov decision processes

• 成果类型: Article
• 署名作者: Esponda, Ignacio; Pouzo, Demian
• 署名单位: University of California System; University of California Berkeley
• 刊物名称: THEORETICAL ECONOMICS
• ISSN/ISSBN: 1933-6837
• DOI: 10.3982/TE3843
• 发表日期: 2021-05-01
• 页码: 717-757
• 关键词: Misspecified model; Markov Decision Process; equilibrium; C61; D83

摘要

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.