Unrealistic Expectations and Misguided Learning

• 成果类型: Article
• 署名作者: Heidhues, Paul; Koszegi, Botond; Strack, Philipp
• 署名单位: Heinrich Heine University Dusseldorf; Central European University; University of California System; University of California Berkeley
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA14084
• 发表日期: 2018
• 页码: 1159-1214
• 关键词: SELF-SERVING BIASES; ceo overconfidence; posterior distributions; misspecified models; equilibrium; INFORMATION; attribution; BEHAVIOR; CONVERGENCE; assessments

摘要

We explore the learning process and behavior of an individual with unrealistically high expectations (overconfidence) when outcomes also depend on an external fundamental that affects the optimal action. Moving beyond existing results in the literature, we show that the agent's beliefs regarding the fundamental converge under weak conditions. Furthermore, we identify a broad class of situations in which learning about the fundamental is self-defeating: it leads the individual systematically away from the correct belief and toward lower performance. Due to his overconfidence, the agenteven if initially correctbecomes too pessimistic about the fundamental. As he adjusts his behavior in response, he lowers outcomes and hence becomes even more pessimistic about the fundamental, perpetuating the misdirected learning. The greater is the loss from choosing a suboptimal action, the further the agent's action ends up from optimal. We partially characterize environments in which self-defeating learning occurs, and show that the decisionmaker learns to take the optimal action if, and in a sense only if, a specific non-identifiability condition is satisfied. In contrast to an overconfident agent, an underconfident agent's misdirected learning is self-limiting and therefore not very harmful. We argue that the decision situations in question are common in economic settings, including delegation, organizational, effort, and public-policy choices.