Welfare Comparisons for Biased Learningt

• 成果类型: Article
• 署名作者: Frick, Mira; Iijima, Ryota; Ishii, Yuhta
• 署名单位: Yale University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20210410
• 发表日期: 2024
• 页码: 1612-1649
• 关键词: LARGE DEMAND; models; equilibrium; BEHAVIOR; agents; LAW

摘要

We study robust welfare comparisons of learning biases (misspecified signal distribution, we deem one bias more harmful than another if it yields lower objective expected payoffs in all decision problems. We characterize this ranking in static and dynamic settings. While the static characterization compares posteriors signal by signal, the dynamic characterization employs an efficiency index measuring how fast beliefs converge. We quantify and compare the severity of several well-documented biases. We also highlight disagreements between the static and dynamic rankings, and that some large biases dynamically outperform other vanishingly small biases.