Overinference from Weak Signals and Underinference from Strong Signals*

• 成果类型: Article
• 署名作者: Augenblick, Ned; Lazarus, Eben; Thaler, Michael
• 署名单位: University of California System; University of California Berkeley; City St Georges, University of London
• 刊物名称: QUARTERLY JOURNAL OF ECONOMICS
• ISSN/ISSBN: 0033-5533
• DOI: 10.1093/qje/qjae032
• 发表日期: 2024
• 页码: 335-401
• 关键词: BAYES RULE; INFORMATION; prices; MODEL; news

摘要

When people receive new information, sometimes they revise their beliefs too much, and sometimes too little. We show that a key driver of whether people overinfer or underinfer is the strength of the information. Based on a model in which people know which direction to update in, but not exactly how much to update, we hypothesize that people will overinfer from weak signals and underinfer from strong signals. We then test this hypothesis across four different environments: abstract experiments, a naturalistic experiment, sports betting markets, and financial markets. In each environment, our consistent and robust finding is overinference from weak signals and underinference from strong signals. Our framework and findings can help harmonize apparently contradictory results from the experimental and empirical literatures.