Prolonged Learning and Hasty Stopping: The Wald Problem with Ambiguity

• 成果类型: Article
• 署名作者: Auster, Sarah; Che, Yeon-Koo; Mierendorff, Konrad
• 署名单位: University of Bonn; Columbia University; University of London; University College London
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20221149
• 发表日期: 2024
• 页码: 426-461
• 关键词: PROSPECT-THEORY; randomization

摘要

This paper studies sequential information acquisition by an ambiguity -averse decision -maker (DM), who decides how long to collect information before taking an irreversible action. The agent optimizes against the worst -case belief and updates prior by prior. We show that the consideration of ambiguity gives rise to rich dynamics: compared to the Bayesian DM, the DM here tends to experiment excessively when facing modest uncertainty and, to counteract it, may stop experimenting prematurely when facing high uncertainty. In the latter case, the DM's stopping rule is nonmonotonic in beliefs and features randomized stopping. (JEL C61, D81, D83, D91)