Prolonged Learning and Hasty Stopping: The Wald Problem with Ambiguity

Sarah Auster, Yeon-Koo Che and Konrad Mierendorff

American Economic Review, 2024, vol. 114, issue 2, 426-61

Abstract: 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-codes: C61 D81 D83 D91 (search for similar items in EconPapers)
Date: 2024
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Related works:
Working Paper: Prolonged Learning and Hasty Stopping: the Wald Problem with Ambiguity (2023)
Working Paper: Prolonged Learning and Hasty Stopping: the Wald Problem with Ambiguity (2023)
Working Paper: Prolonged Learning and Hasty Stopping: the Wald Problem with Ambiguity (2022)
Working Paper: Prolonged Learning and Hasty Stopping: The Wald Problem With Ambiguity (2022)
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DOI: 10.1257/aer.20221149

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