Prolonged Learning and Hasty Stopping: The Wald Problem with Ambiguity
American Economic Review
vol. 114,
no. 2, February 2024
(pp. 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.Citation
Auster, Sarah, Yeon-Koo Che, and Konrad Mierendorff. 2024. "Prolonged Learning and Hasty Stopping: The Wald Problem with Ambiguity." American Economic Review, 114 (2): 426-61. DOI: 10.1257/aer.20221149Additional Materials
JEL Classification
- C61 Optimization Techniques; Programming Models; Dynamic Analysis
- D81 Criteria for Decision-Making under Risk and Uncertainty
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making