Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/268027 
Year of Publication: 
2022
Series/Report no.: 
ECONtribute Discussion Paper No. 194
Publisher: 
University of Bonn and University of Cologne, Reinhard Selten Institute (RSI), Bonn and Cologne
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 non-monotonic in beliefs and features randomized stopping.
Subjects: 
Wald problem
ambiguity aversion
prolonged learning
preemptive stopping
JEL: 
C61
D81
D83
D91
Document Type: 
Working Paper

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