Working Paper: CEPR ID: DP18295
Authors: Sarah Auster; Yeonkoo Che; Konrad Mierendorff
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.
Keywords: ambiguity; aversion; prolonged learning; hasty stopping; Wald problem
JEL Codes: C61; D81; D83; D91
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| DM's ambiguity aversion (D80) | prolonged indecision (D80) |
| prolonged indecision (D80) | excessive experimentation (C90) |
| prolonged indecision (D80) | premature stopping (C41) |
| modest uncertainty (D89) | excessive experimentation (C90) |
| high uncertainty (D89) | premature stopping (C41) |
| DM's worst-case beliefs (D80) | stopping behavior (C92) |
| ambiguity (D84) | expected experimentation time (C90) |
| DM's beliefs (Y80) | stopping behavior (C92) |