Optimal Learning under Robustness and Time-Consistency

2017/08/06 by Larry G. Epstein, Epstein, Larry G., Shaolin Ji +1 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · #Auction Theory and Applications #Decision-Making and Behavioral Economics #Economic and Environmental Valuation #FOS: Economics and business #General Economics (econ.GN)

paper · doi:10.48550/arxiv.1708.01890

openalex publication_date 2017/08/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We model learning in a continuous-time Brownian setting where there is prior ambiguity. The associated model of preference values robustness and is time-consistent. It is applied to study optimal learning when the choice between actions can be postponed, at a per-unit-time cost, in order to observe a signal that provides information about an unknown parameter. The corresponding optimal stopping problem is solved in closed-form, with a focus on two specific settings: Ellsberg's two-urn thought experiment expanded to allow learning before the choice of bets, and a robust version of the classical problem of sequential testing of two simple hypotheses about the unknown drift of a Wiener process. In both cases, the link between robustness and the demand for learning is studied.

Citations

• Rationality and the Bayesian paradigm

Cited by

• Pricing Model for Data Assets in Investment-Consumption Framework with Ambiguity

Related

• Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions
• Robust Exploratory Stopping under Ambiguity in Reinforcement Learning
• From Time-inconsistency to Time-consistency for Optimal Stopping Problems
• From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions
• A model for system uncertainty in reinforcement learning