Optimal Learning Under Robustness and Time-Consistency

Larry Epstein and Shaolin Ji ()
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Shaolin Ji: Zhongtai Securities Institute of Financial Studies, Shandong University, 250100 Jinan, China

Operations Research, 2022, vol. 70, issue 3, 1317-1329

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.

Keywords: Special Issue: Mathematical Models of Individual and Group Decision Making in Operations Research (in honor of Kenneth Arrow); ambiguity; robust decisions; learning; partial information; optimal stopping; sequential testing of simple hypotheses; Ellsberg Paradox; recursive utility; time-consistency; model uncertainty (search for similar items in EconPapers)
Date: 2022
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http://dx.doi.org/10.1287/opre.2019.1899 (application/pdf)

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Working Paper: Optimal Learning under Robustness and Time-Consistency (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:70:y:2022:i:3:p:1317-1329

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