Improving robust decisions with data

Xiaoyu Cheng ()
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Xiaoyu Cheng: Department of Economics, Florida State University

Theoretical Economics, Forthcoming

Abstract: A decision-maker faces uncertainty governed by a data-generating process (DGP), which is only known to belong to a set of sequences of independent but possibly non-identical distributions. A robust decision maximizes the expected payoff against the worst possible DGP in this set. This paper characterizes when and how such robust decisions can be \emph{objectively} improved with data --- that is, yield higher expected payoffs under the true DGP regardless of which DGP is the truth. It further develops simple and novel inference procedures that achieve such improvement, while common methods (e.g., maximum likelihood) may fail to do so.

Keywords: Maxmin expected utility; $\Gamma$-minimax; learning under ambiguity; statistical decisions (search for similar items in EconPapers)
JEL-codes: C44 D81 D83 (search for similar items in EconPapers)
Date: 2026-02-13
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