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Improving Robust Decisions with Data

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  • Xiaoyu Cheng

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 improved with data, measured by the expected payoff under the true DGP, no matter which possible DGP is the truth. It further develops novel and simple inference methods to achieve it, as common methods (e.g., maximum likelihood) may fail to deliver such an improvement.

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  • Xiaoyu Cheng, 2023. "Improving Robust Decisions with Data," Papers 2310.16281, arXiv.org, revised Jul 2024.
    • Handle: RePEc:arx:papers:2310.16281
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    References listed on IDEAS

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    1. Cerreia-Vioglio, Simone & Maccheroni, Fabio & Marinacci, Massimo & Montrucchio, Luigi, 2013. "Ambiguity and robust statistics," Journal of Economic Theory, Elsevier, vol. 148(3), pages 974-1049.
      • Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Luigi Montrucchio, 2011. "Ambiguity and Robust Statistics," Working Papers 382, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Wellner, Jon A., 1981. "A Glivenko-Cantelli theorem for empirical measures of independent but non-identically distributed random variables," Stochastic Processes and their Applications, Elsevier, vol. 11(3), pages 309-312, August.
    3. Charles F. Manski, 2021. "Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald," Econometrica, Econometric Society, vol. 89(6), pages 2827-2853, November.
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