Conditional Distributionally Robust Functionals

Alexander Shapiro () and Alois Pichler
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Alexander Shapiro: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Alois Pichler: Faculty of Mathematics, University of Technology, 09126 Chemnitz, Germany

Operations Research, 2024, vol. 72, issue 6, 2745-2757

Abstract: Many decisions, in particular decisions in a managerial context, are subject to uncertainty. Risk measures cope with uncertainty by involving more than one candidate probability. The corresponding risk averse decision takes all potential candidate probabilities into account and is robust with respect to all potential probabilities. This paper considers conditional robust decision making, where decisions are subject to additional prior knowledge or information. The literature discusses various definitions to characterize the corresponding conditional risk measure, which determines further the decision. The aim of this paper is to compare two different approaches for the construction of conditional functionals used in multistage distributionally robust optimization. As an application, we discuss conditional counterparts of a distance between probability measures.

Keywords: Optimization; distributional robustness; conditional risk measures; strict monotonicity; Wasserstein distance; rectangularity (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:72:y:2024:i:6:p:2745-2757

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