Shortfall Risk Models When Information on Loss Function Is Incomplete
Erick Delage, Shaoyan Guo, Huifu Xu
Operations Research · 2022 · Volume 70 · Issue 6
Abstract
The utility-based shortfall risk (SR) measure effectively captures a decision maker’s risk attitude on tail losses by an increasing convex loss function. In this paper, we consider a situation where the decision maker’s risk attitude toward tail losses is ambiguous and introduce a robust version of SR, which mitigates the risk arising from such ambiguity. Specifically, we use some available partial information or subjective judgement to construct a set of utility-based loss functions and define a so-called preference robust shortfall risk (PRSR) through the worst loss function from the (ambiguity) set. We then apply the PRSR to optimal decision-making problems and demonstrate how the latter can be reformulated as tractable convex programs when the underlying exogenous uncertainty is discretely distributed. Funding: This research was partially supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2016-05208]; the Canada Research Chairs [Grant 950-230057]; the Research Grants Council Hong Kong [Grant 14500620]; and the National Natural Science Foundation of China [Grant 11801057].
Keywords
Optimization, preference robust optimization, utility-based shortfall risk measure, preference elicitation, linear programming, tractability
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