“Dice”-sion–Making Under Uncertainty: When Can a Random Decision Reduce Risk?

Stochastic programming and distributionally robust optimization seek deterministic decisions that optimize a risk measure, possibly in view of the most adverse distribution in an ambiguity set. We investigate under which circumstances such deterministic decisions are strictly outperformed by random...

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Bibliographic Details
Published in:Management science 2019-07, Vol.65 (7), p.3282-3301
Main Authors: Delage, Erick, Kuhn, Daniel, Wiesemann, Wolfram
Format: Article
Language:English
Subjects:
Ambiguity
ambiguity aversion
Decision making
Decisions
Deviation
distributionally robust optimization
Optimization
Property
randomized decisions
Risk
Risk assessment
risk measures
Risk reduction
Stochastic models
stochastic programming
Uncertainty
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