Abstract

We consider modeling and implementation of risk-averse preferences in stochastic programming problems using axiomatically defined risk measures. We derive representations for several classes of risk measures (e.g., coherent risk measures, deviation measures) via solutions of specially formulated stochastic programming problems that facilitate incorporation of risk measures in multistage stochastic programming problems. As an illustration of the general approach, we consider a two-stage stochastic weapon-target assignment problem, where a coherent risk measure is used to capture the risk of the second-stage (recourse) action.

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