A Four-Moments Alternative to Simulation for a Class of Stochastic Management Models

This paper presents a computational alternative to simulation for a large class of stochastic management models involving functions of random variables. An example of a model in this class is the well-known "risk analysis" problem studied by Hertz and Hillier. Our computational approach includes i a versatile framework to describe the univariate and dependence characteristics of a model's random variables, and ii formulas for computing the central moments of the model's objective variable. The usefulness of these central moments in decision making is then illustrated and discussed.

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