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An Economic analysis of simulation selection problems

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This paper presents a new approach to the simulation ranking and selection problem, one that maximizes the expected NPV of decisions made when using stochastic simulation. The authors formulation assumes that facilities exist to simulate a fixed number of alternative projects, and they pose the problem as a "stoppable" version of a Bayesian bandit problem. They show that, under relatively general conditions, a Gittins index can be used to indicate which system to simulate or implement. They then provide an asymptotic approximation for the index that is appropriate when simulation outputs are normally distributed with known but potentially different variances for the different systems. The authors conclude with a discussion that relaxes some of those assumptions.

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