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Economic analysis of simulation selection problems

Author: Chick, Stephen ; Gans, NoahINSEAD Area: Technology and Operations ManagementIn: Management Science, vol. 55, no. 3, March 2009 Language: EnglishDescription: p. 421-437.Type of document: INSEAD ArticleNote: Please ask us for this itemAbstract: Ranking and selection procedures are standard methods for selecting the best of a finite number of simulated design alternatives based on a desired level of statistical evidence for correct selection. But the link between statistical significance and financial significance is indirect, and there has been little or no research into it. This paper presents a new approach to the simulation selection problem, one that maximizes the expected net present value of decisions made when using stochastic simulation. We provide a framework for answering these managerial questions: When does a proposed system design, whose performance is unknown, merit the time and money needed to develop a simulation to infer its performance? For how long should the simulation analysis continue before a design is approved or rejected? We frame the simulation selection problem as a “stoppable” version of a Bayesian bandit problem that treats the ability to simulate as a real option prior to project implementation. For a single proposed system, we solve a free boundary problem for a heat equation that approximates the solution to a dynamic program that finds optimal simulation project stopping times and that answers the managerial questions. For multiple proposed systems, we extend previous Bayesian selection procedures to account for discounting and simulation-tool development costs.
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Ranking and selection procedures are standard methods for selecting the best of a finite number of simulated design alternatives based on a desired level of statistical evidence for correct selection. But the link between statistical significance and financial significance is indirect, and there has been little or no research into it. This paper presents a new approach to the simulation selection problem, one that maximizes the expected net present value of decisions made when using stochastic simulation. We provide a framework for answering these managerial questions: When does a proposed system design, whose performance is unknown, merit the time and money needed to develop a simulation to infer its performance? For how long should the simulation analysis continue before a design is approved or rejected? We frame the simulation selection problem as a “stoppable” version of a Bayesian bandit problem that treats the ability to simulate as a real option prior to project implementation. For a single proposed system, we solve a free boundary problem for a heat equation that approximates the solution to a dynamic program that finds optimal simulation project stopping times and that answers the managerial questions. For multiple proposed systems, we extend previous Bayesian selection procedures to account for discounting and simulation-tool development costs.

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