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Economic Analysis of Simulation Selection Problems

Author

Listed:
  • Stephen E. Chick

    (Technology and Operations Management Area, INSEAD, 77305 Fontainebleau, France)

  • Noah Gans

    (Operations and Information Management Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

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.

Suggested Citation

  • Stephen E. Chick & Noah Gans, 2009. "Economic Analysis of Simulation Selection Problems," Management Science, INFORMS, vol. 55(3), pages 421-437, March.
    • Handle: RePEc:inm:ormnsc:v:55:y:2009:i:3:p:421-437
    DOI: 10.1287/mnsc.1080.0949
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    References listed on IDEAS

    as
    1. John Butler & Douglas J. Morrice & Peter W. Mullarkey, 2001. "A Multiple Attribute Utility Theory Approach to Ranking and Selection," Management Science, INFORMS, vol. 47(6), pages 800-816, June.
    2. Brezzi, Monica & Lai, Tze Leung, 2002. "Optimal learning and experimentation in bandit problems," Journal of Economic Dynamics and Control, Elsevier, vol. 27(1), pages 87-108, November.
    3. Bennett L. Fox & Peter W. Glynn, 1989. "Simulating Discounted Costs," Management Science, INFORMS, vol. 35(11), pages 1297-1315, November.
    4. Barry L. Nelson & David Goldsman, 2001. "Comparisons with a Standard in Simulation Experiments," Management Science, INFORMS, vol. 47(3), pages 449-463, March.
    5. Stephen E. Chick & Koichiro Inoue, 2001. "New Two-Stage and Sequential Procedures for Selecting the Best Simulated System," Operations Research, INFORMS, vol. 49(5), pages 732-743, October.
    Full references (including those not matched with items on IDEAS)

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