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Selection Procedures with Frequentist Expected Opportunity Cost Bounds

Author

Listed:
  • Stephen E. Chick

    (Technology Management Area, INSEAD, Boulevard de Constance, 77305 Fontainebleau Cedex, France)

  • Yaozhong Wu

    (Technology Management Area, INSEAD, Boulevard de Constance, 77305 Fontainebleau Cedex, France)

Abstract

Selection procedures help identify the best of a finite set of simulated alternatives. Most work has measured the quality of a selection with the probability of correct selection, P(CS), but the expected opportunity cost of a potentially incorrect decision makes more sense in business contexts. This paper analyzes the first selection procedures that guarantee an upper bound for the expected opportunity cost, in a frequentist sense, of a potentially incorrect selection. The paper therefore bridges a gap between the indifference-zone approach (with frequentist guarantees, but for the P(CS)) and the Bayesian approach to selection procedures (which has considered the opportunity cost). We also provide “unexpected” expected opportunity cost guarantees for several existing indifference-zone selection procedures that were originally designed to provide P(CS) guarantees.

Suggested Citation

  • Stephen E. Chick & Yaozhong Wu, 2005. "Selection Procedures with Frequentist Expected Opportunity Cost Bounds," Operations Research, INFORMS, vol. 53(5), pages 867-878, October.
    • Handle: RePEc:inm:oropre:v:53:y:2005:i:5:p:867-878
    DOI: 10.1287/opre.1040.0187
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    References listed on IDEAS

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    1. Barry L. Nelson & Julie Swann & David Goldsman & Wheyming Song, 2001. "Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large," Operations Research, INFORMS, vol. 49(6), pages 950-963, December.
    2. Barry L. Nelson & Frank J. Matejcik, 1995. "Using Common Random Numbers for Indifference-Zone Selection and Multiple Comparisons in Simulation," Management Science, INFORMS, vol. 41(12), pages 1935-1945, December.
    3. Nathan L. Kleinman & James C. Spall & Daniel Q. Naiman, 1999. "Simulation-Based Optimization with Stochastic Approximation Using Common Random Numbers," Management Science, INFORMS, vol. 45(11), pages 1570-1578, November.
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    Citations

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    Cited by:

    1. Juergen Branke & Wen Zhang, 2019. "Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 831-865, September.
    2. Stephen E. Chick & Jürgen Branke & Christian Schmidt, 2010. "Sequential Sampling to Myopically Maximize the Expected Value of Information," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 71-80, February.
    3. Siyang Gao & Weiwei Chen & Leyuan Shi, 2017. "A New Budget Allocation Framework for the Expected Opportunity Cost," Operations Research, INFORMS, vol. 65(3), pages 787-803, June.
    4. Yoon, Moonyoung & Bekker, James, 2019. "Considering sample means in Rinott’s procedure with a Bayesian approach," European Journal of Operational Research, Elsevier, vol. 273(1), pages 249-258.
    5. Weiwei Fan & L. Jeff Hong & Xiaowei Zhang, 2020. "Distributionally Robust Selection of the Best," Management Science, INFORMS, vol. 66(1), pages 190-208, January.
    6. Weiwei Chen & Siyang Gao & Wenjie Chen & Jianzhong Du, 2023. "Optimizing resource allocation in service systems via simulation: A Bayesian formulation," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 65-81, January.
    7. Andres Alban & Stephen E. Chick & Martin Forster, 2023. "Value-Based Clinical Trials: Selecting Recruitment Rates and Trial Lengths in Different Regulatory Contexts," Management Science, INFORMS, vol. 69(6), pages 3516-3535, June.
    8. Jürgen Branke & Stephen E. Chick & Christian Schmidt, 2007. "Selecting a Selection Procedure," Management Science, INFORMS, vol. 53(12), pages 1916-1932, December.

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