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Input Distribution Selection for Simulation Experiments: Accounting for Input Uncertainty

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  • Stephen E. Chick

    (Department of Industrial and Operations Engineering, The University of Michigan, 1205 Beal Avenue, Ann Arbor, Michigan 48109-2117)

Abstract

A number of authors have identified problematic issues with techniques used in current simulation practice for selecting probability distributions and their parameters for input to stochastic simulations. A major goal of this paper is to address some of those issues by presenting a self-consistent evaluation of the uncertainty about the mean value of the simulation output, when there is uncertainty in both the parameters and functional form of input distributions (structural uncertainty), and uncertainty due to the stochastic nature of simulation output (stochastic uncertainty), as is common in simulation practice. The analysis leads to an algorithm for randomly sampling input distributions and parameters before each simulation replication, using a Bayesian posterior distribution for input distributions and parameters, given historical data. Mechanisms for addressing issues of importance to the discrete-event simulation community are illustrated by example, such as the specification of prior distributions, and analysis for shifted distributions.

Suggested Citation

  • Stephen E. Chick, 2001. "Input Distribution Selection for Simulation Experiments: Accounting for Input Uncertainty," Operations Research, INFORMS, vol. 49(5), pages 744-758, October.
    • Handle: RePEc:inm:oropre:v:49:y:2001:i:5:p:744-758
    DOI: 10.1287/opre.49.5.744.10606
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    References listed on IDEAS

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    1. Jon C. Helton, 1994. "Treatment of Uncertainty in Performance Assessments for Complex Systems," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 483-511, August.
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    Cited by:

    1. Corlu, Canan G. & Akcay, Alp & Xie, Wei, 2020. "Stochastic simulation under input uncertainty: A Review," Operations Research Perspectives, Elsevier, vol. 7(C).
    2. Aleksandrina Goeva & Henry Lam & Huajie Qian & Bo Zhang, 2019. "Optimization-Based Calibration of Simulation Input Models," Operations Research, INFORMS, vol. 67(5), pages 1362-1382, September.
    3. Soumyadip Ghosh & Henry Lam, 2019. "Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees," Operations Research, INFORMS, vol. 67(1), pages 232-249, January.
    4. M D Stevenson & J E Oakley & S E Chick & K Chalkidou, 2009. "The cost-effectiveness of surgical instrument management policies to reduce the risk of vCJD transmission to humans," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(4), pages 506-518, April.
    5. Zhaolin Hu & L. Jeff Hong, 2022. "Robust Simulation with Likelihood-Ratio Constrained Input Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2350-2367, July.
    6. Yunpeng Sun & Daniel W. Apley & Jeremy Staum, 2011. "Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation," Operations Research, INFORMS, vol. 59(4), pages 998-1007, August.
    7. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    8. Bahar Biller & Canan G. Corlu, 2011. "Accounting for Parameter Uncertainty in Large-Scale Stochastic Simulations with Correlated Inputs," Operations Research, INFORMS, vol. 59(3), pages 661-673, June.
    9. Bahar Biller & Michael Hartig & Ronald J. Olson & Peter Sandvik & Gerald Trant & Yang Sui & Andrew Minnick, 2019. "General Electric Uses Simulation and Risk Analysis for Silicon Carbide Production System Design," Interfaces, INFORMS, vol. 49(2), pages 117-128, March.
    10. Borgonovo, Emanuele & Marinacci, Massimo, 2015. "Decision analysis under ambiguity," European Journal of Operational Research, Elsevier, vol. 244(3), pages 823-836.
    11. Wei Xie & Barry L. Nelson & Russell R. Barton, 2014. "A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation," Operations Research, INFORMS, vol. 62(6), pages 1439-1452, December.
    12. Tianyi Liu & Enlu Zhou, 2019. "Online Quantification of Input Model Uncertainty by Two-Layer Importance Sampling," Papers 1912.11172, arXiv.org, revised Feb 2020.
    13. Barry L. Nelson & Alan T. K. Wan & Guohua Zou & Xinyu Zhang & Xi Jiang, 2021. "Reducing Simulation Input-Model Risk via Input Model Averaging," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 672-684, May.
    14. Helin Zhu & Tianyi Liu & Enlu Zhou, 2015. "Risk Quantification in Stochastic Simulation under Input Uncertainty," Papers 1507.06015, arXiv.org, revised Dec 2017.
    15. Jason R. W. Merrick & J. Rene Van Dorp & Varun Dinesh, 2005. "Assessing Uncertainty in Simulation‐Based Maritime Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 25(3), pages 731-743, June.
    16. Russell R. Barton & Barry L. Nelson & Wei Xie, 2014. "Quantifying Input Uncertainty via Simulation Confidence Intervals," INFORMS Journal on Computing, INFORMS, vol. 26(1), pages 74-87, February.
    17. Weiwei Fan & L. Jeff Hong & Xiaowei Zhang, 2020. "Distributionally Robust Selection of the Best," Management Science, INFORMS, vol. 66(1), pages 190-208, January.
    18. Jason R. W. Merrick & Rene Van Dorp, 2006. "Speaking the Truth in Maritime Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 26(1), pages 223-237, February.
    19. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
    20. Zhaolin Hu & Jing Cao & L. Jeff Hong, 2012. "Robust Simulation of Global Warming Policies Using the DICE Model," Management Science, INFORMS, vol. 58(12), pages 2190-2206, December.
    21. Jason R. W. Merrick, 2009. "Bayesian Simulation and Decision Analysis: An Expository Survey," Decision Analysis, INFORMS, vol. 6(4), pages 222-238, December.

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