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A Simulation-Based Approach to Decision Making with Partial Information

Author

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  • Luis V. Montiel

    (Graduate Program in Operations Research and Industrial Engineering, University of Texas at Austin, Austin, Texas 78712)

  • J. Eric Bickel

    (Graduate Program in Operations Research and Industrial Engineering, University of Texas at Austin, Austin, Texas 78712)

Abstract

The construction of a probabilistic model is a key step in most decision and risk analyses. Typically this is done by defining a single joint distribution in terms of marginal and conditional distributions. The difficulty of this approach is that often the joint distribution is underspecified. For example, we may lack knowledge of the marginal distributions or the underlying dependence structure. In this paper, we suggest an approach to analyzing decisions with partial information. Specifically, we propose a simulation procedure to create a collection of joint distributions that match the known information. This collection of distributions can then be used to analyze the decision problem. We demonstrate our method by applying it to the Eagle Airlines case study used in previous studies.

Suggested Citation

  • Luis V. Montiel & J. Eric Bickel, 2012. "A Simulation-Based Approach to Decision Making with Partial Information," Decision Analysis, INFORMS, vol. 9(4), pages 329-347, December.
    • Handle: RePEc:inm:ordeca:v:9:y:2012:i:4:p:329-347
    DOI: 10.1287/deca.1120.0252
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    References listed on IDEAS

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    Cited by:

    1. L. Robin Keller & Ali Abbas & J. Eric Bickel & Vicki M. Bier & David V. Budescu & John C. Butler & Enrico Diecidue & Robin L. Dillon-Merrill & Raimo P. Hämäläinen & Kenneth C. Lichtendahl & Jason R. W, 2012. "From the Editors ---Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus," Decision Analysis, INFORMS, vol. 9(4), pages 297-302, December.
    2. Tianyang Wang & James S. Dyer & John C. Butler, 2016. "Modeling Correlated Discrete Uncertainties in Event Trees with Copulas," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 396-410, February.
    3. Rakesh K. Sarin & L. Robin Keller, 2013. "From the Editors: Probability Approximations, Anti-Terrorism Strategy, and Bull's-Eye Display for Performance Feedback," Decision Analysis, INFORMS, vol. 10(1), pages 1-5, March.
    4. Luo, Chunling & Tan, Chin Hon & Liu, Xiao, 2020. "Maximum excess dominance: Identifying impractical solutions in linear problems with interval coefficients," European Journal of Operational Research, Elsevier, vol. 282(2), pages 660-676.
    5. Sérgio Luís Proença Duarte Guerreiro, 2022. "Cooperative Decision-Making to Minimize Biased Perceived Value Effect on Business Process Decisions Using Partially Observable Markov Decision Processes," SN Operations Research Forum, Springer, vol. 3(2), pages 1-26, June.
    6. Tianyang Wang & James S. Dyer & Warren J. Hahn, 2017. "Sensitivity analysis of decision making under dependent uncertainties using copulas," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 117-139, November.
    7. Christoph Werner & Tim Bedford & John Quigley, 2018. "Sequential Refined Partitioning for Probabilistic Dependence Assessment," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2683-2702, December.
    8. Luis V. Montiel & J. Eric Bickel, 2013. "Approximating Joint Probability Distributions Given Partial Information," Decision Analysis, INFORMS, vol. 10(1), pages 26-41, March.
    9. Luis V. Montiel & J. Eric Bickel, 2014. "A Generalized Sampling Approach for Multilinear Utility Functions Given Partial Preference Information," Decision Analysis, INFORMS, vol. 11(3), pages 147-170, September.
    10. Woodruff, Joshua & Dimitrov, Nedialko B., 2018. "Optimal discretization for decision analysis," Operations Research Perspectives, Elsevier, vol. 5(C), pages 288-305.

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