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Decision Analysis by Augmented Probability Simulation

Listed author(s):
  • Concha Bielza

    (Decision Analysis Group, Madrid Technical University, Spain)

  • Peter Müller

    (Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina 27706)

  • David Ríos Insua

    (School of Engineering, Universidad Rey Juan Carlos, Spain)

Registered author(s):

    We provide a generic Monte Carlo method to find the alternative of maximum expected utility in a decision analysis. We define an artificial distribution on the product space of alternatives and states, and show that the optimal alternative is the mode of the implied marginal distribution on the alternatives. After drawing a sample from the artificial distribution, we may use exploratory data analysis tools to approximately identify the optimal alternative. We illustrate our method for some important types of influence diagrams.

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    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 45 (1999)
    Issue (Month): 7 (July)
    Pages: 995-1007

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    Handle: RePEc:inm:ormnsc:v:45:y:1999:i:7:p:995-1007
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    1. Izhar Matzkevich & Bruce Abramson, 1995. "Decision Analytic Networks in Artificial Intelligence," Management Science, INFORMS, vol. 41(1), pages 1-22, January.
    2. Allen C. Miller, III & Thomas R. Rice, 1983. "Discrete Approximations of Probability Distributions," Management Science, INFORMS, vol. 29(3), pages 352-362, March.
    3. James E. Smith, 1993. "Moment Methods for Decision Analysis," Management Science, INFORMS, vol. 39(3), pages 340-358, March.
    4. Shenoy, Prakash P., 1994. "A comparison of graphical techniques for decision analysis," European Journal of Operational Research, Elsevier, vol. 78(1), pages 1-21, October.
    5. Brewer, M. J. & Aitken, C. G. G. & Talbot, M., 1996. "A comparison of hybrid strategies for Gibbs sampling in mixed graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 21(3), pages 343-365, March.
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