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The Magnitude of Errors in Proximal Multiattribute Decision Analysis with Probabilistically Dependent Attributes

Author

Listed:
  • James L. Corner

    (Department of Management Systems, University of Waikato, Hamilton, New Zealand)

  • Craig W. Kirkwood

    (Department of Management, Arizona State University, Tempe, Arizona 85287-4006)

Abstract

This paper investigates the accuracy of an approximation procedure for evaluating alternatives under uncertainty with multiple evaluation attributes. This approximation uses only the first two moments of the probability distributions for the alternatives, and hence it can substantially reduce the amount of information which must be collected in order to evaluate alternatives when evaluation attributes are probabilistically dependent. The accuracy of the approximation is investigated by comparing results from using it with exact calculations for a variety of situations representative of those found in decision analysis practice. This investigation shows that the approximation is accurate for situations representative of many decision analysis applications. However, caution is needed in applying the approximation in some situations where it may give inaccurate results. Characteristics of cases where the approximation is less accurate are presented.

Suggested Citation

  • James L. Corner & Craig W. Kirkwood, 1996. "The Magnitude of Errors in Proximal Multiattribute Decision Analysis with Probabilistically Dependent Attributes," Management Science, INFORMS, vol. 42(7), pages 1033-1042, July.
    • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:7:p:1033-1042
    DOI: 10.1287/mnsc.42.7.1033
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    Cited by:

    1. Huifen Chen, 2001. "Initialization for NORTA: Generation of Random Vectors with Specified Marginals and Correlations," INFORMS Journal on Computing, INFORMS, vol. 13(4), pages 312-331, November.
    2. Jiehua Xie & Zhengyong Zhou, 2022. "Patchwork Constructions of Multiattribute Utility Functions," Decision Analysis, INFORMS, vol. 19(2), pages 141-169, June.

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