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Estimating the Impact of Uncertainty on a Deterministic Multiattribute Evaluation

Author

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  • Craig W. Kirkwood

    (Department of Decision and Information Systems, Arizona State University, Tempe, Arizona 85287-4206)

Abstract

A method is presented to estimate the impact of uncertainty on the results of a multiattribute evaluation prior to conducting a complete probabilistic multiattribute utility analysis. The method assumes that a deterministic analysis has been completed using a weighted-additive multiattribute value (evaluation) function and that either an additive or multiplicative utility function is appropriate for the multiattribute utility analysis. An approximation procedure is developed to estimate whether uncertainty about attribute levels could change the deterministic evaluation results. An illustrative application investigates the accuracy of the procedure and its use to simplify analysis of the effects of uncertainty.

Suggested Citation

  • Craig W. Kirkwood, 1992. "Estimating the Impact of Uncertainty on a Deterministic Multiattribute Evaluation," Management Science, INFORMS, vol. 38(6), pages 819-826, June.
    • Handle: RePEc:inm:ormnsc:v:38:y:1992:i:6:p:819-826
    DOI: 10.1287/mnsc.38.6.819
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    Citations

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    Cited by:

    1. Durbach, Ian N. & Calder, Jon M., 2016. "Modelling uncertainty in stochastic multicriteria acceptability analysis," Omega, Elsevier, vol. 64(C), pages 13-23.
    2. Hocine, Amin & Zhuang, Zheng-Yun & Kouaissah, Noureddine & Li, Der-Chiang, 2020. "Weighted-additive fuzzy multi-choice goal programming (WA-FMCGP) for supporting renewable energy site selection decisions," European Journal of Operational Research, Elsevier, vol. 285(2), pages 642-654.
    3. Mustajoki, Jyri & Hamalainen, Raimo P. & Lindstedt, Mats R.K., 2006. "Using intervals for global sensitivity and worst-case analyses in multiattribute value trees," European Journal of Operational Research, Elsevier, vol. 174(1), pages 278-292, October.
    4. Durbach, Ian N. & Stewart, Theodor J., 2012. "A comparison of simplified value function approaches for treating uncertainty in multi-criteria decision analysis," Omega, Elsevier, vol. 40(4), pages 456-464.
    5. Hocine, Amine & Kouaissah, Noureddine & Bettahar, Samir & Benbouziane, Mohamed, 2018. "Optimizing renewable energy portfolios under uncertainty: A multi-segment fuzzy goal programming approach," Renewable Energy, Elsevier, vol. 129(PA), pages 540-552.
    6. Mustajoki, Jyri, 2012. "Effects of imprecise weighting in hierarchical preference programming," European Journal of Operational Research, Elsevier, vol. 218(1), pages 193-201.
    7. Butler, John C. & Dyer, James S. & Jia, Jianmin & Tomak, Kerem, 2008. "Enabling e-transactions with multi-attribute preference models," European Journal of Operational Research, Elsevier, vol. 186(2), pages 748-765, April.
    8. Durbach, Ian N. & Stewart, Theodor J., 2009. "Using expected values to simplify decision making under uncertainty," Omega, Elsevier, vol. 37(2), pages 312-330, April.
    9. Katsikopoulos, Konstantinos V. & Durbach, Ian N. & Stewart, Theodor J., 2018. "When should we use simple decision models? A synthesis of various research strands," Omega, Elsevier, vol. 81(C), pages 17-25.
    10. Hocine, Amine & Kouaissah, Noureddine, 2020. "XOR analytic hierarchy process and its application in the renewable energy sector," Omega, Elsevier, vol. 97(C).
    11. Durbach, Ian N. & Stewart, Theodor J., 2012. "Modeling uncertainty in multi-criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 223(1), pages 1-14.
    12. Durbach, Ian N. & Stewart, Theodor J., 2011. "An experimental study of the effect of uncertainty representation on decision making," European Journal of Operational Research, Elsevier, vol. 214(2), pages 380-392, October.

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