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Modelling uncertainty in stochastic multicriteria acceptability analysis

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  • Durbach, Ian N.
  • Calder, Jon M.

Abstract

This paper considers problem contexts in which decision makers are unable or unwilling to assess trade-off information precisely. A simulation experiment is used to assess (a) how closely a rank order of alternatives based on partial information and stochastic multicriteria acceptability analysis (SMAA) can approximate results obtained using full-information multi-attribute utility theory (MAUT) with multiplicative utility, and (b) which characteristics of the decision problem influence the accuracy of this approximation. We find that fairly good accuracy can be achieved with limited preference information, and is highest if either quantiles and probability distributions are used to represent uncertainty.

Suggested Citation

  • Durbach, Ian N. & Calder, Jon M., 2016. "Modelling uncertainty in stochastic multicriteria acceptability analysis," Omega, Elsevier, vol. 64(C), pages 13-23.
    • Handle: RePEc:eee:jomega:v:64:y:2016:i:c:p:13-23
    DOI: 10.1016/j.omega.2015.10.015
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    6. 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.
    7. Ciomek, Krzysztof & Kadziński, Miłosz & Tervonen, Tommi, 2017. "Heuristics for prioritizing pair-wise elicitation questions with additive multi-attribute value models," Omega, Elsevier, vol. 71(C), pages 27-45.
    8. He, Jian & Alavifard, Farzad & Ivanov, Dmitry & Jahani, Hamed, 2019. "A real-option approach to mitigate disruption risk in the supply chain," Omega, Elsevier, vol. 88(C), pages 133-149.

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