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Modeling uncertainty in multi-criteria decision analysis

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  • Durbach, Ian N.
  • Stewart, Theodor J.
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    Abstract

    his paper provides a review of multiple criteria decision analysis (MCDA) for cases where attribute evaluations are uncertain. The main aim is to identify different tools which can be used to represent uncertain evaluations, and to broadly survey the available decision models that can be used to support uncertain decision making. The review includes models using probabilities or probability-like quantities; explicit risk measures such as quantiles and variances; fuzzy numbers, and scenarios. The practical assessment of uncertain outcomes and preferences associated with these outcomes is also discussed.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 223 (2012)
    Issue (Month): 1 ()
    Pages: 1-14

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    Handle: RePEc:eee:ejores:v:223:y:2012:i:1:p:1-14

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Decision analysis; Multiple criteria analysis; Risk management; Uncertainty modeling;

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    1. S. Scrieciu & Valerie Belton & Zaid Chalabi & Reinhard Mechler & Daniel Puig, 2014. "Advancing methodological thinking and practice for development-compatible climate policy planning," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 19(3), pages 261-288, March.
    2. Merigó, José M. & Casanovas, Montserrat & Yang, Jian-Bo, 2014. "Group decision making with expertons and uncertain generalized probabilistic weighted aggregation operators," European Journal of Operational Research, Elsevier, vol. 235(1), pages 215-224.
    3. Sirbiladze, Gia & Khutsishvili, Irina & Ghvaberidze, Bezhan, 2014. "Multistage decision-making fuzzy methodology for optimal investments based on experts’ evaluations," European Journal of Operational Research, Elsevier, vol. 232(1), pages 169-177.
    4. Durbach, Ian N., 2014. "Outranking under uncertainty using scenarios," European Journal of Operational Research, Elsevier, vol. 232(1), pages 98-108.

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