Modelling uncertainty in stochastic multicriteria acceptability analysis
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.
Citation
Durbach , I N & Calder , J 2016 , ' Modelling uncertainty in stochastic multicriteria acceptability analysis ' , Omega: The International Journal of Management Science , vol. 64 , pp. 13-23 . https://doi.org/10.1016/j.omega.2015.10.015
Publication
Omega: The International Journal of Management Science
Status
Peer reviewed
ISSN
0305-0483Type
Journal article
Collections
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