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dc.contributor.authorDurbach, Ian Noel
dc.contributor.authorCalder, Jon
dc.identifier.citationDurbach , I N & Calder , J 2016 , ' Modelling uncertainty in stochastic multicriteria acceptability analysis ' , Omega: The International Journal of Management Science , vol. 64 , pp. 13-23 .
dc.identifier.otherPURE: 270837215
dc.identifier.otherPURE UUID: 33cfb243-9e7b-49f5-baa2-8203e5abfdb0
dc.identifier.otherScopus: 84964990251
dc.identifier.otherORCID: /0000-0003-0769-2153/work/82501102
dc.description.abstractThis 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.
dc.relation.ispartofOmega: The International Journal of Management Scienceen
dc.rightsCopyright © 2015 Elsevier Ltd. All rights reserved. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectDecision making/processen
dc.subjectDecision support systemsen
dc.subjectSensitvity analysisen
dc.subjectHD61 Risk Managementen
dc.subjectQA Mathematicsen
dc.titleModelling uncertainty in stochastic multicriteria acceptability analysisen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews.School of Mathematics and Statisticsen
dc.description.statusPeer revieweden

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