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dc.contributor.authorDurbach, Ian
dc.date.accessioned2020-04-01T23:31:59Z
dc.date.available2020-04-01T23:31:59Z
dc.date.issued2019-04-02
dc.identifier258447547
dc.identifiercb73030a-260e-4aea-8823-5ec16a3ee62e
dc.identifier85086577527
dc.identifier.citationDurbach , I 2019 , ' Scenario planning in the analytic hierarchy process ' , Futures & Foresight Science , vol. Early View , e16 . https://doi.org/10.1002/ffo2.16en
dc.identifier.issn2573-5152
dc.identifier.otherRIS: urn:9921251473D08E97E6D5B28D2682D284
dc.identifier.otherORCID: /0000-0003-0769-2153/work/61370223
dc.identifier.urihttps://hdl.handle.net/10023/19742
dc.description.abstractMulticriteria decision analysis and scenario planning are complementary tools for supporting large‐scale, strategic decision making, but there has been limited interaction between the fields. This paper describes how scenario planning can be integrated with the analytic hierarchy process (AHP), one of the most popular approaches in decision analysis and one that is arguably more accessible for new users. Scenario planners looking for an avenue into multi‐criteria decision analysis may find the AHP a useful introduction, and AHP practitioners may find in scenario planning a tool with which to address problems with large, structural or “deep” uncertainties. A common understanding of scenarios as plausible futures, rather than states of nature, is emphasized, as is how scenarios can be viewed as a kind of “meta‐attribute” over which possible courses of action can be compared. A simulation experiment assesses the potential effects of ignoring scenario‐specific information, as well as of different ways of constructing scenarios.
dc.format.extent12
dc.format.extent1437125
dc.language.isoeng
dc.relation.ispartofFutures & Foresight Scienceen
dc.subjectAnalytic hierarchy processen
dc.subjectDecision analysisen
dc.subjectMulticriteria gamesen
dc.subjectScenario Planningen
dc.subjectQA Mathematicsen
dc.subjectT-NDASen
dc.subject.lccQAen
dc.titleScenario planning in the analytic hierarchy processen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doihttps://doi.org/10.1002/ffo2.16
dc.description.statusPeer revieweden
dc.date.embargoedUntil2020-04-02


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