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dc.contributor.authordel Moral, Maria Joseen
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorTapia Garcia, Juan Miguelen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.date.accessioned2017-09-20T10:31:45Z
dc.date.available2017-09-20T10:31:45Z
dc.date.issued2017-12-12
dc.identifier.citationDel Moral, M.J., Chiclana, F., Tapia Garcia, J.M. and Herrera-Viedma, E. (2017) An alternative calculation of the consensus degree in group decision making problems. Procedia Computer Science, 122, pp. 735-742en
dc.identifier.urihttp://hdl.handle.net/2086/14518
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractIn a problem of group decision-making it is desirable to obtain a solution with the highest possible degree of agreement –consensus- among the participants. For this aim, it is necessary to have tools that facilitate the calculation of the degree of consensus in a reliable way. This study proposes a consensus index based on a statistical measure of variability of the preferences expressed by the experts in a group decision-making process and performs a specific comparative study between this index and several known consensus measures. The analysis shows that in this specific situation the proposed measure behaves in a similar way to the previous ones and it could play their role in a process of decision making in group.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectGroup decision makingen
dc.subjectfuzzy preferencesen
dc.subjectconsensusen
dc.subjectdecision support rulesen
dc.subjectdistance functionsen
dc.subjectmean absolute deviationen
dc.titleAn alternative calculation of the consensus degree in group decision making problemsen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1016/j.procs.2017.11.431
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderThe authors would like to acknowledge FEDER financial support from the Project TIN2016-75850-R.en
dc.projectidThe authors would like to acknowledge FEDER financial support from the Project TIN2016-75850-R.en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2017-09-07en
dc.exception.reasongold open accessen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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