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dc.contributor.authorCabrerizo, Francisco Javieren
dc.contributor.authorPérez, Ignacio Javieren
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.date.accessioned2017-01-23T15:40:02Z
dc.date.available2017-01-23T15:40:02Z
dc.date.issued2017-01-14
dc.identifier.citationCabrerizo, F.J., Perez, I.J., Chiclana, F. and Herrera-Viedma, E. (2017) Group Decision Making: Consensus Approaches based on Soft Consensus Measures. In: Vicenc Torra et al. (Eds.): Fuzzy sets, rough sets, multisets and clustering. Springer International Publishing Switzerland, pp. 307--321.en
dc.identifier.isbn9783319475561
dc.identifier.issn1860-949X
dc.identifier.urihttp://hdl.handle.net/2086/13202
dc.description.abstractA group decision making situation involves multiple decision makers communicating with others to reach a decision. In such a situation, the most important issue is to obtain a decision that is best acceptable by the decision makers, and, therefore, consensus has attained a great attention and it is a major goal of group decision making situations. To measure the closeness among the opinions given by the decision makers, different approaches have been proposed. At the beginning, consensus was meant to be a unanimous and full agreement. However, because this situation is often not reachable in practice, the use of a softer consensus, which assesses the level of agreement in a more flexible way and reflects the large spectrum of possible partial agreements, is a more reasonable approach. Soft consensus approaches better reflects a real human perception of the essence of consensus and, therefore, they have been widely used. The purpose of this contribution is to review the different consensus approaches based on soft consensus measures that have been proposed.en
dc.language.isoenen
dc.publisherSpringer International Publishingen
dc.subjectGroup decision makingen
dc.subjectConsensusen
dc.subjectFuzzy set theoryen
dc.titleGroup Decision Making: Consensus Approaches Based on Soft Consensus Measuresen
dc.typeBook chapteren
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-47557-8_18
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderThe authors would like to acknowledge FEDER financial support from the Project TIN2013-40658-P, and also the financial support from the Andalusian Excellence Project TIC-5991.en
dc.projectidThe authors would like to acknowledge FEDER financial support from the Project TIN2013-40658-P, and also the financial support from the Andalusian Excellence Project TIC-5991.en
dc.cclicenceCC-BY-NC-NDen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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