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dc.contributor.authorCabrerizo, Francisco Javieren
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorPerez, Ignacio Javieren
dc.contributor.authorMata, Franciscoen
dc.contributor.authorAlons, Sergioen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.date.accessioned2017-07-10T09:34:34Z
dc.date.available2017-07-10T09:34:34Z
dc.date.issued2017-07-02
dc.identifier.citationCabrerizo, F.J. et al. (2017) A Feedback Mechanism Based on Granular Computing to Improve Consensus in GDM. In Collan, Mikael, Kacprzyk, Janusz (Eds.) Soft computing applications for group decision-making and consensus modeling. Springer, pp. 371-390en
dc.identifier.isbn9783319602066
dc.identifier.isbn9783319602073
dc.identifier.urihttp://hdl.handle.net/2086/14297
dc.description.abstractGroup decision making is an important task in real world activities. It consists in obtaining the best solution to a particular problem according to the opinions given by a set of decision makers. In such a situation, an important issue is the level of consensus achieved among the decision makers before making a decision. For this reason, different feedback mechanisms, which help decision makers for reaching the highest degree of consensus possible, have been proposed in the literature. In this contribution, we present a new feedback mechanism based on granular computing to improve consensus in group decision making problems. Granular computing is a framework of designing, processing, and interpretation of information granules, which can be used to obtain a required flexibility to improve the level of consensus within the group of decision makers.en
dc.language.isoenen
dc.publisherSpringer International Publishingen
dc.subjectGroup decision makingen
dc.subjectConsensusen
dc.subjectFeedback mechanismen
dc.subjectGranular computingen
dc.titleA Feedback Mechanism Based on Granular Computing to Improve Consensus in GDMen
dc.typeBook chapteren
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-60207-3_22
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderThe authors would like to acknowledge FEDER financial support from the Projects TIN2013-40658-P and TIN2016-75850-P.en
dc.projectidThe authors would like to acknowledge FEDER financial support from the Projects TIN2013-40658-P and TIN2016-75850-P.en
dc.cclicenceN/Aen
dc.date.acceptance2017-07-02en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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