A Feedback Mechanism Based on Granular Computing to Improve Consensus in GDM

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dc.contributor.author Cabrerizo, Francisco Javier en
dc.contributor.author Chiclana, Francisco en
dc.contributor.author Perez, Ignacio Javier en
dc.contributor.author Mata, Francisco en
dc.contributor.author Alons, Sergio en
dc.contributor.author Herrera-Viedma, Enrique en
dc.date.accessioned 2017-07-10T09:34:34Z
dc.date.available 2017-07-10T09:34:34Z
dc.date.issued 2017-07-02
dc.identifier.citation Cabrerizo, 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. 371-390 en
dc.identifier.isbn 9783319602066
dc.identifier.isbn 9783319602073
dc.identifier.uri http://hdl.handle.net/2086/14297
dc.description.abstract Group 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.iso en en
dc.publisher Springer International Publishing en
dc.subject Group decision making en
dc.subject Consensus en
dc.subject Feedback mechanism en
dc.subject Granular computing en
dc.title A Feedback Mechanism Based on Granular Computing to Improve Consensus in GDM en
dc.type Book chapter en
dc.identifier.doi http://dx.doi.org/10.1007/978-3-319-60207-3_22
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en
dc.funder The authors would like to acknowledge FEDER financial support from the Projects TIN2013-40658-P and TIN2016-75850-P. en
dc.projectid The authors would like to acknowledge FEDER financial support from the Projects TIN2013-40658-P and TIN2016-75850-P. en
dc.cclicence N/A en
dc.date.acceptance 2017-07-02 en


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