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dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorWu, Jianen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.date.accessioned2015-01-08T09:13:54Z
dc.date.available2015-01-08T09:13:54Z
dc.date.issued2014-10
dc.identifier.citationChiclana, F. Wu, J. Herrera-Viedma, E. (2014) Visual consensus feedback mechanism for group decision making with complementary linguistic preference relations. In: Vicenc Torra et al. (Eds.): MDAI 2014, LNAI 8825, pp. 72–83en
dc.identifier.isbn9783319120546
dc.identifier.urihttp://hdl.handle.net/2086/10549
dc.description.abstractA visual consensus feedback mechanism for group decision making (GDM) problems with complementary linguistic preference relations is presented. Linguistic preferences are modelled using triangular fuzzy membership functions, and the concepts of similarity degree (SD) between two experts as well as the proximity degree (PD) between an expert and the rest of experts in the group are defined and used to measure the consensus level (CL). A feedback mechanism is proposed to identify experts, alternatives and corresponding preference values that contribute less to consensus. The novelty of this feedback mechanism is that it provides experts with visual representations of their consensus status to easily see their consensus position within the group as well as to identify the alternatives and preference values that should be reconsidered for changing in the subsequent consensus round. The feedback mechanism also includes individualised recommendations to those identified experts on changing their identified preference values and visual graphical simulation of future consensus status if the recommended values were to be implemented.en
dc.language.isoenen
dc.publisherSpringer International Publishing Switzerlanden
dc.subjectGroup decisions makingen
dc.subjectConsensusen
dc.subjectlinguistic preferencesen
dc.subjectVisual feedback mechanismen
dc.titleVisual consensus feedback mechanism for group decision making with complementary linguistic preference relationsen
dc.typeBook chapteren
dc.identifier.doihttps://doi.org/10.1007/978-3-319-12054-6_7
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderFEDER financial support from the Project FUZZYLING-II Project TIN2010-17876en
dc.funderAndalusian Excellence Projects TIC-05299 and TIC-5991en
dc.funderUniversity of Granada Excellence campus GENIL-BioTIC-UGR Research Visit programmeen
dc.funderNational Natural Science Foundation of China (NSFC) under the Grant No.71101131 and No.713311002en
dc.funderZhejiang Provincial National Science Foundation for Distinguished Young Scholars of China under the Grant No. LR13G010001en
dc.projectidTIN2010-17876en
dc.projectidTIC-05299en
dc.projectidTIC-5991en
dc.projectidNSFC Grants: No.71101131 and No.713311002en
dc.projectidZhejiang Provincial NSFDYSC LR13G010001en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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