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dc.contributor.authorHerrera-Viedma, Enriqueen
dc.contributor.authorCabrerizo, Francisco Javieren
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorWu, Jianen
dc.contributor.authorCobo, Manuel Jesusen
dc.contributor.authorKonstantin, Samuyloven
dc.date.accessioned2017-10-10T07:57:01Z
dc.date.available2017-10-10T07:57:01Z
dc.date.issued2017-09
dc.identifier.citationHerrera-Viedma, E. et al. (2017) Consensus in Group Decision Making and Social Networks. Studies in Informatics and Control, 26 (3) pp. 259-268en
dc.identifier.urihttp://hdl.handle.net/2086/14582
dc.descriptionOpen Access journalen
dc.description.abstractThe consensus reaching process is the most important step in a group decision making scenario. This step is most frequently identified as a process consisting of some discussion rounds in which several decision makers, which are involved in the problem, discuss their points of view with the purpose of obtaining the maximum agreement before making the decision. Consensus reaching processes have been well studied and a large number of consensus approaches have been developed. In recent years, the researchers in the field of decision making have shown their interest in social networks since they may be successfully used for modelling communication among decision makers. However, a social network presents some features differentiating it from the classical scenarios in which the consensus reaching processes have been applied. The objective of this study is to investigate the main consensus methods proposed in social networks and bring out the new challenges that should be faced in this research field.en
dc.language.isoenen
dc.publisherICI Publishing Houseen
dc.subjectConsensusen
dc.subjectGroup decision makingen
dc.subjectSocial networksen
dc.titleConsensus in Group Decision Making and Social Networksen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.24846/v26i3y201701
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderThe authors would like to thank FEDER financial support from the Projects TIN2013-40658-P and TIN2016-75850-P. This paper was also supported by both the RUDN University Program 5-100 and National Natural Science Foundation of China (NSFC) (No.71571166).en
dc.projectidThe authors would like to thank FEDER financial support from the Projects TIN2013-40658-P and TIN2016-75850-P. This paper was also supported by both the RUDN University Program 5-100 and National Natural Science Foundation of China (NSFC) (No.71571166).en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-09-01en
dc.exception.reasonopen access journalen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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