Consensus in Group Decision Making and Social Networks

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dc.contributor.author Herrera-Viedma, Enrique en
dc.contributor.author Cabrerizo, Francisco Javier en
dc.contributor.author Chiclana, Francisco en
dc.contributor.author Wu, Jian en
dc.contributor.author Cobo, Manuel Jesus en
dc.contributor.author Konstantin, Samuylov en
dc.date.accessioned 2017-10-10T07:57:01Z
dc.date.available 2017-10-10T07:57:01Z
dc.date.issued 2017-09
dc.identifier.citation Herrera-Viedma, E. et al. (2017) Consensus in Group Decision Making and Social Networks. Studies in Informatics and Control, 26 (3) pp. 259-268 en
dc.identifier.uri http://hdl.handle.net/2086/14582
dc.description Open Access journal en
dc.description.abstract The 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.iso en en
dc.publisher ICI Publishing House en
dc.subject Consensus en
dc.subject Group decision making en
dc.subject Social networks en
dc.title Consensus in Group Decision Making and Social Networks en
dc.type Article en
dc.identifier.doi http://dx.doi.org/10.24846/v26i3y201701
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en
dc.funder The 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.projectid The 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.cclicence CC-BY-NC en
dc.date.acceptance 2017-09-01 en
dc.exception.reason open access journal en


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