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 |
dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |