An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making

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dc.cclicenceCC-BY-NCen
dc.contributor.authorWu, Jian
dc.contributor.authorCao, Mingshuo
dc.contributor.authorChiclana, Francisco
dc.contributor.authorDong, Yucheng
dc.contributor.authorHerrera-Viedma, Enrique
dc.date.acceptance2020-03-27
dc.date.accessioned2020-04-08T14:50:30Z
dc.date.available2020-04-08T14:50:30Z
dc.date.issued2020-04-06
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractA novel framework to prevent manipulation behaviour in consensus reaching process under social network group decision making is proposed, which is based on a theoretically sound optimal feedback model. The manipulation behaviour classification is twofold: (1) ‘individual manipulation’ where each expert manipulates his/her own behaviour to achieve higher importance degree (weight); and (2) ‘group manipulation’ where a group of experts force inconsistent experts to adopt specific recommendation advices obtained via the use of fixed feedback parameter. To counteract ‘individual manipulation’, a behavioural weights assignment method modelling sequential attitude ranging from ‘dictatorship’ to ‘democracy’ is developed, and then a reasonable policy for group minimum adjustment cost is established to assign appropriate weights to experts. To prevent ‘group manipulation’, an optimal feedback model with objective function the individual adjustments cost and constraints related to the threshold of group consensus is investigated. This approach allows the inconsistent experts to balance group consensus and adjustment cost, which enhances their willingness to adopt the recommendation advices and consequently the group reaching consensus on the decision making problem at hand. A numerical example is presented to illustrate and verify the proposed optimal feedback model.en
dc.funderOther external funder (please detail below)en
dc.funder.otherNational Natural Science Foundation of China (NSFC)en
dc.funder.otherFEDER fundsen
dc.identifier.citationWu, J., Cao, M., Chiclana, F., Dong, Y., Herrera-Viedma, E. (2020) An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making. IEEE Transactions on Fuzzy Systems,en
dc.identifier.doihttps://doi.org/10.1109/tfuzz.2020.2985331
dc.identifier.issn1063-6706
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/19489
dc.language.isoenen
dc.peerreviewedYesen
dc.projectidNo.71971135,71571166en
dc.projectidNational Spanish project TIN2016-75850-Ren
dc.publisherIEEE Xploreen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectConsensusen
dc.subjectsocial networken
dc.subjectgroup decision makingen
dc.subjectfeedback processen
dc.subjectmanipulation behaviouren
dc.subjectadjustment costen
dc.titleAn optimal feedback model to prevent manipulation behaviours in consensus under social network group decision makingen
dc.typeArticleen

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