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dc.contributor.authorDong, Yucheng
dc.contributor.authorYuxiang, Fan
dc.contributor.authorHaiming, Liang
dc.contributor.authorChiclana, Francisco
dc.contributor.authorHerrera-Viedma, Enrique
dc.date.accessioned2019-03-26T16:01:37Z
dc.date.available2019-03-26T16:01:37Z
dc.date.issued2019-03-20
dc.identifier.citationDong, Y., Fan, Y., Liang, H., Chiclana, F. and Herrera-Viedma, E. (2019) Preference evolution with deceptive interactions and heterogeneous trust in bounded confidence model: A simulation analysis. Knowledge-Based Systems, 175, pp. 87-95en
dc.identifier.issn0950-7051
dc.identifier.urihttps://www.dora.dmu.ac.uk/handle/2086/17649
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.abstractThe bounded confidence model is a popular tool to model the evolution of preferences and knowledge in opinion dynamics. In the bounded confidence model, it is assumed that all agents are honest to express their preferences and knowledge. However, in real-life opinion dynamics, agents often hide their true preferences, and express different preferences to different people. In this paper, we propose the evolution of preferences with deceptive interactions and heterogeneous trust in bounded confidence model, in which some agents will express three types of preferences: true preferences, communicated preferences and public preferences. In the proposed model, the communication regimes of the agents are established. Based on the established communication regimes, the true preferences, communicated preferences and public preferences of the agents are updated. Furthermore, we use an agent-based simulation to unfold the influences of the deceptive interactions and heterogeneous trust on the evolutions of preferences.en
dc.language.isoenen
dc.publisherElsevieren
dc.titlePreference evolution with deceptive interactions and heterogeneous trust in bounded confidence model: A simulation analysis.en
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.knosys.2019.03.010
dc.peerreviewedYesen
dc.funderOther external funder (please detail below)en
dc.projectidThis work was supported by the grants (No. 71571124, No. 71601133 and No. 71804148) from NSF of China, the grants (Nos. sksyl201705 and 2018hhs-58) from Sichuan University, and the grant TIN2016-75850-R supported by the Spanish Ministry of Economy and Competitiveness with FEDER funds.en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2019-03-15
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.funder.otherThis work was supported by the grants (No. 71571124, No. 71601133 and No. 71804148) from NSF of China, the grants (Nos. sksyl201705 and 2018hhs-58) from Sichuan University, and the grant TIN2016-75850-R supported by the Spanish Ministry of Economy and Competitiveness with FEDER funds.en


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