Preference Similarity Network Structural Equivalence Clustering based Consensus Model

De Montfort University Open Research Archive

Show simple item record Chiclana, Francisco en Levesley, Jeremy en Hanimah Kamis, Nor en 2017-11-28T13:31:25Z 2017-11-28T13:31:25Z 2017-11-21
dc.identifier.citation Hanimah Kamis et al. (2017) Preference Similarity Network Structural Equivalence Clustering based Consensus Model. Applied Soft Computing en
dc.description Open access article en
dc.description.abstract Social network analysis (SNA) methods have been developed to analyse social structures and patterns of network relationships, although they have been least explored and/or exploited purposely for decision-making processes. In this study, we bridge a gap between SNA and consensus-based decision making by defining undirected weighted preference network from the similarity of expert preferences using the concept of ‘structural equivalence’. Structurally equivalent experts are represented using the agglomerative hierarchical clustering algorithm with complete link function, thus intra-clusters’ experts are high in density and inter-clusters’ experts are rich in sparsity. We derive cluster consensus based on internal and external cohesions, while group consensus is obtained by identifying the highest level consensus at optimal level of clustering. Thus, the clustering based approach to consensus measure contributes to present homogeneity of experts preferences as a whole. In the event of insufficient group consensus state, we construct a feedback mechanism procedure based on clustering that consists of three main phases: (1) identification of experts that contribute less to consensus; (2) identification of a leader in the network; and (3) advice generation. We make use of the centrality concept in SNA as a way of determining the most important person in a network, who is presented as a leader to provide advices in the feedback process. It is proved that the implementation of the proposed feedback mechanism increases consensus and, because of the bounded condition of consensus measure, convergence to sufficient group agreement is guaranteed. The centrality concept is also applied in the construction of a new aggregation operator, namely as cent-IOWA operator, that is used to derive the collective preference relation from which the feasible alternative of consensus solution, based on the concept of dominance, is achieved according to a majority of the central experts in the network, which is represented in this paper by the linguistic quantifier ‘most of.’ For validation purposes, an existing literature study is used to perform a comparative analysis from which conclusions are drawn and explained. en
dc.language.iso en en
dc.publisher Elsevier en
dc.subject Consensus Group Decision Making en
dc.subject Social Network Analysis en
dc.subject Opinion Similarity en
dc.subject Structural Equivalence en
dc.subject Agglomerative Hierarchical Clustering en
dc.subject IOWA-based aggregation operator en
dc.title Preference Similarity Network Structural Equivalence Clustering based Consensus Model en
dc.type Article en
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en
dc.funder N/A en
dc.projectid N/A en
dc.cclicence CC-BY-NC-ND en 2017-11-15 en

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