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.
Highlights
In decision making, experts can use different representation formats to provide their opinions or preferences on a set of alternatives, whether in numerical or linguistic form
The maximal dominance set quantifies the best alternative chosen is according to ‘most of’ the central experts in the undirected weighted similarity preference network where they are contributing more to consensus, and the decision made will be agreed by the whole group of expert since the consensus level achieved is already sufficient at this stage of the resolution process
This paper proposed an undirected weighted preference network based on experts’ preference similarities and, a cluster-based consensus measure with feedback mechanism algorithm and resolution process, in conjunction with a new aggregation operator and soft majority concept
Summary
Experts can use different representation formats to provide their opinions or preferences on a set of alternatives, whether in numerical or linguistic form. Centrality, one of the important concepts in SNA, is applied in this study in: (i) deciding which experts contribute low to consensus; (ii) determining the leader in the created preference network structure; and (iii) defining a new IOWA operator, the cent-IOWA operator, with ordering of the preferences to aggregate induced via the associated experts’ centrality values, in the resolution process to allow for the implementation of the ‘soft majority’ concept via a corresponding linguistic quantifier.
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