Similarity–trust network for clustering‐based consensus group decision‐making model

Abstract

Trust relation, as defined in Social Network Analysis (SNA), is one of the recent notions considered in decision making. This inspired our integration of trust relation in constructing a similarity–trust network. Similarity of experts' preferences is analyzed inclusively with trust relation by defining a new combination function of both attributes. The agglomerative hierarchical clustering approach is applied to group experts into subclusters based on the constructed similarity—trust degrees. The centrality concept from SNA is then used to determine the expert's similarity–trust centrality (STC) index, which is the basis for the construction of a new aggregation operator, STC-induced ordered weighted averaging operator, to fuse the individual experts' preferences into a collective one, from which the consensus solution is derived. An analysis of results with different levels of trust degree is carried out. We show that this new idea is promising and relevant to be used in solving certain consensus group decision-making problems.