Consensus reaching mechanism with parallel dynamic feedback strategy for large-scale group decision making under social network analysis

Abstract

With the scale of group decision making increasing, it is a crucial issue to get the utmost of collective intelligence for seeking the optimal solution. In this study, we propose an automatic consensus reaching process (CRP) for large-scale group decision making (LSGDM) based on parallel dynamic feedback strategy and two-dimensional scenario-based social network analysis (SNA) model. Firstly, individuals express their preferences by distributed preference relations (DPRs) which could keep the uncertainty of assessment and allow multi-attribute comparison. Secondly, SNA based on trust relationship and connection strength is implemented. Then a two-dimensional scenario-based SNA model is established, and a fuzzy clustering algorithm based on connection strength is designed to reduce the scale of decision makers (DMs). Finally, a two-phase CRP with identification rules and feedback strategy is constructed. Identification rules are used for activating different kinds of feedback mechanisms by identifying whether it reaches acceptable local or global consensus. The rules also identify which kind of social relationship for internal or external subgroups and what dominance does individual or subgroup has. Feedback strategy with parallel dynamic adjustment process is further designed based on opinion and trust adjustment factors and non-cooperative behaviors. A real illustrative case for selecting the optimal carbon footprint management provider is presented to demonstrate the validity of our proposed method, and further compare it with other current methods.