Complex Intuitionistic Fuzzy Trust Propagation-Based Bilayer Coupled Social Network Group Consensus Model with Opinion Evolution

In social network group decision-making (SNGDM), it is difficult for decision-makers (DMs) to reach a consensus owing to individuals' opinions being influenced by others and evolving over time. Moreover, the existing social trust functions are incapable for depicting uncertainty and interference effect of trusts simultaneously, which are driven by the unpredictable human cognition. To address these problems, this study proposes a novel complex intuitionistic fuzzy trust propagation-based group consensus model with opinion evolution under bilayer coupled social network. Firstly, the novel concept of complex intuitionistic fuzzy trust (CIFT) function is put forward to model the direct trust between DMs in SNGDM. Secondly, trust propagation operator on CIFTs is presented based on Einstein operations to derive the indirect trust between DMs. Then, a novel quantum weighted averaging trust aggregation operator considering interference effect is proposed to aggregate multiple trust propagation paths into a collective one. Thirdly, the propagation and aggregation of CIFTs under bilayer coupled social network are explored by obtaining the projection of the two-layer network. In addition, degree-centrality and clustering coefficient are further calculated to derive DMs' weights. Fourthly, the network partition algorithm is conducted to divide the projection network into several sub-networks and a novel consensus optimization model with opinion evolution is developed to guide leaders on adjusting their opinions during consensus reaching process. Eventually, an illustrative case on joint venture capital is provided to show the feasibility of above methods. The superiority and rationality of our proposal are further revealed by a series of simulations and comparative analysis.