Social Network Group Consensus Model Considering Quantum Cognition-Based Social Interaction Pattern and Individual Utility

To promote consensus, various consensus feedback models driven by social relationships (SRs) have been proposed for social network (SN) group decision-making (SNGDM). However, insufficient attention has been paid to how decision-makers (DMs) interact and to the utility they derive from such social interactions. Therefore, a consensus decision process considering social interaction probability and individual utility is proposed. First, this article explores the direct and indirect social interaction patterns among DMs. A quantum-like Bayesian network (BN) is constructed within the SN environment and combined with similarity effect to infer the probabilities of social interactions. Second, individual utility functions are developed based on two dimensions: 1) decision outcomes and 2) social interaction gains. Third, a two-stage consensus feedback model is proposed. It matches reference opinions by assessing the necessity of establishing new SRs and uses adaptive feedback coefficients to balance group consensus with individual utility. Finally, an illustrative example verifies the feasibility of the proposed model. Subsequent comparisons and simulations further demonstrate its effectiveness and advantages.