A Novel Fairness-Oriented Consensus Approach with Multi-Relationship Interaction for Group Decision-Making

With the rapid advancement of intelligent decision-making systems, group decision-making (GDM) suffers from insufficient fairness and defective measurement methods, which leads to consensus quantification deviations and low public recognition, thereby restricting the practical application and efficiency of intelligent GDM. To address this issue, this paper develops a fairness-oriented group consensus method from a multi-relationship interaction perspective to eliminate negative impacts stemming from abnormal decision-maker-alternative correlations and consensus gaming behaviors. In the opinion collection stage, behavioral anomaly identification and regulation rules are established to eliminate interference factors such as potential interest ties and subjective preference biases and purify original decision information. In the consensus negotiation stage, a novel two-dimensional guidance mechanism is constructed. By defining the coordination role of formal leaders based on the group opinions and identifying implicit opinion leaders via social network analysis, the developed method realizes dual opinion anchoring and balances decision fairness and convergence efficiency. Case studies and comparative experiments demonstrate that this method effectively suppresses information distortion and individual gaming, improves consensus fairness and decision rationality, and provides a feasible theoretical and technical basis for the practical deployment of intelligent GDM systems in complex scenarios.