Fuzzy Information Evolution with Three-Way Decision in Social Network Group Decision-Making
This addresses challenges in group decision-making for scenarios like multi-UAV cooperation, though it appears incremental as it combines existing theories into a new framework.
The study tackled uncertainty and dynamic social structures in group decision-making by proposing a social network group decision-making framework integrating three-way decision theory, dynamic network reconstruction, and linguistic opinion representation, with simulation results demonstrating effectiveness in enhancing system stability and representing realistic behaviors.
In group decision-making (GDM) scenarios, uncertainty, dynamic social structures, and vague information present major challenges for traditional opinion dynamics models. To address these issues, this study proposes a novel social network group decision-making (SNGDM) framework that integrates three-way decision (3WD) theory, dynamic network reconstruction, and linguistic opinion representation. First, the 3WD mechanism is introduced to explicitly model hesitation and ambiguity in agent judgments, thereby preventing irrational decisions. Second, a connection adjustment rule based on opinion similarity is developed, enabling agents to adaptively update their communication links and better reflect the evolving nature of social relationships. Third, linguistic terms are used to describe agent opinions, allowing the model to handle subjective, vague, or incomplete information more effectively. Finally, an integrated multi-agent decision-making framework is constructed, which simultaneously considers individual uncertainty, opinion evolution, and network dynamics. The proposed model is applied to a multi-UAV cooperative decision-making scenario, where simulation results and consensus analysis demonstrate its effectiveness. Experimental comparisons further verify the advantages of the algorithm in enhancing system stability and representing realistic decision-making behaviors.