Fuzzy Information Evolution with Three-Way Decision in Social Network Group Decision-Making

• URL: http://arxiv.org/abs/2505.16781v1
• Date: Thu, 22 May 2025 15:26:48 GMT
• Title: Fuzzy Information Evolution with Three-Way Decision in Social Network Group Decision-Making
• Authors: Qianlei Jia, Xinliang Zhou, Ondrej Krejcar, Enrique Herrera-Viedma,
• Abstract summary: In group decision-making (GDM) scenarios, uncertainty, dynamic social structures, and vague information present major challenges.<n>This study proposes a novel social network group decision-making framework that integrates three-way decision (3WD) theory, dynamic network reconstruction, and linguistic opinion representation.
• Score: 22.992898531210326
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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