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| Trust-Driven Three-Way Conflict Analysis Model Based on the Best-Worst Method |
| ZHU Junjie1,2, ZHANG Qinghua1,2,3, LUO Nanfang1,3 |
1. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065;
2. Chongqing Key Laboratory of Computational Intelligence, Chong-qing University of Posts and Telecommunications, Chongqing 400065;
3. Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education, Chongqing University of Posts and Te-lecommunications, Chongqing 400065 |
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Abstract In traditional conflict analysis, agents are typically assumed to be independent, and the latent connections and status inequality among agents in social networks are neglected. Furthermore, trust relationships in social networks are often incomplete, and the scientific determination of the issue weights is also a key factor in mitigating conflict. To address these issues, a trust-driven three-way conflict analysis model based on the best-worst method(TBWM-3WCA) is proposed. First, to address the issue of incomplete trust relationships in social networks, the path penalty coefficient and the Einstein product are utilized to simulate trust propagation and complete the trust matrix. Second, to reflect the differences in agents influence within a group, latent relationships among agents are explored to derive influence weights. Then, the influence weights are employed to aggregate group attitudes and objectively identify the most and least supported issues in conflict scenarios. BWM is subsequently incorporated to determine the issue weights. Finally, a dynamic feedback mechanism based on the system conflict degree is incorporated to iteratively adjust the attitudes of agents, thereby promoting consensus formation and conflict convergence. Case studies and comparative experiments demonstrate the effectiveness of the proposed model in resolving conflicts.
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Received: 28 February 2026
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| Fund:National Natural Science Foundation of China(No.62276038,62576056), Joint Fund of Chongqing Natural Science Foundation for Innovation and Development(No.CSTB2023NSCQ-LZX0164) |
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Corresponding Authors:
ZHANG Qinghua, Ph.D., professor. His research interests include uncertain information processing, multi-granularity cognitive computing and intelligent analysis of big data.
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About author:: ZHU Junjie, Master student. His research interests include three-way decision and conflict analysis.
LUO Nanfang, Ph.D. candidate. Her research interests include three-way decision, intelligent decision-making and uncertain information processing. |
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2026, Vol. 39 
