Fuzzy Information Evolution with Three-Way Decision in Social Network Group Decision-Making
Artikel i vetenskaplig tidskrift, 2025

In group decision-making (GDM) scenarios, uncertainty, dynamic social structures, and vague information present 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 vague and 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 algorithm's advantages in enhancing system stability and representing realistic decision-making behaviors.

Social network group decision-making (SNGDM)

Consensus analysis

Three-way decision (3WD)

Författare

Qianlei Jia

Chalmers, Elektroteknik, System- och reglerteknik

Northwestern Polytechnical University

Xinliang Zhou

Nanyang Technological University

Ondrej Krejcar

Vysoká škola báňská - Technical University of Ostrava

University of Hradec Králové

Skoda Auto

Enrique Herrera-Viedma

Instituto Andaluz Interuniversitario en Data Science and Computational Intelligence

IEEE Transactions on Fuzzy Systems

1063-6706 (ISSN) 19410034 (eISSN)

Vol. In Press

Ämneskategorier (SSIF 2025)

Datavetenskap (datalogi)

Reglerteknik

DOI

10.1109/TFUZZ.2025.3614003

Mer information

Senast uppdaterat

2025-10-07