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

TL;DR

This paper introduces a novel social network group decision-making framework that combines three-way decision theory, dynamic network updates, and linguistic opinions to better handle uncertainty and vagueness in social decision processes.

Contribution

It presents an integrated multi-agent decision model that explicitly accounts for hesitation, adapts social links based on opinions, and uses linguistic terms for subjective information.

Findings

Enhanced decision stability demonstrated in simulations.

Effective handling of vague and incomplete information.

Improved reflection of social relationship dynamics.

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.