Fuzzy Clustering-Based Three-Way Asynchronous Consensus for Identifying Manipulative and Herd Behaviors
Jiaxin Zhan, Mingjie Cai, Qingguo Li
Abstract
In the era of Big Data, the integration and fusion of heterogeneous information have become essential for addressing complex decision-making challenges in large-scale group decision-making (LSGDM), where expanding scale and diverse participant behaviors increasingly demand advanced techniques to enhance efficiency, fairness, and accuracy. To meet these demands, this article proposes a novel framework that leverages information-fusion principles to optimize group decision-making processes. First, an enhanced fuzzy C-means algorithm, termed Trust-based regularized fuzzy C-means, is introduced. By incorporating a regularization term derived from trust relationships, it improves clustering precision and enables finer segmentation of decision-makers (DMs), thus laying solid groundwork for consensus. Building on this, we develop an optimization model integrating three-way decision theory to resolve asynchronous differences among experts. The model uses dynamic trust updates to guide DMs from the negative region toward those in the positive region, with special emphasis on rapid consensus in emergencies. Furthermore, the study, for the first time, systematically investigates manipulative and herd behaviors within large datasets; adopting a cautious strategy to mitigate their impact on fairness and effectiveness, it embeds pseudotrust identification into the framework to further refine the decision environment. Extensive case studies and comparative experiments demonstrate that the proposed method significantly improves decision-making efficiency, fairness, and accuracy, offering new perspectives and practical tools for managing complex information and optimizing group behaviors in LSGDM.