Collusion core-Nash ordinal consensus mechanism for large-scale group decision-making
针对大规模群体决策中序数偏好信息的共识问题，提出集成框架，包括改进的模糊C均值算法、基于多数原则的序数共识度量、两阶段核心-纳什讨价还价调整机制，并分析子群合谋策略，通过疫情控制案例验证有效性。
Large-scale group decision-making (LSGDM) using ordinal preference information provides an effective means of addressing the complexities of decision-making problems by streamlining the decision-making process and alleviating the burden on decision makers (DMs). Considering these benefits and current research limitations, this study introduces an integrated framework designed to facilitate ordinal consensus in LSGDM. Firstly, to enhance computational efficiency and address the shortcomings of traditional methodologies, an Improved Fuzzy C-Means for Applications with Noise (IFCMAN) algorithm is developed. Secondly, a new ordinal consensus measure grounded in the majority principle is introduced. Thirdly, recognising the interactive nature of consensus formation, cooperative game theory is employed to design a consensus-adjustment mechanism via a two-stage core-Nash bargaining approach. Furthermore, subgroup collusion is analysed, culminating in the characterisation of an optimal collusion strategy. Finally, the effectiveness and feasibility of the proposed method are illustrated through a pandemic-control case study, accompanied by relevant analyses. The results demonstrate that the proposed approach provides a robust and practical framework for enhancing consensus building in LSGDM with ordinal preferences.