Weight Penalty Mechanism for Noncooperative Behavior in Large-Scale Group Decision Making With Unbalanced Linguistic Term Sets

This article proposes a novel framework to manage subgroups' noncooperative behavior by weight penalty in large-scale group decision making. To do that, a trust-consensus index (TCI) is defined by combining trust score and consensus degree among experts expressed by unbalanced linguistic term sets. A Louvain algorithm clustering process based on undirected graph composed of TCI is introduced to detect the subgroups in large network. Hence, a weight penalty feedback model is established to manage the subgroups detected as discordant and noncooperative. The proposed method novelty resides in that the minimum adjustment cost can be obtained with respect to the penalty parameter. A detail analysis regarding the computation of the optimal penalty parameter to prevent excessive penalization is reported. Finally, a detailed numerical and comparative analyses are provided to verify the validity of the proposed method.