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

Date

2023-03

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE

Type

Article

Peer reviewed

Yes

Abstract

This paper proposes a novel framework to manage subgroups’ non-cooperative behavior by weight penalty in large scale group decision making (LSGDM). 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 non-cooperative. 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.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Large-scale group decision making, Noncooperative behavior, Unbalanced linguistic term sets, Weight penalty mechanism, Trust-consensus index

Citation

Sun, Q., Chiclana, F., Wu, J., Liu, Y., Liang, C. and Viedma, E.H. (2023) Weight Penalty Mechanism for Noncooperative Behavior in Large-Scale Group Decision Making With Unbalanced Linguistic Term Sets. IEEE Transactions on Fuzzy Systems. 31 (10), pp. 3507-3521

Rights

Research Institute

Institute of Artificial Intelligence (IAI)