Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 51, Issue 10, Pages 4784-4795Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.2974924
Keywords
Decision making; Indexes; Adaptation models; Cybernetics; Classification algorithms; Clustering algorithms; Compatibility index; conflict; fuzzy c-means clustering (FCM); multicriteria large-scale group decision making (LSGDM); subgroup-to-subgroup
Categories
Funding
- National Natural Science Foundation of China [71771156]
- Spanish Ministry of Science and Universities Funds [TIN2016-75850-R]
Ask authors/readers for more resources
This article proposes a dynamic adaptive subgroup-to-subgroup conflict model to address multicriteria large-scale Group Decision Making (GDM) problems. By introducing a compatibility index, fuzzy c-means clustering algorithm, and conflict resolution model, it effectively manages conflicts in expert groups to enhance decision-making efficiency and accuracy.
The current societal demands and technological developments have resulted in the participation of a large number of experts in making decisions as a group. Conflicts are imminent in groups and conflict management is complex and necessary especially in a large group. However, there are few studies that quantitatively research the conflict detection and resolution in the large-group context, especially in the multicriteria large-group decision making (GDM) context. This article proposes a dynamic adaptive subgroup-to-subgroup conflict model to solve multicriteria large-scale GDM problems. A compatibility index is proposed based on two kinds of conflicts among experts: 1) cognitive conflict and 2) interest conflict. Then, the fuzzy c-means clustering algorithm is used to classify experts into several subgroups. A subgroup-to-subgroup conflict detection method and a weight-determination approach are developed based on the clustering results. Afterward, a conflict resolution model, which can dynamically generate feedback suggestion, is introduced. Finally, an illustrative example is provided to demonstrate the effectiveness and applicability of the proposed model.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available