A Dynamic Adaptive Subgroup-to-Subgroup Compatibility-Based Conflict Detection and Resolution Model for Multicriteria Large-Scale Group Decision Making

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.

Automated summary

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.