Balance Dynamic Clustering Analysis and Consensus Reaching Process with Consensus Evolution Networks in Large-scale Group Decision Making

Abstract

Nowadays, large-scale group decision making is usually handled based on clustering analysis process (CAP) and consensus reaching process (CRP). However, CAP and CRP can be contradictory since CAP is performed based on differences between potentially small groups and CRP is conducted to improve the overall similarity of a large group. To balance CAP and CRP, we propose a dynamic clustering analysis process (DCAP) based on consensus evolution networks. A clustering algorithm proposed based on a commonly used community detection method can be shown as a way to handle the diverse network structures with dynamic consensus thresholds. We evaluate the clustering validity based on the intra-consensus levels in subgroups and the inter-consensus level among subgroups. Then, we reanalyze the DCAP after each feedback adjustment round in CRP. In such a way, effective clustering can also be found after a satisfying consensus is reached. Finally, we include a case study to show the availability of this study and provide a comparative analysis to highlight its advantages from a theoretical and numerical perspective.