Novel consensus-reaching model in the social network environment for large-group emergency decision-making: an approach to managing non-cooperative behaviors

Given the complexity and uncertainty of emergency decision-making for satellite emergency observation schemes, it often involves the participation of multiple decision makers (DMs), which results in difficulties with the implementation of large-group emergency decision-making (LGEDM). Meanwhile, LGEDM requires a high-quality emergency scheme within a limited period of time and thus rational treatment of non-cooperative behaviors is crucial to guarantee the performance and timeliness of the consensus-reaching process (CRP). To this end, this study proposes a novel consensus-reaching model in the social network environment for LGEDM, which aims at addressing non-cooperative behaviors. Firstly, we combine social network analysis and the modularity-based Louvain clustering algorithm to cluster DMs and reduce the complexity of LGEDM. Subsequently, we present a hierarchical feedback adjustment mechanism, where non-cooperative clusters and DMs undergo opinion adjustment without changing the clustering structure. In this way, a non-cooperative behavior management mechanism in CRP is established, which is capable of handling six different types of non-cooperative behaviors. Finally, a case study verifies the feasibility of the proposed model, and a comparative analysis illustrates the superiority of the model in clustering and managing non-cooperative behaviors in LGEDM.

Automated summary

This study proposes a novel consensus-reaching model in the social network environment for large-group emergency decision-making (LGEDM), which addresses non-cooperative behaviors. The model combines social network analysis and the modularity-based Louvain clustering algorithm to cluster decision makers and reduce complexity. A hierarchical feedback adjustment mechanism is introduced to manage non-cooperative behaviors without changing the clustering structure. A case study demonstrates the feasibility of the proposed model and comparative analysis showcases its superiority in clustering and managing non-cooperative behaviors in LGEDM.