A consensus model for large-scale group decision making based on empathetic network analysis and its application in strategical selection of COVID-19 vaccines

Abstract

Effective COVID-19 vaccines are the best weapons against the COVID-19 pandemic and have become a strategic property of local governments. Experienced experts need to be invited to improve the accuracy of COVID-19 vaccine selection; such a selection process can be regarded as a large-scale group decision-making (LSGDM) problem. Many LSGDM models have been proposed in order to overcome the non-independence of experts. However, the objective empathetic relationships among experts which can affect decision results have been ignored. To fill these gaps, this article proposes a LSGDM method based on empathetic network analysis (ENA). First, we identify the dissociative empathetic network, central empathetic network, and general empathetic network. Then, we determine the results of internal preference evolution from the perspective of preference interactions. We adopt the fuzzy c-means (FCM) clustering algorithm to divide a large group of experts into several subgroups according to the empathetic centrality of experts, and then propose three kinds of feedback mechanisms with respect to the empathetic relationships for central, dissociative, and general empathetic networks to improve the quality of the consensus-reaching process. Finally, an illustrative example related to the selection of COVID-19 vaccines is presented to validate the proposed model.

Keywords:

• Group decision making
• empathetic network analysis
• consensus reaching process
• fuzzy preference relations
• COVID-19 outbreak

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China (71771156, 71971145, and 72171158).