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

Xiaofang Li and Huchang Liao

Journal of the Operational Research Society, 2023, vol. 74, issue 2, 604-621

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.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2022.2064782 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:74:y:2023:i:2:p:604-621

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2022.2064782

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().