In recent years,large-scale group decision-making(LSGDM)has garnered significant scholarly attention.Given that decision-makers(DMs)may come from various departments and have distinct knowledge backgrounds,they often ...In recent years,large-scale group decision-making(LSGDM)has garnered significant scholarly attention.Given that decision-makers(DMs)may come from various departments and have distinct knowledge backgrounds,they often use heterogeneous information to express their assessments.To address these challenges,this paper proposes a new decision-making model for large groups in a social network environment.Initially,the large group is divided into communities based on the trust relationships among DMs using community detection algorithm.Subsequently,a direct method is used to process DMs’heterogeneous information.Simultaneously,we integrate the weight information of DMs and communities to calculate the preferences of the group using a weighted averaging operator.Additionally,an innovative feedback mechanism is designed,which takes into account the bounded confidence of experts and the influence of community leaders,to enhance the consensus level.Finally,the feasibility and effectiveness of the proposed model are demonstrated through a specific case study.展开更多
基金supported by National Natural Science Foundation of China:[Grant Number 72201066].
文摘In recent years,large-scale group decision-making(LSGDM)has garnered significant scholarly attention.Given that decision-makers(DMs)may come from various departments and have distinct knowledge backgrounds,they often use heterogeneous information to express their assessments.To address these challenges,this paper proposes a new decision-making model for large groups in a social network environment.Initially,the large group is divided into communities based on the trust relationships among DMs using community detection algorithm.Subsequently,a direct method is used to process DMs’heterogeneous information.Simultaneously,we integrate the weight information of DMs and communities to calculate the preferences of the group using a weighted averaging operator.Additionally,an innovative feedback mechanism is designed,which takes into account the bounded confidence of experts and the influence of community leaders,to enhance the consensus level.Finally,the feasibility and effectiveness of the proposed model are demonstrated through a specific case study.
