Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection

Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns, leading to non-cooperative behaviors during the consensus-reaching process. Many studies on non-cooperative behavior management assumed that the maximum degree of cooperation of experts is to totally accept the revisions suggested by the moderator, which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process. In addition, when grouping a large group into subgroups by clustering methods, existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously. In this study, we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensus model taking into account the altruistic behaviors of experts. First, we cluster experts into subgroups by a constrained K -means clustering algorithm according to the opinion similarity and trust relationship of experts. Then, we calculate the weights of experts and clusters based on the centrality degrees of experts. Next, to enhance the quality of consensus reaching, we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts. A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making. The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.

Automated summary

Emergency decision-making problems often involve experts from different backgrounds who exhibit non-cooperative behaviors during the consensus-reaching process. Previous studies focused on experts' maximum cooperation as accepting revisions, limiting contributions by altruistic individuals. Additionally, existing studies grouped experts based on evaluation similarity or trust separately, without considering both factors simultaneously. This study introduces a clustering method considering evaluation similarity and trust relations and develops a consensus model incorporating experts' altruistic behaviors. Numerical results demonstrate the effectiveness of the proposed method, highlighting the importance of integrating altruistic behavior analysis to safeguard expert interests and decision-making integrity.