Managing non-cooperative behaviors in consensus-based multiple attribute group decision making: An approach based on social network analysis

In consensus-based multiple attribute group decision making (MAGDM) problems, it is frequent that some experts exhibit non-cooperative behaviors owing to the different areas to which they may belong and the different (sometimes conflicting) interests they might present. This may adversely affect the overall efficiency of the consensus reaching process, especially when some uncooperative behaviors by experts arise. To this end, this paper develops a novel consensus framework based on Social Network Analysis (SNA) to deal with non-cooperative behaviors. In the proposed SNA-based consensus framework, a trust propagation and aggregation mechanism to yield experts’ weights from the social trust network is presented, and the obtained weights of experts are then integrated into the consensus-based MAGDM framework. Meanwhile, a non-cooperative behavior analysis module is designed to analyze the behaviors of experts. Based on the results of such analysis during the consensus process, each expert can express and modify the trust values pertaining other experts in the social trust network. As a result, both the social trust network and the weights of experts derived from it are dynamically updated in parallel. A simulation and comparison study is presented to demonstrate the efficiency of the SNA-based consensus framework for coping with non-cooperative behaviors.