A bargaining game based feedback mechanism to support consensus in dynamic social network group decision making

Abstract

A bargaining game is used to develop feedback mechanism for dynamic social networks group decision making (SN-GDM). The dynamic trust relationships between experts are updated by the change of their consensus state after each round of interaction. Then, a maximum entropy model based on individual interactive relationship and fairness is established to determine the comprehensive weight of each expert, which considers: (1) the individual weight by influence of expert; (2) the interaction weight by social relationships of experts. Hence, 2-tuple linguistic collective evaluation matrix of the 2-additive Choquet integral under Möbius transform is put forward. Further, the equilibrium solution of two experts in the bargaining game is established, and then this equilibrium recommendation will be accepted by both experts. Consequently, a bargaining game based feedback mechanism driven by trust relationship is proposed to reflect the interaction behaviors between the inconsistent expert and her/his most trusted consistent one, and therefore the recommendation advices are generated for them to promote consensus in SN-GDM. Finally, a sustainable supplier selection example demonstrates the effectiveness of the proposed approach.