An Adaptive Consensus Model in Large-Scale Group Decision Making with Noncooperative and Compromising Behaviors

In recent years, different feedback mechanisms have been reported in many consensus models to improve decision levels. However, the improvement of decision level often leads to the reduction of decision efficiency, which has been rarely considered in existing consensus models. This paper proposes an adaptive consensus model consisting of automatic strategy and interactive strategy, which are implemented in different consensus stages to balance decision efficiency and decision level. In addition, the behavior diversity of decision makers (DMs) is often unavoidable, such as noncooperative behavior, which brings greater complexity to the consensus reaching. Meanwhile, cooperative behavior is usually accompanied by compromise behavior. Considering that the compromise behavior of DMs will change subgroup structure, dynamic cluster analysis is performed in the consensus reaching process. On the premise of dynamic clustering, the traditional weight penalty mechanism will fail to manage the noncooperative behaviors of subgroups. To this end, this paper proposes a new penalty mechanism. The proposed adaptive consensus model is applied to the selection of cities for establishing the freight hub. Finally, some numerical simulations and comparative analyses are presented to verify the effectiveness of the proposed model.