A large-scale group decision-making model with no consensus threshold based on social network analysis

Recently, large-scale group decision-making in the social network context has become an attractive research hotspot in decision science. The focus of large-scale group decision-making is how to objectively and reasonably generate a collective opinion accepted by most decision makers. To this end, this study investigates a novel consensus model with no threshold based on social networks. In the model, inspired by the “similarity-attraction paradigm” in psychology, we first propose a novel adjustment method for the trust degree between decision makers. A clustering method that depends on the similarity-trust score between decision makers is presented to reduce the complexity of decision-making. In the consensus reaching process, we provide an approach to managing the overconfident or unconfident behaviour of decision makers. Moreover, the social network DeGroot model is used to adjust the preferences of decision makers in the feedback mechanism. In addition, we design an objective condition to terminate the consensus reaching process. Based on this, the no threshold consensus model is established, which makes the decision results more rational and scientific. Finally, the applicability of the proposed model is demonstrated by an illustrative example. Simulation experiments and comparative analysis illustrate the validity of the proposed model in promoting consensus reaching.