Method of Consensus Evolution for Wisdom of Crowd Based on Social Network

In the traditional methods for wisdom of crowd, the measure and improvement of crowd consensus is very important. In the process of crowd consensus measure and improvement, it is necessary to modify the decision-maker information that does not meet the consensus threshold. And in the traditional methods for wisdom of crowd, decision-makers do not exchange information and communication. In order to avoid the above problems, this paper proposes the method for wisdom of crowd based on the evolution of decision makers in social network. The method is mainly divided into three parts: Firstly, the decision-makers compare the alternatives in pairs according to their own knowledge and experience to get the evaluation results. Secondly, the bisecting K-means clustering algorithm is used to group the alternatives according to the evaluation results of the decision makers, and the French-Harary-DeGroot model is used to simulate the opinions of the decision makers in the crowd. After negotiation, the consensus is reached. Finally, the opinions of each group are gathered and the best alternative is selected. At the end of the paper, the experiment is used to prove the effectiveness of the proposed method.