Group decision-making and consensus modeling have always been important research topics. With the widespread use of the Internet, group decisions can be made online, in which a large number of decision-makers participate. Most of the existing studies on large-scale group decision-making consider 20–50 decision-makers. Therefore, there is a need for a framework that focuses on situations where thousands of decision-makers exist. As dimension reduction is one of the five primary challenges in large-scale group decision-making, in this study, after reviewing the existing approaches, a new model is presented using a statistical approach along with complex network analysis techniques. The opinions are generalized first, and then the network of opinions is built. This new method reduces the dimensions of the problem by considering a hierarchy of opinions. Different scenarios were designed for the evaluation. The results show that the effect of this generalization on dimension reduction depends on the parameters of the problem. We have shown that in a group decision scenario with 3000 decision-makers and 6 alternatives, 99% of the data was reduced. As dimension reduction is the main focus of the current research, the effect of consistency on the diversity of opinions has also been investigated, and the results show that opinion consistency affects opinion generalization, which in turn affects dimension reduction. In addition, in the performed simulations, three types of functions were used to calculate similarity. The aim was to determine the best similarity function for the decision problems whose purpose was to rank the available alternatives. The results show that Euclidean similarity is a strict criterion compared with Cosine similarity.

群体决策和共识建模一直是重要的研究课题。随着互联网的广泛使用，群体决策可以在网上进行，大量决策者参与其中。现有的大规模群体决策研究大多考虑 20-50 名决策者。因此，需要一个专注于存在数千名决策者的情况的框架。由于降维是大规模群体决策中的五个主要挑战之一，因此在本研究中，在回顾现有方法后，使用统计方法和复杂的网络分析技术提出了一种新模型。首先将意见进行概括，然后建立意见网络。这种新方法通过考虑意见的层次结构来减少问题的维度。设计了不同的场景进行评估。结果表明，这种推广对降维的效果取决于问题的参数。我们已经证明，在有 3000 名决策者和 6 个备选方案的群体决策场景中，99% 的数据被减少。由于降维是当前研究的主要焦点，因此也研究了一致性对观点多样性的影响，结果表明观点一致性影响观点泛化，进而影响降维。此外，在进行的模拟中，使用了三种类型的函数来计算相似度。目的是确定决策问题的最佳相似函数，其目的是对可用替代方案进行排名。结果表明，与余弦相似度相比，欧氏相似度是一个严格的标准。