چكيده به لاتين
Group decisions are made with the aim of taking into account the needs of all decision-makers and finding a balance between the demands of individuals. Group decision making in computer science is defined as a group of people coming together to discuss a decision until they find a solution that all members of the group agree on. One of the members of the group plays the role of mediator. In order to converge the views of the group, he is responsible for presenting the suggestion and managing the meeting. In order to make automatic decision-making process, group decision modeling and modeling to reach consensus in group decision making has always been an important research issue. In the initial modeling of group decision making, few decision makers participate in discussion all decision parameters were considered fixed. With the advent of new theories, these theories were applied to group decision making problems; For example, a variety of fuzzy set theories, complex network concepts such as trust networks, and theories of opinion evolution. In recent years, with the increasing use of social media and smartphones, it is possible for a large number of decision makers to participate in the virtual group decision making event. That is why the focus of group decision-making today is on large-scale decision-making groups with hundreds and thousands of decision makers. In addition, there are more diverse individuals in large-scale decision-making, and not all individuals have sufficient expertise or certainty. So the proposed methods should be able to take into account the characteristics of these new conditions. In this research, after examining the existing solutions and challenges, considering the limitations of the existing methods, a new model is presented using the science of fuzzy sets and complex networks. Neutrosophical fuzzy sets are used to express decision makers and two-layer network is used to model decision makers and the similarity between their views. This new method, by considering the hierarchy of opinions, reduces the dimensions of the problem, causing faster convergence of decision makers' opinions on large-scale group decision-making issues. The simulation experiments and comparative analysis are presented to illustrate the efficiency of the proposed method. In this study, the effect of consistency of opinions on the distribution of opinions has been investigated. In addition, three types of similarity functions have been studied and it has been shown that Euclidean similarity is a strict criterion for decision problems aimed at ranking existing alternatives.
