An evolution model of risk preference influenced by extremists in large group emergency consensus process

2020 ◽  
Vol 39 (5) ◽  
pp. 7733-7746
Author(s):  
Jing Cao ◽  
Xuan-hua Xu ◽  
Fei Dai ◽  
Bin Pan

This study uses opinion dynamics to explore the influence of extremists in the consensus process of large group decision-making. When moderates are exposed to extremists, their risk preference will be affected. By using the opinion leader theory for reference, the influence model of extremists is constructed. To better study the influence of extremists, the similarity of risk preference between extremists and moderates is modeled to measure their similarity degree. From this model, for every moderate, the extremists are divided into two groups: homogeneous group and heterogeneous group. Finally, the risk preference evolution model is structured by considering that moderates change their risk preference dynamically according to their initial preference, their attitude towards the homogeneous groups, and the heterogeneous groups. Finding from data analysis shows that moderates with high acceptance toward the influence of extremists are more likely to reach group consensus. It is also found that the preference trend of moderates with a certain degree of acceptance toward heterogeneous groups fluctuates with a ‘W’ shape. This study bridges the gap between opinion dynamics and group decision making. Meanwhile, the model inspires new explanations and new perspectives for the group consensus process.

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1180
Author(s):  
Lei Wang ◽  
Huifeng Xue

Existing decision-making methods are mostly a simple aggregation of expert decision information when solving large group decision-making problems. In these methods, priority should be given to expert weight information; however, it is difficult to avoid the loss of expert decision information in the decision-making process. Therefore, a new idea to solve the problem of large group decision-making by combining the expert group clustering algorithm and the group consensus model is proposed in this paper in order to avoid the disadvantages of subjectively assigning expert weights. First, expert groups are classified by the clustering algorithm of breadth-first search neighbors. Next, the decision information of the experts in the class is corrected adaptively using the group consensus model; then, expert decision information in the class is integrated using probabilistic linguistic translation methods. This method not only avoids the shortcomings of artificially given expert weights, but also reduces the loss of expert decision information. Finally, the method comprehensively considers the scale of the expert class and the difference between the classes to determine the weight of the expert class, and then it weights and integrates the consensus information of all expert classes to obtain the final decision result. This article verifies the effectiveness of the proposed method through a case analysis of urban water resource sustainability evaluation, and provides a scientific evaluation method for the sustainable development level of urban water resources.


Author(s):  
Gui-ju Zhu ◽  
Chen-guang Cai ◽  
Bin Pan ◽  
Pei Wang

AbstractFocusing on the characteristics of public participation and large group decision making of major livelihood projects, this paper proposes a multi-agent linguistic-style large group decision-making method with the consideration of public expectations. Firstly, based on the discrimination degree of evaluating information, the comprehensive weight of each attribute is calculated with the principle of maximum entropy. Secondly, the expert preference information for different alternatives is clustered and several aggregations are formed. Thirdly, the preference conflict level of experts' group for each alternative is calculated, and a conflict-oriented experts' aggregation weight optimization model is constructed to ensure the effectiveness of conflict resolution. Fourthly, the public group's satisfaction is determined with the expectation distribution of public’s and the expert group's preference, so as to obtain the sorting result of the decision alternatives. Finally, the effectiveness and applicability of the proposed method are verified by method comparison.


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