scholarly journals Group Decision-Making Method Based on Expert Classification Consensus Information Integration

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1180
Author(s):  
Lei Wang ◽  
Huifeng Xue
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Clustering Algorithm ◽  
Group Decision ◽  
Final Decision ◽  
Urban Water ◽  
Consensus Model ◽  
Group Consensus ◽  
Large Group ◽  
Consensus Information

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.

scholarly journals An Influence Network-Based Consensus Model for Large-Scale Group Decision Making with Linguistic Information

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Shengbao Yao ◽  
Miao Gu
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Large Scale ◽  
Group Decision ◽  
Decision Makers ◽  
Consensus Model ◽  
Group Consensus ◽  
Preference Information ◽  
Adjustment Strategy ◽  
Consensus Reaching

AbstractThe vast majority of the existing social network-based group decision-making models require extra information such as trust/distrust, influence and so on. However, in practical decision-making process, it is difficult to get additional information apart from opinions of decision makers. For large-scale group decision making (LSGDM) problem in which decision makers articulate their preferences in the form of comparative linguistic expressions, this paper proposes a consensus model based on an influence network which is inferred directly from preference information. First, a modified agglomerative hierarchical clustering algorithm is developed to detect subgroups in LSGDM problem with flexible linguistic information. Meanwhile, a measure method of group consensus level is proposed and the optimal clustering level can be determined. Second, according to the preference information of group members, influence network is constructed by determining intra-cluster and inter-cluster influence relationships. Third, a two-stage feedback mechanism guided by influence network is established for the consensus reaching process, which adopts cluster adjustment strategy and individual adjustment strategy depending on the different levels of group consensus. The proposed mechanism can not only effectively improve the efficiency of consensus reaching of LSGDM, but also take individual preference adjustment into account. Finally, the feasibility and effectiveness of the proposed method are verified by the case of intelligent environmental protection project location decision.

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
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Risk Preference ◽  
Opinion Dynamics ◽  
Evolution Model ◽  
Group Consensus ◽  
Consensus Process ◽  
Large Group ◽  
Heterogeneous Groups

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.

scholarly journals A Reliability-Based Consensus Model for Multiattribute Group Decision-Making with Analytically Evidential Reasoning Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Yuan-Wei Du ◽  
Ning Yang ◽  
Wen Zhou ◽  
Chang-Xing Li
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Evidential Reasoning ◽  
Final Decision ◽  
Consensus Model ◽  
Evidential Reasoning Approach ◽  
Basic Probability ◽  
Numerical Case Study

Expert reliability is the ability to make unmistakable evaluations on attributes for the performance of an alternative in multiattribute group decision making (MAGDM). It has a significant effect on the group consensus calculation and group decision-making; unfortunately the reliability has not been considered in the consensus-reaching model yet. This study focuses on providing a reliability-based consensus model for MAGDM with analytically evidential reasoning (analytical ER for short) approach. The basic probability assignment (BPA) function which can be discounted by expert reliability is introduced to describe the performance judgments of each expert, by combining which of the group judgments could be determined with analytical ER rule. Then the consensus degrees of three levels (attribute level, alternative level, and expert level) are defined by Jousselme distance to identify the experts who should revise their judgments and point out revised suggestions, based on which a decision-making method within interaction is proposed to determine the effective BPA functions of all experts and make final decision-making. Finally, a numerical case study is carried out to illustrate the effectiveness of the method.

Optimization-Based Consensus Model to Solve Multi-Criteria Large Group Decision Making Problems

2020 ◽  
Vol 22 (02) ◽  
pp. 2040010
Author(s):  
Anjali Singh ◽  
Anjana Gupta
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Optimization Theory ◽  
Feedback Mechanism ◽  
Management Decision ◽  
Consensus Model ◽  
Suggested Approach ◽  
Large Group ◽  
Time Problem

In this contribution, a consensus model is proposed to acquire a unified and converging solution of multi-criteria large group decision making problems. Unlike the iterative process and feedback mechanism based models, the suggested approach features the optimization theory to establish the consensus in one go only among the efficient experts. The time salvation characteristic of the model makes it expedient for the emergency planning and management decision problems. The algorithm is validated using the hurricane evacuation notification time problem of United States.

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