Managing Strategic Manipulation Behaviors Based on Historical Data of Preferences and Trust Relationships in Large-Scale Group Decision-Making
Managing Strategic Manipulation Behaviors Based on Historical Data of Preferences and Trust Relationships in Large-Scale Group Decision-Making
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DOI：
10.1109/tfuzz.2023.3328009
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发表时间：
2024-03
影响因子：
11.9
通讯作者：
Kai Xiong;Yucheng Dong;Quanbo Zha
中科院分区：
文献类型：
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作者：
Kai Xiong;Yucheng Dong;Quanbo Zha
在大规模群体决策（LSGDM）问题中，一些专家可能会采取战略操纵行为，这种行为可以通过信任和偏好价值观来体现。这些行为可能会影响或阻碍大规模的共识达成过程。本文从LSGDM中信任和偏好价值的历史数据出发，提出了一个新的共识框架来处理这些策略操纵行为。在提出的共识框架中，使用信任和偏好值的历史数据将专家分类为若干类。其次，分别对集群信任和偏好值的策略操纵行为进行了识别。然后，通过更新专家的权值，对具有两种策略操纵行为的聚类专家采取惩罚策略。仿真和比较研究表明，所提出的共识框架相对于传统框架在LSGDM中管理策略操纵行为的有效性。
In the large-scale group decision-making (LSGDM) problems, some experts may adopt strategic manipulation behaviors which can be reflected in trust and preference values. These behaviors can bias or hinder the large-scale consensus reaching process. This article proposes a novel consensus framework to deal with these strategic manipulation behaviors from the perspective of historical data of trust and preference values in LSGDM. In the proposed consensus framework, the experts are classified into several clusters using the historical data of trust and preference values. Next, the strategic manipulation behaviors of trust and preference values of clusters are identified, respectively. Then, we take the penalty strategy against experts in clusters with two kinds of strategic manipulation behaviors by updating experts’ weights. Simulation and comparison studies are employed to show the validity of the proposed consensus framework against traditional frameworks for managing strategic manipulation behaviors in LSGDM.