偏爱
有界函数
群体决策
计算机科学
比例(比率)
机制(生物学)
现存分类群
人工智能
机器学习
数学
统计
心理学
社会心理学
认识论
物理
数学分析
哲学
生物
进化生物学
量子力学
作者
Quanbo Zha,Haiming Liang,Gang Kou,Yucheng Dong,Shui Yu
出处
期刊:IEEE Transactions on Computational Social Systems
[Institute of Electrical and Electronics Engineers]
日期:2019-10-01
卷期号:6 (5): 994-1006
被引量:69
标识
DOI:10.1109/tcss.2019.2938258
摘要
Different feedback mechanisms have been developed in large-scale group decision-making (GDM) to provide the decision-makers with advices for preference adjustment with the aim of improving the group consensus level. However, the willingness of the decision-makers to accept these advices is rarely considered in the extant feedback mechanisms. In the field of opinion dynamics, this issue is studied by the bounded confidence model, which shows that the decision-makers only consider the preferences that differ from their own preferences not more than a certain confidence level. Following this idea, this article proposes a large-scale consensus model with a bounded confidence-based feedback mechanism to promote the consensus level among decision-makers with bounded confidences. Specifically, this feedback mechanism classifies the decision-makers into different clusters and provides the corresponding clusters with more acceptable advices based on a bounded confidence-based optimization approach. Finally, through the numerical example and the simulation analysis, the use of the model is introduced, and the effectiveness of the model is justified.
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