Discrete-time signed bounded confidence model for opinion dynamics

被引：17

作者：

He, Guang ^{[1
,2
,4
]}

Liu, Jing ^{[3
]}

Hu, Huimin ^{[1
,2
]}

Fang, Jian-An ^{[3
]}

机构：

[1] Anhui Polytech Univ, Minist Educ, Key Lab Adv Percept & Intelligent Control High En, Wuhu 241000, Anhui, Peoples R China

[2] Anhui Polytech Univ, Sch Math & Phys, Wuhu 241000, Anhui, Peoples R China

[3] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China

[4] Potsdam Inst Climate Impact Res, Telegraphenber, D-14415 Potsdam, Germany

来源：

NEUROCOMPUTING | 2021年 / 425卷

基金：

中国国家自然科学基金;

关键词：

Opinion dynamics; Bounded confidence; Consensus; Cluster; STABILITY ANALYSIS; CONSENSUS; SYNCHRONIZATION; CONVERGENCE; NETWORKS; SYSTEMS;

D O I：

10.1016/j.neucom.2019.12.061

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the classic discrete-time bounded confidence (BC) model is modified by using the sign function such that signs of opinions never change during the opinion evolution. Considering the antag-onistic and indifference behaviors between individuals, we propose two discrete-time signed BC models. Then, by comparing our proposed signed BC models with the classic BC model, we investigate dynam-ics characteristic of them in detail. We find that opinion evolution of our proposed signed BC models is more complicated than the classic BC model, but they retain clustering behavior of the classic BC model. Furthermore, several examples are given to test and verify the obtained results. (c) 2019 Elsevier B.V. All rights reserved.

引用

页码：53 / 61

页数：9

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