Analyzing the effects of confidence thresholds on opinion clustering in homogeneous Hegselmann-Krause models

被引：2

作者：

Srivastava, Trisha ^{[1
]}

Bernardo, Carmela ^{[2
]}

Altafini, Claudio ^{[2
]}

Vasca, Francesco ^{[1
]}

机构：

[1] Univ Sannio, Dept Engn, I-82100 Benevento, Italy

[2] Linkoping Univ, Dept Elect Engn, SE-58183 Linkoping, Sweden

来源：

2023 31ST MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION, MED | 2023年

关键词：

BOUNDED CONFIDENCE; DYNAMICS; CONVERGENCE; NETWORKS;

D O I：

10.1109/MED59994.2023.10185838

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Hegselmann-Krause (HK) models exhibit complex behaviors which are not easily tractable through mathematical analysis. In this paper, a characterization of the steady-state behaviors of homogeneous HK models and sensitivity to confidence thresholds is discussed by commenting on existing and new numerical results. The typical decreasing of number of clusters and convergence time by increasing the confidence thresholds are discussed and motivations for the behavior of some counterexamples are provided. A tighter upper bound for the dependence of the number of clusters with respect to the confidence thresholds is proposed. Differences and analogies between the opinions' evolution for symmetric and asymmetric HK models are commented.

引用

页码：587 / 592

页数：6

共 31 条

[1] Finite-time convergence of opinion dynamics in homogeneous asymmetric bounded confidence models [J].