2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

具有类万有引力的有界置信观点动力学分析与应用

刘青松 习晓苗 柴利

刘青松, 习晓苗, 柴利. 具有类万有引力的有界置信观点动力学分析与应用. 自动化学报, 2023, 49(9): 1967−1975 doi: 10.16383/j.aas.c211134
引用本文: 刘青松, 习晓苗, 柴利. 具有类万有引力的有界置信观点动力学分析与应用. 自动化学报, 2023, 49(9): 1967−1975 doi: 10.16383/j.aas.c211134
Liu Qing-Song, Xi Xiao-Miao, Chai Li. Analysis and application of bounded confidence opinion dynamics with universal gravitation-like. Acta Automatica Sinica, 2023, 49(9): 1967−1975 doi: 10.16383/j.aas.c211134
Citation: Liu Qing-Song, Xi Xiao-Miao, Chai Li. Analysis and application of bounded confidence opinion dynamics with universal gravitation-like. Acta Automatica Sinica, 2023, 49(9): 1967−1975 doi: 10.16383/j.aas.c211134

具有类万有引力的有界置信观点动力学分析与应用

doi: 10.16383/j.aas.c211134
基金项目: 国家自然科学基金(61903282, 62173259), 中国博士后科学基金(2020T130488)资助
详细信息
    作者简介:

    刘青松:武汉科技大学信息科学与工程学院副教授. 2019年获得哈尔滨工业大学博士学位. 主要研究方向为社会网络, 观点动力学分析, 时滞系统和多智能体系统. E-mail: qingsongliu@wust.edu.cn

    习晓苗:武汉科技大学信息科学与工程学院硕士研究生. 2020年获得湖南科技大学学士学位. 主要研究方向为社会网络, 观点动力学分析. E-mail: xixiaomiaoivy@163.com

    柴利:武汉科技大学信息科学与工程学院教授. 2002年获得香港科技大学博士学位. 主要研究方向为分布式优化, 滤波器组框架, 图信号处理和网络化控制系统. 本文通信作者. E-mail: chaili@wust.edu.cn

Analysis and Application of Bounded Confidence Opinion Dynamics With Universal Gravitation-like

Funds: Supported by National Natural Science Foundation of China (61903282, 62173259) and China Postdoctoral Science Foundation (2020T130488)
More Information
    Author Bio:

    LIU Qing-Song Associate professor at the School of Information Science and Engineering, Wuhan University of Science and Technology. He received his Ph.D. degree from Harbin Institute of Technology in 2019. His research interest covers social networks, opinion dynamics analysis, time-delay systems, and multi-agent systems

    XI Xiao-Miao Master student at the School of Information Science and Engineering, Wuhan University of Science and Technology. She received her bachelor degree from Hunan University of Science and Technology in 2020. Her research interest covers social networks and opinion dynamics analysis

    CHAI Li Professor at the School of Information Science and Engineering, Wuhan University of Science and Technology. He received his Ph.D. degree from Hong Kong University of Science and Technology in 2002. His research interest covers distributed optimization, filter bank frames, graph signal processing, and networked control systems. Corresponding author of this paper

  • 摘要: 在社会网络中, Hegselmann-Krause模型描述了置信阈值内不同邻居对个体的观点影响权重都相同且邻居对个体的吸引力与它们的观点差值成正比, 这是不切实际的. 为了克服经典Hegselmann-Krause模型的不足, 提出具有类万有引力的有界置信观点动力学模型, 描述个体观点的更新依赖于观点之间的差值和邻居的权威性, 且不同邻居对个体的观点影响权重不同. 根据置信矩阵的性质证明观点的收敛性, 并分析具有衰减置信阈值的观点动力学行为, 给出观点收敛速率的显式解. 最后, 利用提出的观点动力学模型, 研究社会心理学中的“权威效应”和“非零和效应”. 仿真结果表明, 邻居的权威性有利于观点达成一致.
  • 图  1  $d_{ij}(k)$关于$\vert N_j\vert$和$x_j(k)-x_i(k)$的函数图

    Fig.  1  The trajectories of $d_{ij}(k)$ with respect to $\vert N_j\vert$ and $x_j(k)-x_i(k)$

    图  2  个体$j$对个体$i$观点的影响

    Fig.  2  The influence of individual $j$ on the opinion of individual $i$

    图  3  网络拓扑结构 (个体4为权威个体)

    Fig.  3  Network structure (individual 4 is the authoritative individual)

    图  4  权威效应 (个体4为权威个体)

    Fig.  4  Authority effect (individual 4 is the authoritative individual)

    图  5  网络拓扑结构 (个体5为权威个体)

    Fig.  5  Network structure (individual 5 is the authoritative individual)

    图  6  权威效应 (个体5为权威个体)

    Fig.  6  Authority effect (individual 5 is the authoritative individual)

    图  7  非零和效应

    Fig.  7  Sum non-zero effect

    图  8  初值为均匀分布时的观点演化

    Fig.  8  Opinion evolution when the initial value is uniformly distributed

    图  9  初值为正态分布时的观点演化

    Fig.  9  Opinion evolution when the initial value is normally distributed

    图  10  模型(10)观点演化

    Fig.  10  Opinion evolution of model (10)

  • [1] Zhou B, Lin Z. Consensus of high-order multi-agent systems with large input and communication delays. Automatica, 2014, 50: 452-464 doi: 10.1016/j.automatica.2013.12.006
    [2] 陈世明, 邵赛, 姜根兰. 基于事件触发二阶多智能体系统的固定时间比例一致性. 自动化学报, 2022, 48(1): 261-270

    Chen Shi-Ming, Shao Sai, Jiang Gen-Lan. Distributed event-triggered fixed-time scaled consensus control for second-order multi-agent systems. Acta Automatica Sinica, 2022, 48(1): 261-270
    [3] Zhou B. Consensus of delayed multi-agent systems by reduced-order observer-based truncated predictor feedback protocols. IET Control Theory & Applications, 2014, 8(16): 1741-1751
    [4] 王龙, 田野, 杜金铭. 社会网络上的观念动力学. 中国科学: 信息科学, 2018, 48(1): 3-23 doi: 10.1360/N112017-00096

    Wang Long, Tian Ye, Du Jin-Ming. Opinion dynamics in social networks. Scientia Sinica: Informationis, 2018, 48(1): 3-23 doi: 10.1360/N112017-00096
    [5] Ghaderi J, Srikant R. Opinion dynamics in social networks with stubborn agents: Equilibrium and convergence rate. Automatica, 2014, 50(12): 3209-3215 doi: 10.1016/j.automatica.2014.10.034
    [6] Xia W, Ye M, Liu J, Cao M, Sun X M. Analysis of a nonlinear opinion dynamics model with biased assimilation. Automatica, 2020, 120: 109113 doi: 10.1016/j.automatica.2020.109113
    [7] Liu C, Wu X, Niu R, Aziz-Alaoui M A, Lü J. Opinion diffusion in two-layer interconnected networks. IEEE Transactions on Circuits and Systems I: Regular Papers, 2021, 68(9): 3772-3783 doi: 10.1109/TCSI.2021.3093537
    [8] Ye M, Qin Y, Govaert A, Anderson B D, Cao M. An influence network model to study discrepancies in expressed and private opinions. Automatica, 2019, 107: 371-381 doi: 10.1016/j.automatica.2019.05.059
    [9] 刘青松, 李明鹏, 柴利. 具有遗忘群体的社会网络多维观点动力学分析与应用. 自动化学报, 2022, DOI: 10.16383/j.aas.c210091

    Liu Qing-Song, Li Ming-Peng, Chai Li, Analysis and application of multidimensional opinion dynamics on social networks with oblivion individuals. Acta Automatica Sinica, 2022, DOI: 10.16383/j.aas.c210091
    [10] Hou J, Li W, Jiang M. Opinion dynamics in modified expressed and private model with bounded confidence. Physica A: Statistical Mechanics and its Applications, 2021, 574: 125968 doi: 10.1016/j.physa.2021.125968
    [11] 郑维, 张志明, 刘和鑫, 张明泉, 孙富春. 基于线性变换的领导-跟随多智能体系统动态反馈均方一致性控制. 自动化学报, 2021, DOI: 10.16383/j.aas.c200850

    Zheng Wei, Zhang Zhi-Ming, Liu He-Xin, Zhang Ming-Quan, Sun Fu-Chun. Dynamic feedback mean square consensus control based on linear transformation for leader-follower multi-agent systems. Acta Automatica Sinica, 2021, DOI: 10.16383/j.aas.c200850
    [12] Yi J W, Chai L, Zhang J. Average consensus by graph filtering: new approach, explicit convergence rate, and optimal design. IEEE Transactions on Automatic Control, 2020, 65(1): 191-206 doi: 10.1109/TAC.2019.2907410
    [13] Liu Q, Zhou B. Consensus of discrete-time multiagent systems with state, input, and communication delays. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(11): 4425-4437 doi: 10.1109/TSMC.2018.2852944
    [14] Liu Q. Pseudo-predictor feedback control for multiagent systems with both state and input delays. IEEE/CAA Journal of Automatica Sinica, 2021, 8(11): 1827-1836 doi: 10.1109/JAS.2021.1004180
    [15] French Jr J R. A formal theory of social power. Psychological Review, 1956, 63(3): 181-194 doi: 10.1037/h0046123
    [16] DeGroot M H. Reaching a consensus. Journal of the American Statistical Association, 1974, 69(345): 118-121 doi: 10.1080/01621459.1974.10480137
    [17] Friedkin N, Johnsen E. Social influence networks and opinion change. Advances Group Processes, 1999, 16: 1-29
    [18] Parsegov S E, Proskurnikov A V, Tempo R, Friedkin N E. Novel multidimensional models of opinion dynamics in social networks. IEEE Transactions on Automatic Control, 2017, 62(5): 2270-2285 doi: 10.1109/TAC.2016.2613905
    [19] Tian Y, Wang L. Opinion dynamics in social networks with stubborn agents: An issue-based perspective. Automatica, 2018, 96: 213-223 doi: 10.1016/j.automatica.2018.06.041
    [20] Deffuant G, Neau D, Amblard F, Weisbuch G. Mixing beliefs among interacting agents. Advances in Complex Systems, 2000, 3: 87-98 doi: 10.1142/S0219525900000078
    [21] Zhang J, Hong Y. Opinion evolution analysis for short-range and long-range Deffuant–Weisbuch models. Physica A: Statistical Mechanics and its Applications, 2013, 392(21): 5289-5297 doi: 10.1016/j.physa.2013.07.014
    [22] Dong Y, Ding Z, Martínez L, Herrera F. Managing consensus based on leadership in opinion dynamics. Information Sciences, 2017, 397: 187-205
    [23] Mei W, Friedkin N E, Lewis K, Bullo F. Dynamic models of appraisal networks explaining collective learning. IEEE Transactions on Automatic Control, 2018, 63(9): 2898-2912 doi: 10.1109/TAC.2017.2775963
    [24] Hegselmann R, Krause U. Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 2002, 5(3): 1-33
    [25] Bullo F, Cortes J, Martinez S. Distributed Control of Robotic Networks. Princeton: Princeton University Press, 2009.
    [26] Cody W F. Authoritative effect of FDA regulations. The Business Lawyer, 1969, 24: 479-491
    [27] Chen Z, Lan H. Dynamics of public opinion: Diverse media and audiences’ choices. Journal of Artificial Societies and Social Simulation, 2021, 24(2): 1-21 doi: 10.18564/jasss.4518
    [28] Canuto C, Fagnani F, Tilli P. An Eulerian approach to the analysis of Krause's consensus models. SIAM Journal on Control and Optimization, 2012, 50(1): 243-265 doi: 10.1137/100793177
    [29] Su W, Chen G, Hong Y. Noise leads to quasi-consensus of Hegselmann–Krause opinion dynamics. Automatica, 2017, 85: 448-454 doi: 10.1016/j.automatica.2017.08.008
    [30] Yang Y, Dimarogonas D V, Hu X. Opinion consensus of modified Hegselmann–Krause models. Automatica, 2014, 50(2): 622-627 doi: 10.1016/j.automatica.2013.11.031
    [31] Haskovec J. A simple proof of asymptotic consensus in the Hegselmann-Krause and Cucker-Smale models with normalization and delay. SIAM Journal on Applied Dynamical Systems, 2021, 20(1): 130-148 doi: 10.1137/20M1341350
    [32] Vasca F, Bernardo C, Iervolino R. Practical consensus in bounded confidence opinion dynamics. Automatica, 2021, 129: 109683 doi: 10.1016/j.automatica.2021.109683
    [33] Gerrig R J. Psychology and Life (20th Edition). New York: Pearson, 2013.
    [34] Mei W, Bullo F, Chen G, Hendrickx J, Dörfler F. Rethinking the micro-foundation of opinion dynamics: Rich consequences of the weighted-median mechanism [Online], available: https://arxiv.org//abs/1909.06474, January 26, 2022
    [35] Swingle P G, Santi A. Communication in non-zero-sum games. Journal of Personality and Social Psychology, 1972, 23 (1), 54-63 doi: 10.1037/h0032878
    [36] Lorenz J. A stabilization theorem for dynamics of continuous opinions. Physica A: Statistical Mechanics and its Applications, 2005, 355(1): 217-223 doi: 10.1016/j.physa.2005.02.086
    [37] Morarescu I C, Girard A. Opinion dynamics with decaying confidence: Application to community detection in graphs. IEEE Transactions on Automatic Control, 2011, 56(8): 1862-1873 doi: 10.1109/TAC.2010.2095315
  • 加载中
图(10)
计量
  • 文章访问数:  582
  • HTML全文浏览量:  160
  • PDF下载量:  112
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-11-30
  • 录用日期:  2022-04-28
  • 网络出版日期:  2022-05-30
  • 刊出日期:  2023-09-26

目录

    /

    返回文章
    返回