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Mass media and heterogeneous bounds of confidence in continuous opinion dynamics

Author

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  • Pineda, M.
  • Buendía, G.M.

Abstract

This work focuses on the effects of an external mass media on continuous opinion dynamics with heterogeneous bounds of confidence. We modified the original Deffuant et al. and Hegselmann and Krause models to incorporate both, an external mass media and a heterogeneous distribution of confidence levels. We analysed two cases, one where only two bounds of confidence are taken into account, and other where each individual of the system has her/his own characteristic level of confidence. We found that, in the absence of mass media, diversity of bounds of confidence can improve the capacity of the systems to reach consensus. We show that the persuasion capacity of the external message is optimal for intermediate levels of heterogeneity. Our simulations also show the existence, for certain parameter values, of a counter-intuitive effect in which the persuasion capacity of the mass media decreases if the mass media intensity is too large. We discuss similarities and differences between the two heterogeneous versions of these continuous opinion dynamic models under the influence of mass media.

Suggested Citation

  • Pineda, M. & Buendía, G.M., 2015. "Mass media and heterogeneous bounds of confidence in continuous opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 73-84.
  • Handle: RePEc:eee:phsmap:v:420:y:2015:i:c:p:73-84
    DOI: 10.1016/j.physa.2014.10.089
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    Citations

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    Cited by:

    1. Low, Nicholas Kah Yean & Melatos, Andrew, 2022. "Discerning media bias within a network of political allies and opponents: The idealized example of a biased coin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    2. Evangelos Ioannidis & Nikos Varsakelis & Ioannis Antoniou, 2020. "Promoters versus Adversaries of Change: Agent-Based Modeling of Organizational Conflict in Co-Evolving Networks," Mathematics, MDPI, vol. 8(12), pages 1-25, December.
    3. Ding, Haixin & Xie, Li, 2023. "Simulating rumor spreading and rebuttal strategy with rebuttal forgetting: An agent-based modeling approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    4. Pinto, Sebastián & Balenzuela, Pablo & Dorso, Claudio O., 2016. "Setting the agenda: Different strategies of a Mass Media in a model of cultural dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 378-390.
    5. Huang, Changwei & Dai, Qionglin & Han, Wenchen & Feng, Yuee & Cheng, Hongyan & Li, Haihong, 2018. "Effects of heterogeneous convergence rate on consensus in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 428-435.
    6. Fang, Siwei & Zhao, Nan & Chen, Nan & Xiong, Fei & Yi, Yunhui, 2019. "Analyzing and predicting network public opinion evolution based on group persuasion force of populism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 809-824.
    7. Di Benedetto, Andrea & Wieners, Claudia E. & Dijkstra, Henk A. & Stoof, Henk T.C., 2023. "Media preference increases polarization in an agent-based election model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    8. Li, Tingyu & Zhu, Hengmin, 2020. "Effect of the media on the opinion dynamics in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    9. Huang, Changwei & Hou, Yongzhao & Han, Wenchen, 2023. "Coevolution of consensus and cooperation in evolutionary Hegselmann–Krause dilemma with the cooperation cost," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    10. Glass, Catherine A. & Glass, David H., 2021. "Opinion dynamics of social learning with a conflicting source," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    11. Chen, Shuwei & Glass, David H. & McCartney, Mark, 2016. "Characteristics of successful opinion leaders in a bounded confidence model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 426-436.
    12. Xi Chen & Xiao Zhang & Yong Xie & Wei Li, 2017. "Opinion Dynamics of Social-Similarity-Based Hegselmann–Krause Model," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    13. Catherine A. Glass & David H. Glass, 2021. "Social Influence of Competing Groups and Leaders in Opinion Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 799-823, October.
    14. Han, Wenchen & Feng, Yuee & Qian, Xiaolan & Yang, Qihui & Huang, Changwei, 2020. "Clusters and the entropy in opinion dynamics on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    15. Piras, Simone & Righi, Simone & Setti, Marco & Koseoglu, Nazli & Grainger, Matthew & stewart, Gavin & Vittuari, Matteo, 2021. "From social interactions to private environmental behaviours: The case of consumer food waste," SocArXiv 7k4vy, Center for Open Science.
    16. Low, Nicholas Kah Yean & Melatos, Andrew, 2022. "Vacillating about media bias: Changing one’s mind intermittently within a network of political allies and opponents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    17. Takesue, Hirofumi, 2023. "Relative opinion similarity leads to the emergence of large clusters in opinion formation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).

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