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Compromise and synchronization in opinion dynamics

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  • A. Pluchino
  • V. Latora
  • A. Rapisarda

Abstract

We discuss two models of opinion dynamics. We first present a brief review of the Hegselmann and Krause (HK) compromise model in two dimensions, showing that it is possible to simulate the dynamics in the limit of an infinite number of agents by solving numerically a rate equation for a continuum distribution of opinions. Then, we discuss the Opinion Changing Rate (OCR) model, which allows to study under which conditions a group of agents with a different natural tendency (rate) to change opinion can find the agreement. In the context of the this model, consensus is viewed as a synchronization process. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2006

Suggested Citation

  • A. Pluchino & V. Latora & A. Rapisarda, 2006. "Compromise and synchronization in opinion dynamics," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 169-176, March.
    • Handle: RePEc:spr:eurphb:v:50:y:2006:i:1:p:169-176
    DOI: 10.1140/epjb/e2006-00131-0
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    Citations

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    Cited by:

    1. Nguyen, Tung T. & Budzinski, Roberto C. & Pasini, Federico W. & Delabays, Robin & Mináč, Ján & Muller, Lyle E., 2023. "Broadcasting solutions on networked systems of phase oscillators," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    2. Antonio Parravano & Ascensión Andina-Díaz & Miguel A Meléndez-Jiménez, 2016. "Bounded Confidence under Preferential Flip: A Coupled Dynamics of Structural Balance and Opinions," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-23, October.
    3. Fabio Bagagiolo & Dario Bauso & Raffaele Pesenti, 2016. "Mean-Field Game Modeling the Bandwagon Effect with Activation Costs," Dynamic Games and Applications, Springer, vol. 6(4), pages 456-476, December.
    4. Dario Bauso & Raffaele Pesenti & Marco Tolotti, 2016. "Opinion Dynamics and Stubbornness Via Multi-Population Mean-Field Games," Journal of Optimization Theory and Applications, Springer, vol. 170(1), pages 266-293, July.
    5. Biondo, A.E. & Brosio, G. & Pluchino, A. & Zanola, R., 2022. "Authoritarianism vs. democracy: Simulating responses to disease outbreaks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    6. Yoon, Jisung & Park, Jinseo & Yun, Jinhyuk & Jung, Woo-Sung, 2023. "Quantifying knowledge synchronization with the network-driven approach," Journal of Informetrics, Elsevier, vol. 17(4).
    7. G Jordan Maclay & Moody Ahmad, 2021. "An agent based force vector model of social influence that predicts strong polarization in a connected world," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-42, November.
    8. 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.
    9. Biswas, Dhrubajyoti & Gupta, Sayan, 2024. "Symmetry-breaking higher-order interactions in coupled phase oscillators," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

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