IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v168y2023ics096007792300036x.html
   My bibliography  Save this article

Mesoscopic analytical approach in a three state opinion model with continuous internal variable

Author

Listed:
  • Pedraza, Lucía
  • Pinasco, Juan Pablo
  • Semeshenko, Viktoriya
  • Balenzuela, Pablo

Abstract

Analytical approaches in models of opinion formation have been extensively studied either for an opinion represented as a discrete or a continuous variable. In this paper, we analyze a model which combines both approaches. The state of an agent is represented with an internal continuous variable (the leaning or propensity), that leads to a discrete public opinion: pro, against or neutral. This model can be described by a set of master equations which are a nonlinear coupled system of first order differential equations of hyperbolic type including non-local terms and non-local boundary conditions, which cannot be solved analytically. We developed an approximation to tackle this difficulty by deriving a set of master equations for the dynamics of the average leaning of agents with the same opinion, under the hypothesis of a time scale separation in the dynamics of the variables. We show that this simplified model accurately predicts the expected transition between a neutral consensus and a bi-polarized state, and also gives an excellent approximation for the dynamics of the average leaning of agents with the same opinion, even when the time separation scale hypothesis is not completely fulfilled.

Suggested Citation

  • Pedraza, Lucía & Pinasco, Juan Pablo & Semeshenko, Viktoriya & Balenzuela, Pablo, 2023. "Mesoscopic analytical approach in a three state opinion model with continuous internal variable," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
  • Handle: RePEc:eee:chsofr:v:168:y:2023:i:c:s096007792300036x
    DOI: 10.1016/j.chaos.2023.113135
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096007792300036X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2023.113135?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jan Lorenz, 2007. "Continuous Opinion Dynamics Under Bounded Confidence: A Survey," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(12), pages 1819-1838.
    2. Ivan V. Kozitsin, 2022. "Formal models of opinion formation and their application to real data: evidence from online social networks," The Journal of Mathematical Sociology, Taylor & Francis Journals, vol. 46(2), pages 120-147, April.
    3. Katarzyna Sznajd-Weron & Józef Sznajd, 2000. "Opinion Evolution In Closed Community," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1157-1165.
    4. Pablo Balenzuela & Juan Pablo Pinasco & Viktoriya Semeshenko, 2015. "The Undecided Have the Key: Interaction-Driven Opinion Dynamics in a Three State Model," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-21, October.
    5. M. S. de la Lama & I. G. Szendro & J. R. Iglesias & H. S. Wio, 2006. "Van Kampen's expansion approach in an opinion formation model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 51(3), pages 435-442, June.
    6. Guillaume Deffuant, 2006. "Comparing Extremism Propagation Patterns in Continuous Opinion Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(3), pages 1-8.
    7. Pareschi, Lorenzo & Toscani, Giuseppe, 2013. "Interacting Multiagent Systems: Kinetic equations and Monte Carlo methods," OUP Catalogue, Oxford University Press, number 9780199655465.
    8. Pinasco, Juan Pablo & Semeshenko, Viktoriya & Balenzuela, Pablo, 2017. "Modeling opinion dynamics: Theoretical analysis and continuous approximation," Chaos, Solitons & Fractals, Elsevier, vol. 98(C), pages 210-215.
    9. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    10. Guillaume Deffuant & David Neau & Frederic Amblard & Gérard Weisbuch, 2000. "Mixing beliefs among interacting agents," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 3(01n04), pages 87-98.
    11. Serge Galam, 2008. "Sociophysics: A Review Of Galam Models," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 409-440.
    12. Wander Jager & Frédéric Amblard, 2005. "Uniformity, Bipolarization and Pluriformity Captured as Generic Stylized Behavior with an Agent-Based Simulation Model of Attitude Change," Computational and Mathematical Organization Theory, Springer, vol. 10(4), pages 295-303, January.
    13. Andreas Flache & Michael Mäs & Thomas Feliciani & Edmund Chattoe-Brown & Guillaume Deffuant & Sylvie Huet & Jan Lorenz, 2017. "Models of Social Influence: Towards the Next Frontiers," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(4), pages 1-2.
    14. Amblard, Frédéric & Deffuant, Guillaume, 2004. "The role of network topology on extremism propagation with the relative agreement opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 343(C), pages 725-738.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kurmyshev, Evguenii & Juárez, Héctor A. & González-Silva, Ricardo A., 2011. "Dynamics of bounded confidence opinion in heterogeneous social networks: Concord against partial antagonism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2945-2955.
    2. Shane T. Mueller & Yin-Yin Sarah Tan, 2018. "Cognitive perspectives on opinion dynamics: the role of knowledge in consensus formation, opinion divergence, and group polarization," Journal of Computational Social Science, Springer, vol. 1(1), pages 15-48, January.
    3. Pedraza, Lucía & Pinasco, Juan Pablo & Saintier, Nicolas & Balenzuela, Pablo, 2021. "An analytical formulation for multidimensional continuous opinion models," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    4. Maciel, Marcelo V. & Martins, André C.R., 2020. "Ideologically motivated biases in a multiple issues opinion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    5. Agnieszka Kowalska-Styczeń & Krzysztof Malarz, 2020. "Noise induced unanimity and disorder in opinion formation," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-22, July.
    6. Fan, Kangqi & Pedrycz, Witold, 2015. "Emergence and spread of extremist opinions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 87-97.
    7. Francisco J. León-Medina & Jordi Tena-Sánchez & Francisco J. Miguel, 2020. "Fakers becoming believers: how opinion dynamics are shaped by preference falsification, impression management and coherence heuristics," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(2), pages 385-412, April.
    8. Guillaume Deffuant & Ilaria Bertazzi & Sylvie Huet, 2018. "The Dark Side Of Gossips: Hints From A Simple Opinion Dynamics Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-20, September.
    9. 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).
    10. Martins, André C.R., 2022. "Extremism definitions in opinion dynamics models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    11. María Cecilia Gimenez & Luis Reinaudi & Ana Pamela Paz-García & Paulo Marcelo Centres & Antonio José Ramirez-Pastor, 2021. "Opinion evolution in the presence of constant propaganda: homogeneous and localized cases," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(1), pages 1-11, January.
    12. Fan, Kangqi & Pedrycz, Witold, 2016. "Opinion evolution influenced by informed agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 431-441.
    13. Song, Xiao & Shi, Wen & Tan, Gary & Ma, Yaofei, 2015. "Multi-level tolerance opinion dynamics in military command and control networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 322-332.
    14. Song, Xiao & Zhang, Shaoyun & Qian, Lidong, 2013. "Opinion dynamics in networked command and control organizations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5206-5217.
    15. Muslim, Roni & Wella, Sasfan A. & Nugraha, Ahmad R.T., 2022. "Phase transition in the majority rule model with the nonconformist agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P2).
    16. Gimenez, M. Cecilia & Paz García, Ana Pamela & Burgos Paci, Maxi A. & Reinaudi, Luis, 2016. "Range of interaction in an opinion evolution model of ideological self-positioning: Contagion, hesitance and polarization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 320-330.
    17. Khalil, Nagi, 2021. "Approach to consensus in models of continuous-opinion dynamics: A study inspired by the physics of granular gases," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    18. Wang, Huanjing & Shang, Lihui, 2015. "Opinion dynamics in networks with common-neighbors-based connections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 180-186.
    19. 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.
    20. Tiwari, Mukesh & Yang, Xiguang & Sen, Surajit, 2021. "Modeling the nonlinear effects of opinion kinematics in elections: A simple Ising model with random field based study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:168:y:2023:i:c:s096007792300036x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.