IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v589y2022ics0378437121008748.html
   My bibliography  Save this article

An opinion dynamics model of meta-contrast with continuous social influence forces

Author

Listed:
  • Weimer, Christopher W.
  • Miller, J.O.
  • Hill, Raymond R.
  • Hodson, Douglas D.

Abstract

Opinion dynamics is the study of how opinions in a group of individuals change over time and opinion dynamics models attempt to mathematically formulate this process. This research lays the foundations for, and develops the meta-contrast influence field model, a novel opinion dynamics model based on self-categorization theory. It improves on the existing meta-contrast model by providing a properly scaled, continuous influence basis while replicating key results of the original model. This influence basis is modular in nature, allowing future research to include other competing psychological forces in the mathematical formulation of influence. This flexibility is achieved while drastically reducing computational complexity, making feasible larger models of more psychologically complex agents.

Suggested Citation

  • Weimer, Christopher W. & Miller, J.O. & Hill, Raymond R. & Hodson, Douglas D., 2022. "An opinion dynamics model of meta-contrast with continuous social influence forces," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
  • Handle: RePEc:eee:phsmap:v:589:y:2022:i:c:s0378437121008748
    DOI: 10.1016/j.physa.2021.126617
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121008748
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2021.126617?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. Károly Takács & Andreas Flache & Michael Mäs, 2016. "Discrepancy and Disliking Do Not Induce Negative Opinion Shifts," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-21, June.
    2. Andreas Flache & Michael Mäs, 2008. "How to get the timing right. A computational model of the effects of the timing of contacts on team cohesion in demographically diverse teams," Computational and Mathematical Organization Theory, Springer, vol. 14(1), pages 23-51, March.
    3. Takasumi Kurahashi-Nakamura & Michael Mäs & Jan Lorenz, 2016. "Robust Clustering in Generalized Bounded Confidence Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(4), pages 1-7.
    4. 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.
    5. Guillaume Deffuant & Frederic Amblard & Gérard Weisbuch, 2002. "How Can Extremism Prevail? a Study Based on the Relative Agreement Interaction Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-1.
    6. Pawel Sobkowicz, 2009. "Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-11.
    7. Laurent Salzarulo, 2006. "A Continuous Opinion Dynamics Model Based on the Principle of Meta-Contrast," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(1), pages 1-13.
    8. Christopher Weimer & J.O. Miller & Raymond Hill & Douglas Hodson, 2019. "Agent Scheduling in Opinion Dynamics: A Taxonomy and Comparison Using Generalized Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(4), pages 1-5.
    9. Peter Duggins, 2017. "A Psychologically-Motivated Model of Opinion Change with Applications to American Politics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-13.
    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. Sylvie Huet & Guillaume Deffuant, 2010. "Openness Leads To Opinion Stability And Narrowness To Volatility," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 405-423.
    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.
    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. 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.
    2. 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.
    3. Andreas Flache, 2018. "About Renegades And Outgroup Haters: Modeling The Link Between Social Influence And Intergroup Attitudes," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-32, September.
    4. 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.
    5. 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).
    6. 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.
    7. Sylvie Huet & Jean-Denis Mathias, 2018. "Few Self-Involved Agents Among Bounded Confidence Agents Can Change Norms," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-27, September.
    8. Cui, Peng-Bi, 2023. "Exploring the foundation of social diversity and coherence with a novel attraction–repulsion model framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    9. Boschi, Gioia & Cammarota, Chiara & Kühn, Reimer, 2021. "Opinion dynamics with emergent collective memory: The impact of a long and heterogeneous news history," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    10. Ghezelbash, Ehsan & Yazdanpanah, Mohammad Javad & Asadpour, Masoud, 2019. "Polarization in cooperative networks through optimal placement of informed agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    11. Shyam Gouri Suresh & Scott Jeffrey, 2017. "The Consequences of Social Pressures on Partisan Opinion Dynamics," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(2), pages 242-259, March.
    12. 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).
    13. 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).
    14. Carpentras, Dino & Quayle, Michael, 2022. "Propagation of measurement error in opinion dynamics models: The case of the Deffuant model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    15. Deffuant, Guillaume & Keijzer, Marijn & Banisch, Sven, 2023. "Regular access to constantly renewed online content favors radicalization of opinions," IAST Working Papers 23-154, Institute for Advanced Study in Toulouse (IAST).
    16. 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).
    17. Lipiecki, Arkadiusz & Sznajd-Weron, Katarzyna, 2022. "Polarization in the three-state q-voter model with anticonformity and bounded confidence," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    18. 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.
    19. Laurent Salzarulo, 2006. "A Continuous Opinion Dynamics Model Based on the Principle of Meta-Contrast," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(1), pages 1-13.
    20. Thomas Feliciani & Andreas Flache & Michael Mäs, 2021. "Persuasion without polarization? Modelling persuasive argument communication in teams with strong faultlines," Computational and Mathematical Organization Theory, Springer, vol. 27(1), pages 61-92, March.

    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:phsmap:v:589:y:2022:i:c:s0378437121008748. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.