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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
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