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A Psychologically-Motivated Model of Opinion Change with Applications to American Politics

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Abstract

Agent-based models are versatile tools for studying how societal opinion change, including political polarization and cultural diffusion, emerges from individual behavior. This study expands agents' psychological realism using empirically-motivated rules governing interpersonal influence, commitment to previous beliefs, and conformity in social contexts. Computational experiments establish that these extensions produce three novel results: (a) sustained strong diversity of opinions within the population, (b) opinion subcultures, and (c) pluralistic ignorance. These phenomena arise from a combination of agents' intolerance, susceptibility and conformity, with extremist agents and social networks playing important roles. The distribution and dynamics of simulated opinions reproduce two empirical datasets on Americans' political opinions.

Suggested Citation

  • 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.
    • Handle: RePEc:jas:jasssj:2016-121-2
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    Citations

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    Cited by:

    1. Sven Banisch & Eckehard Olbrich, 2021. "An Argument Communication Model of Polarization and Ideological Alignment," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 24(1), pages 1-1.
    2. 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).
    3. 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).
    4. Stern, Samuel & Livan, Giacomo, 2021. "The impact of noise and topology on opinion dynamics in social networks," LSE Research Online Documents on Economics 113424, London School of Economics and Political Science, LSE Library.
    5. 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).
    6. 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.
    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. Jan-Philipp Fränken & Toby Pilditch, 2021. "Cascades Across Networks Are Sufficient for the Formation of Echo Chambers: An Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 24(3), pages 1-1.
    9. Aleksejus Kononovicius, 2017. "Empirical Analysis and Agent-Based Modeling of the Lithuanian Parliamentary Elections," Complexity, Hindawi, vol. 2017, pages 1-15, November.
    10. 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.
    11. 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.

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