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Uniformity, Bipolarization and Pluriformity Captured as Generic Stylized Behavior with an Agent-Based Simulation Model of Attitude Change

Author

Listed:
  • Wander Jager

    (University of Groningen)

  • Frédéric Amblard

    (Ecole Normale Supérieure)

Abstract

This paper focuses at the dynamics of attitude change in large groups. A multi-agent computer simulation has been developed as a tool to study hypothesis we take to study these dynamics. A major extension in comparison to earlier models is that Social Judgment Theory is being formalized to incorporate processes of assimilation and contrast in persuasion processes. Results demonstrate that the attitude structure of agents determines the occurrence of assimilation and contrast effects, which in turn cause a group of agents to reach consensus, to bipolarize, or to develop a number of subgroups sharing the same position. Subsequent experiments demonstrate the robustness of these effects for a different formalization of the social network, and the susceptibility for population size.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:comaot:v:10:y:2005:i:4:d:10.1007_s10588-005-6282-2
    DOI: 10.1007/s10588-005-6282-2
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    Cited by:

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    3. 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.
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    5. Tinggui Chen & Qianqian Li & Jianjun Yang & Guodong Cong & Gongfa Li, 2019. "Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity," Mathematics, MDPI, vol. 7(10), pages 1-33, October.
    6. 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.
    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. 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).
    9. 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).
    10. 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.
    11. Victorien Barbet & Noé Guiraud & Vincent Laperrière & Juliette Rouchier, 2019. "Haggling on Values: Towards Consensus or Trouble," Working Papers halshs-02066846, HAL.
    12. George Butler & Gabriella Pigozzi & Juliette Rouchier, 2019. "Mixing Dyadic and Deliberative Opinion Dynamics in an Agent-Based Model of Group Decision-Making," Complexity, Hindawi, vol. 2019, pages 1-31, August.
    13. 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).
    14. 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).
    15. 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.
    16. 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).
    17. 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.
    18. Snellman, Jan E. & Barrio, Rafael A. & Kaski, Kimmo K., 2021. "Social structure formation in a network of agents playing a hybrid of ultimatum and dictator games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
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