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Persuasion without polarization? Modelling persuasive argument communication in teams with strong faultlines

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  • Thomas Feliciani

    (University of Groningen
    University College Dublin)

  • Andreas Flache

    (University of Groningen)

  • Michael Mäs

    (University of Groningen)

Abstract

Strong demographic faultlines are a potential source of conflict in teams. To study conditions under which faultlines can result in between-group bi-polarization of opinions, a computational model of persuasive argument communication has been proposed. We identify two hitherto overlooked degrees of freedom in how researchers formalized the theory. First, are arguments agents communicate influencing each other’s opinions explicitly or implicitly represented in the model? Second, does similarity between agents increase chances of interaction or the persuasiveness of others’ arguments? Here we examine these degrees of freedom in order to assess their effect on the model’s predictions. We find that both degrees of freedom matter: in a team with strong demographic faultline, the model predicts more between-group bi-polarization when (1) arguments are represented explicitly, and (2) when homophily is modelled such that the interaction between similar agents are more likely (instead of more persuasive).

Suggested Citation

  • 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.
  • Handle: RePEc:spr:comaot:v:27:y:2021:i:1:d:10.1007_s10588-020-09315-8
    DOI: 10.1007/s10588-020-09315-8
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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. 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.
    3. Duanxu Wang & Xin Pi & Yuhao Pan, 2017. "The interpersonal diffusion mechanism of unethical behavior in groups: a social network perspective," Computational and Mathematical Organization Theory, Springer, vol. 23(2), pages 271-292, June.
    4. J. Richard Harrison & Glenn R. Carroll, 2002. "The Dynamics of Cultural Influence Networks," Computational and Mathematical Organization Theory, Springer, vol. 8(1), pages 5-30, May.
    5. Jean-Yves Duclos & Joan Esteban & Debraj Ray, 2004. "Polarization: Concepts, Measurement, Estimation," Econometrica, Econometric Society, vol. 72(6), pages 1737-1772, November.
    6. Thomas Feliciani & Andreas Flache & Jochem Tolsma, 2017. "How, when and where can Spatial Segregation Induce Opinion Polarization? Two Competing Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(2), pages 1-6.
    7. Davide Secchi & Nicole L. Gullekson, 2016. "Individual and organizational conditions for the emergence and evolution of bandwagons," Computational and Mathematical Organization Theory, Springer, vol. 22(1), pages 88-133, March.
    8. 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.
    9. Fu, Guiyuan & Zhang, Weidong, 2016. "Opinion formation and bi-polarization with biased assimilation and homophily," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 700-712.
    10. Ray Reagans, 2011. "Close Encounters: Analyzing How Social Similarity and Propinquity Contribute to Strong Network Connections," Organization Science, INFORMS, vol. 22(4), pages 835-849, August.
    11. Lisa Hope Pelled, 1996. "Demographic Diversity, Conflict, and Work Group Outcomes: An Intervening Process Theory," Organization Science, INFORMS, vol. 7(6), pages 615-631, December.
    12. Juliette Rouchier & Paola Tubaro & Cécile Emery, 2014. "Opinion transmission in organizations: an agent-based modeling approach," Computational and Mathematical Organization Theory, Springer, vol. 20(3), pages 252-277, September.
    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.
    14. Michael Mäs & Andreas Flache & Károly Takács & Karen A. Jehn, 2013. "In the Short Term We Divide, in the Long Term We Unite: Demographic Crisscrossing and the Effects of Faultlines on Subgroup Polarization," Organization Science, INFORMS, vol. 24(3), pages 716-736, June.
    15. 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.
    16. André Grow & Andreas Flache, 2011. "How attitude certainty tempers the effects of faultlines in demographically diverse teams," Computational and Mathematical Organization Theory, Springer, vol. 17(2), pages 196-224, May.
    17. David Anzola & Peter Barbrook-Johnson & Juan I. Cano, 2017. "Self-organization and social science," Computational and Mathematical Organization Theory, Springer, vol. 23(2), pages 221-257, June.
    18. Liang Chen & Guy G. Gable & Haibo Hu, 2013. "Communication and organizational social networks: a simulation model," Computational and Mathematical Organization Theory, Springer, vol. 19(4), pages 460-479, December.
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