IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v27y2021i1d10.1007_s10588-020-09315-8.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-020-09315-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10588-020-09315-8?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. 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. 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.
    5. 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.
    6. Jean-Yves Duclos & Joan Esteban & Debraj Ray, 2004. "Polarization: Concepts, Measurement, Estimation," Econometrica, Econometric Society, vol. 72(6), pages 1737-1772, November.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    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. 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.
    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. 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.
    4. 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.
    5. Tanzhe Tang & Amineh Ghorbani & Flaminio Squazzoni & Caspar G. Chorus, 2022. "Together alone: a group-based polarization measurement," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3587-3619, October.
    6. 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.
    7. 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.
    8. 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).
    9. Azzimonti, Marina & Fernandes, Marcos, 2023. "Social media networks, fake news, and polarization," European Journal of Political Economy, Elsevier, vol. 76(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. Baumann, Fabian & Sokolov, Igor M. & Tyloo, Melvyn, 2020. "A Laplacian approach to stubborn agents and their role in opinion formation on influence networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    12. 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.
    13. Patrick Mellacher, 2021. "Opinion Dynamics with Conflicting Interests," Papers 2111.09408, arXiv.org.
    14. Pedraza, Lucía & Pinasco, Juan Pablo & Saintier, Nicolas & Balenzuela, Pablo, 2021. "An analytical formulation for multidimensional continuous opinion models," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    15. Fabrizio Maturo & Stefania Migliori & Francesco Paolone, 2019. "Measuring and monitoring diversity in organizations through functional instruments with an application to ethnic workforce diversity of the U.S. Federal Agencies," Computational and Mathematical Organization Theory, Springer, vol. 25(4), pages 357-388, December.
    16. 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.
    17. Bruno Trezzini, 2013. "A measure of multidimensional polarization for categorical diversity data," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 313-333, January.
    18. 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).
    19. Kozitsin, Ivan V., 2024. "Optimal control in opinion dynamics models: diversity of influence mechanisms and complex influence hierarchies," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    20. Aleksejus Kononovicius, 2017. "Empirical Analysis and Agent-Based Modeling of the Lithuanian Parliamentary Elections," Complexity, Hindawi, vol. 2017, pages 1-15, November.

    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:spr:comaot:v:27:y:2021:i:1:d:10.1007_s10588-020-09315-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.