IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v21y2015i2d10.1007_s10588-015-9181-1.html
   My bibliography  Save this article

Opinions, influence, and zealotry: a computational study on stubbornness

Author

Listed:
  • Dustin L. Arendt

    (Pacific Northwest National Laboratory)

  • Leslie M. Blaha

    (Air Force Research Laboratory)

Abstract

We present a simple, efficient, and predictive model for opinion dynamics with zealots. Our model captures curvature-driven dynamics (e.g., clear, smooth boundaries separating domains whose curvature decreases over time) through a simple, individual rule, providing a method for rapidly testing basic hypotheses about innovation diffusion, opinion dynamics, and related phenomena. Our model belongs to a class of models called dimer automata, which are asynchronous, graph-based (i.e., non-uniform lattice) variants of cellular automata. Individuals in the model update their states via a dyadic update rule; population opinion dynamics emerge from these pairwise interactions. Zealots are stubborn individuals whose opinion is not susceptible to influence by others. We observe experimentally that a system without zealots usually converges to the majority opinion, but a relatively small number of zealots can sway the opinion of the whole population. The influence of zealots can be further increased by placing zealots at more effective locations within the network. These locations can be determined by rankings from standard social network analysis metrics, or by using a greedy algorithm for influence maximization. We apply the influence maximization technique to a politically polarized social network to explore opinion dynamics in a real-world network and to gain insight about influence and political entrenchment through the zealot model’s ability to sway the entire network to one side or the other.

Suggested Citation

  • Dustin L. Arendt & Leslie M. Blaha, 2015. "Opinions, influence, and zealotry: a computational study on stubbornness," Computational and Mathematical Organization Theory, Springer, vol. 21(2), pages 184-209, June.
    • Handle: RePEc:spr:comaot:v:21:y:2015:i:2:d:10.1007_s10588-015-9181-1
    DOI: 10.1007/s10588-015-9181-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-015-9181-1
    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/s10588-015-9181-1?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. Ning Nan & Robert Zmud & Emre Yetgin, 2014. "A complex adaptive systems perspective of innovation diffusion: an integrated theory and validated virtual laboratory," Computational and Mathematical Organization Theory, Springer, vol. 20(1), pages 52-88, March.
    2. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Raúl M. Ortiz-Gaona & Marcos Postigo-Boix & José L. Melús-Moreno, 2021. "Extent prediction of the information and influence propagation in online social networks," Computational and Mathematical Organization Theory, Springer, vol. 27(2), pages 195-230, June.
    2. Markus Brede, 2019. "How Does Active Participation Affect Consensus: Adaptive Network Model of Opinion Dynamics and Influence Maximizing Rewiring," Complexity, Hindawi, vol. 2019, pages 1-16, June.
    3. 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).
    4. Qesmi, Redouane, 2021. "Dynamics of an opinion model with threshold-type delay," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    5. William G. Kennedy & Robert St. Amant & David Reitter, 2016. "Behavior representation in modeling and simulation: introduction to CMOT special issue: BRiMS 2013," Computational and Mathematical Organization Theory, Springer, vol. 22(1), pages 1-3, March.

    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. 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.
    2. D׳Souza, Derrick E. & Kulkarni, Shailesh S., 2015. "A framework and model for absorptive capacity in a dynamic multi-firm environment," International Journal of Production Economics, Elsevier, vol. 167(C), pages 50-62.
    3. Hayes, Samantha & Desha, Cheryl & Baumeister, Dayna, 2020. "Learning from nature – Biomimicry innovation to support infrastructure sustainability and resilience," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    4. Gregory Vial, 2023. "A Complex Adaptive Systems Perspective of Software Reuse in the Digital Age: An Agenda for IS Research," Information Systems Research, INFORMS, vol. 34(4), pages 1728-1743, December.
    5. Pedro Lopez-Merino & Juliette Rouchier, 2021. "An agent-based model of (food) consumption: Accounting for the Intention-Behaviour-Gap on three dimensions of characteristics with limited knowledge," Post-Print hal-03618377, HAL.
    6. Raúl M. Ortiz-Gaona & Marcos Postigo-Boix & José L. Melús-Moreno, 2021. "Extent prediction of the information and influence propagation in online social networks," Computational and Mathematical Organization Theory, Springer, vol. 27(2), pages 195-230, June.
    7. 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.
    8. Jeroen van der Heijden, 2020. "Understanding voluntary program performance: Introducing the diffusion network perspective," Regulation & Governance, John Wiley & Sons, vol. 14(1), pages 44-62, January.

    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:21:y:2015:i:2:d:10.1007_s10588-015-9181-1. 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.