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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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. 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.
    5. Qesmi, Redouane, 2021. "Dynamics of an opinion model with threshold-type delay," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

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