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Distributions of Opinion and Extremist Radicalization: Insights from Agent-Based Modeling

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Social Informatics (SocInfo 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8851))

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  • International Conference on Social Informatics

Abstract

We apply an agent-based opinion dynamics model to investigate the distribution of opinions and the size of opinion clusters. We use parameter sweeps to examine the sensitivity of opinion distributions and cluster sizes relative to changes in individuals’ tolerance and uncertainty. Our results demonstrate that opinion distributions and cluster sizes are structurally unstable, not stationary, and have fat tails in most configurations of the model, rather than stable Gaussian distributions. Hence, extremist radical individuals occur far more frequently than “normally” expected. Opinion clusters, in addition to being fat-tailed, reveal a dynamic transition from lognormal to exponential distributions as parameters change.

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Author information

Authors and Affiliations

  1. Department of Computational Social Science, George Mason University, Virginia, 22030, USA

    Meysam Alizadeh & Claudio Cioffi-Revilla

  2. Center for Social Complexity, Krasnow Institute for Advanced Study, George Mason University, Virginia, 22030, USA

    Claudio Cioffi-Revilla

Authors
  1. Meysam Alizadeh
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  2. Claudio Cioffi-Revilla
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Editor information

Editors and Affiliations

  1. Yahoo! Research, Barcelona, Spain

    Luca Maria Aiello

  2. Stanford Graduate School of Education, 485 Lasuen Mall, 94305-3096, Stanford, CA, USA

    Daniel McFarland

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Alizadeh, M., Cioffi-Revilla, C. (2014). Distributions of Opinion and Extremist Radicalization: Insights from Agent-Based Modeling. In: Aiello, L.M., McFarland, D. (eds) Social Informatics. SocInfo 2014. Lecture Notes in Computer Science, vol 8851. Springer, Cham. https://doi.org/10.1007/978-3-319-13734-6_26

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  • DOI: https://doi.org/10.1007/978-3-319-13734-6_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13733-9

  • Online ISBN: 978-3-319-13734-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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