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

  • Meysam Alizadeh
  • Claudio Cioffi-Revilla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8851)

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Costa Filho, R.N., Almeida, M.P., Andrade, J.S., Moreira, J.E.: Scaling behavior in a proportional voting process. Physical Review E 60(1), 1067 (1999)CrossRefGoogle Scholar
  2. 2.
    Brown, R., Condor, S., Mathews, A., Wade, G., Williams, J.: Explaining intergroup differentiation in an industrial organization. Journal of Occupational Psychology 59(4), 273–286 (1986)CrossRefGoogle Scholar
  3. 3.
    Atran, S., Ginges, J.: Religious and Sacred Imperatives in Human Conflict. Science 336(6083), 855–857 (2012)CrossRefGoogle Scholar
  4. 4.
    Alizadeh, M., Coman, A., Lewis, M., Cioffi-Revilla, C.: Intergroup Conflict Escalation Leads to more Extremism. Journal of Artificial Societies and Social Simulation 17(4), 4 (2014)Google Scholar
  5. 5.
    Zeitzoff, T.: Using Social Media to Measure Conflict Dynamics: An Application to the 2008–2009 Gaza Conflict. Journal of Conflict Resolution 6, 938–969 (2011)CrossRefGoogle Scholar
  6. 6.
    Tufekci, Z., Wilson, C.: Social Media and the Decision to Participate in Political Protest: Observations From Tahrir Square. Journal of Communication 62(2), 363–379 (2012)CrossRefGoogle Scholar
  7. 7.
    Elson, S.B., Yeung, D., Roshan, P., Bohandy, S.R., Nader, A.: Using Social Media to Gauge Iranian Public Opinion and Mood After the, Election. RAND Corporation Technical Report (2012)Google Scholar
  8. 8.
    Mitchell, A., Hitlin, P.: Twitter reaction to events often at odds with overall public opinion. Pew Research Center (2013)Google Scholar
  9. 9.
    Mitchell, A., Guskin, E.: Twitter News Consumers: Young, Mobile and Educated. Pew Research Center (2013)Google Scholar
  10. 10.
    Festinger, L.A.: Theory of Cognitive Dissonance. Stanford University Press, Stanford (1957)Google Scholar
  11. 11.
    Sherif, M., Hovland, C.I.: Social judgment: Assimilation and contrast effects in communication and attitude change. Yale University Press, Oxford (1961)Google Scholar
  12. 12.
    Tajfel, H., Turner, J.C.: An integrative theory of intergroup conflict. The Social Psychology of Intergroup Relations, 33–47 (1979)Google Scholar
  13. 13.
    Axelrod, R.: The Dissemination of Culture: A Model with Local Convergence and Global Polarization. Journal of Conflict Resolution 41(2), 203–226 (1997)CrossRefGoogle Scholar
  14. 14.
    Flache, A., Macy, M.W.: Local Convergence and Global Diversity: From Interpersonal to Social Influence. Journal of Conflict Resolution 55(6), 970–995 (2011)CrossRefGoogle Scholar
  15. 15.
    Deffuant, G., Neau, D., Amblard, F., Weisbuch, G.: Mixing beliefs among interacting agents. Advances in Complex Systems 3(1-4), 87–98 (2000)CrossRefGoogle Scholar
  16. 16.
    Huet, S., Deffuant, G., Jager, W.: Rejection mechanism in 2D bounded confidence provides more conformity. Advances in Complex Systems 11(4), 529–549 (2008)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Mäs, M., Flache, A., Kitts, J.: Cultural Integration and Differentiation in Groups and Organizations. In: Dignum, V., Dignum, F. (eds.) Perspectives on Culture and Agent-based Simulations, vol. 3, pp. 71–90. Springer International Publishing (2014)Google Scholar
  18. 18.
    Mäs, M., Flache, A., Helbing, D.: Individualization as driving force of clustering phenomena in humans. PLoS Comput. Biol. 6(10), e1000959 (2010)Google Scholar
  19. 19.
    Weidlich, W.: The statistical description of polarization phenomena in society. British. Journal of Mathematical and Statistical Psychology 24, 251–266 (1971)CrossRefMATHGoogle Scholar
  20. 20.
    Sznajd-Weron, K.J.S.: Opinion evolution in closed community. International Journal of Modern Physics C 11(6), 1157–1165Google Scholar
  21. 21.
    Galam, S.: Heterogeneous beliefs, segregation, and extremism in the making of public opinions. Physical Review E 71(4), 046123 (2005)Google Scholar
  22. 22.
    Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Reviews of Modern Physics 81, 591–646 (2009)CrossRefGoogle Scholar
  23. 23.
    Gilbert, N.: Agent-Based Models. Sage Publishers, Thousand Oaks (2008)Google Scholar
  24. 24.
    Cioffi-Revilla, C.: Introduction to Computational Social Science: Principles and Applications. London and Heidelberg. Springer (2014)Google Scholar
  25. 25.
    Hegselmann, R., Krauze, U.: Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation 5(3) (2002), http://jasss.soc.surrey.ac.uk/5/3/2.html
  26. 26.
    Deffuant, G.: Comparing Extremism Propagation Patterns in Continuous Opinion Models. Journal of Artificial Societies and Social Simulation, 9(3) (2006)Google Scholar
  27. 27.
    Alstott, J., Bullmore, E., Plenz, D.: powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions. PLoS ONE 9(4), e95816 (2014), doi:10.1371/journal.pone.0095816Google Scholar
  28. 28.
    Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-Law Distributions in Empirical Data. SIAM Review 51(4), 661–703 (2009)CrossRefMATHMathSciNetGoogle Scholar
  29. 29.
    Smith, M.A., Rainie, L., Shneiderman, B., Himelboim, I.: Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Center (2014)Google Scholar
  30. 30.
    Kellstedt, P.: Race prejudice and Power Laws of Extremism. In: Cioffi-Revilla, C. (ed.) Power Laws and Non-Equilibrium Distributions of Complexity in the Social Sciences (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Meysam Alizadeh
    • 1
  • Claudio Cioffi-Revilla
    • 1
    • 2
  1. 1.Department of Computational Social ScienceGeorge Mason UniversityVirginiaUSA
  2. 2.Center for Social Complexity, Krasnow Institute for Advanced StudyGeorge Mason UniversityVirginiaUSA

Personalised recommendations