Model Exploration and Computer Experiments

Part of the SpringerBriefs in Complexity book series (BRIEFSCOMPLEXITY)


This chapter contains a description of a set of simulation experiments for exploration of the agent-based model proposed in the present work, devised to illustrate the model’s generative capacity and highlight the influence of the newly introduced mechanisms on the complexity of the solutions. The first experiment shows the influence of the critical “cop”-to-“active” ratio ρ c in the risk perception model on the size, duration, and recurrence of rebellion peaks. The relationship between ρ c , the occurrence of cascades and the maximum possible peak size was demonstrated analytically and then studied via computer simulations. It was shown that the value of ρ c has a strong impact on the stability of the system and has associated tipping points. The second experiment illustrates the influence of the maximum jail term on the interval between successive events of social unrest. The third experiment was devised to study the effect of value-sensitive deprivation in a scenario of low legitimacy and high level of repression. It was shown that the model can produce solutions with three different regimes (calm, punctuated equilibrium, and permanent turmoil) for low values of legitimacy. It was also found that the solutions’ behavior is strongly dependent on the parameter γ that controls sensitivity to deprivation, which has an associated tipping point for the setup conditions considered in the experiment. The fourth and fifth experiments illustrate the effect of combining RD-dependent grievance with legitimacy feedback in the same scenario of low legitimacy and high level of repression considered before, and how legitimacy feedback leads to solutions with intermittent regime in an otherwise stable scenario with high legitimacy and low level of repression. The final experiment shows how network influence effects lead to instability for the two types of networks implemented in the model (“group” and “influentials”), and that the degree of connectivity has a larger impact than the influence weight on the magnitude of the simulated events.


Simulation of social conflict Risk perception Relative deprivation Endogenous legitimacy feedback Network influence effects Parameterization and validation 


  1. 11.
    R. Collins, Violence: A Micro-sociological Theory (Princeton University Press, Princeton, 2008)CrossRefGoogle Scholar
  2. 12.
    R. Collins, Micro and macro causes of violence. Int. J. Conflict Violence 3(1), 9–22 (2009)Google Scholar
  3. 13.
    A. Comninos, Twitter revolutions and cyber crackdowns. User-generated content and social networking in the Arab spring and beyond. Technical report, Association of Progressive Communication (PAC), 2011Google Scholar
  4. 21.
    J.M. Epstein, Modeling civil violence: An agent-based computational approach. Proc. Natl. Acad. Sci. USA 99, 7243–7250 (2002)CrossRefGoogle Scholar
  5. 24.
    J.M. Epstein, J.D. Steinbruner, M.T. Parker, Modeling Civil Violence: An Agent-Based Computational Approach. Center on Social and Economic Dynamics, Working Paper No. 20, January 2001Google Scholar
  6. 25.
    D. Faris, Revolutions Without Revolutionaries? Social Media Networks and Regime Response in Egypt. Ph.D. thesis, University of Pennsylvania, 2010Google Scholar
  7. 26.
    M. Fonoberova, V.A. Fonoberov, I. Mezic, J. Mezic, P. Jeffrey Brantingham, Nonlinear dynamics of crime and violence in urban settings. J. Artif. Soc. Soc. Simul. 15(1), (2012)Google Scholar
  8. 35.
    M.S. Granovetter, Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420–1443 (1978)CrossRefGoogle Scholar
  9. 45.
    C.M. Lemos, On Agent-Based Modelling of Large Scale Conflict Against a Central Authority: from Mechanisms to Complex Behaviour. Ph.D. thesis, ISCTE - University Institute of Lisbon and Faculty of Sciences of the University of Lisbon, December 2016. ISBN: 978-989-8862-62-4Google Scholar
  10. 46.
    K. Lorenz, On Aggression (Routledge Classics, London, 2002)Google Scholar
  11. 51.
    A. Mood, F.A. Graybill, D.C. Boes, Introduction to the Theory of Statistics, 3rd edn. (McGraw-Hill, New York, 1974)Google Scholar
  12. 52.
    A. Moro, Understanding the dynamics of violent political revolutions in an agent-based framework. PLoS ONE 11(4), 1–17 (2016)CrossRefGoogle Scholar
  13. 71.
    The Robert S. Strauss Center, Social Conflict Analysis Database., 2015. Accessed 2015-07-25
  14. 76.
    P.-O.H. Wikström, K.H. Treiber, Violence as situational action. Int. J. Conflict Violence 3(1), 75–96 (2009)Google Scholar
  15. 77.
    U. Wilensky, NetLogo. Center for Connected Learning and Computer-Based Modelling (Northwestern University, Evanston, IL, 1999)Google Scholar
  16. 78.
    U. Wilensky, NetLogo Rebellion Model (Northwestern University, Evanston, IL, 2004)Google Scholar
  17. 79.
    Wolfram Research, Inc., Mathematica, version 10.1 edn. (Champaign, Illinois, 2015)Google Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Department of Religion, Philosophy and HistoryUniversity of AgderKristiansandNorway

Personalised recommendations