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An Agent-Based Model for Emergent Opponent Behavior

  • Koen van der ZwetEmail author
  • Ana Isabel Barros
  • Tom M. van Engers
  • Bob van der Vecht
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

Organized crime, insurgency and terrorist organizations have a large and undermining impact on societies. This highlights the urgency to better understand the complex dynamics of these individuals and organizations in order to timely detect critical social phase transitions that form a risk for society. In this paper we introduce a new multi-level modelling approach that integrates insights from complex systems, criminology, psychology, and organizational studies with agent-based modelling. We use a bottom-up approach to model the active and adaptive reactions by individuals to the society, the economic situation and law enforcement activity. This approach enables analyzing the behavioral transitions of individuals and associated micro processes, and the emergent networks and organizations influenced by events at meso- and macro-level. At a meso-level it provides an experimentation analysis modelling platform of the development of opponent organization subject to the competitive characteristics of the environment and possible interventions by law enforcement. While our model is theoretically founded on findings in literature and empirical validation is still work in progress, our current model already enables a better understanding of the mechanism leading to social transitions at the macro-level. The potential of this approach is illustrated with computational results.

Keywords

Opponent behavior Opponent networks Multidisciplinary Complex adaptive systems Agent-based modelling 

References

  1. 1.
    Barsade, S.G.: The ripple effect: emotional contagion and its influence on group behavior. Adm. Sci. Q. 47(4), 644–675 (2002)CrossRefGoogle Scholar
  2. 2.
    Bharathy, G.K., Silverman, B.: Validating agent based social systems models. In: Proceedings of the 2010 Winter Simulation Conference (WSC), pp. 441–453. IEEE (2010)Google Scholar
  3. 3.
    Bohorquez, J.C., Gourley, S., Dixon, A.R., Spagat, M., Johnson, N.F.: Common ecology quantifies human insurgency. Nature 462(7275), 911 (2009)CrossRefGoogle Scholar
  4. 4.
    Bright, D.A., Delaney, J.J.: Evolution of a drug trafficking network: mapping changes in network structure and function across time. Glob. Crime 14(2–3), 238–260 (2013)CrossRefGoogle Scholar
  5. 5.
    Bright, D.A., Greenhill, C., Ritter, A., Morselli, C.: Networks within networks: using multiple link types to examine network structure and identify key actors in a drug trafficking operation. Glob. Crime 16(3), 219–237 (2015)CrossRefGoogle Scholar
  6. 6.
    Carley, K.M., Dombroski, M., Tsvetovat, M., Reminga, J., Kamneva, N., et al.: Destabilizing dynamic covert networks. In: Proceedings of the 8th international Command and Control Research and Technology Symposium (2003)Google Scholar
  7. 7.
    Cioffi-Revilla, C., Rouleau, M.: Mason rebeland: an agent-based model of politics, environment, and insurgency. Int. Stud. Rev. 12(1), 31–52 (2010)CrossRefGoogle Scholar
  8. 8.
    Clauset, A., Gleditsch, K.S.: The developmental dynamics of terrorist organizations. PloS one 7(11), e48633 (2012)CrossRefGoogle Scholar
  9. 9.
    Cornish, D.B., Clarke, R.V.: Understanding crime displacement: an application of rational choice theory. Criminology 25(4), 933–948 (1987)CrossRefGoogle Scholar
  10. 10.
    Duijn, P.: Detecting and disrupting criminal networks: a data driven approach (2016)Google Scholar
  11. 11.
    Easton, S.T., Karaivanov, A.K.: Understanding optimal criminal networks. Glob. Crime 10(1–2), 41–65 (2009)CrossRefGoogle Scholar
  12. 12.
    Eidelson, R.J.: Complex adaptive systems in the behavioral and social sciences. Rev. Gen. Psychol. 1(1), 42 (1997)CrossRefGoogle Scholar
  13. 13.
    Enders, W., Su, X.: Rational terrorists and optimal network structure. J. Conflict Resolut. 51(1), 33–57 (2007)CrossRefGoogle Scholar
  14. 14.
    Epstein, J.M.: Modeling civil violence: an agent-based computational approach. Proc. Nat. Acad. Sci. 99(suppl 3), 7243–7250 (2002)CrossRefGoogle Scholar
  15. 15.
    Faria, J.R., Arce, D.: Counterterrorism and its impact on terror support and recruitment: accounting for backlash. Def. Peace Econ. 23(5), 431–445 (2012)CrossRefGoogle Scholar
  16. 16.
    Framis, A.G.S.: Illegal networks or criminal organizations: power, roles and facilitators in four cocaine trafficking structures (2011)Google Scholar
  17. 17.
    Bichler, G., Malm, A., Cooper, T.: Drug supply networks: a systematic review of the organizational structure of illicit drug trade. Crime Sci. 6(1), 2 (2017)CrossRefGoogle Scholar
  18. 18.
    Ganor, B.: Terrorist organization typologies and the probability of a boomerang effect. Stud. Confl. Terror. 31(4), 269–283 (2008).  https://doi.org/10.1080/10576100801925208CrossRefGoogle Scholar
  19. 19.
    Georgeff, M.P., Lansky, A.L.: Reactive reasoning and planning. In: AAAI, vol. 87, pp. 677–682 (1987)Google Scholar
  20. 20.
    Gilbert, N.: Agent-based models (2008).  https://doi.org/10.4135/9781412983259CrossRefGoogle Scholar
  21. 21.
    Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J., Railsback, S.F.: The odd protocol: a review and first update. Ecol. Model. 221(23), 2760–2768 (2010)CrossRefGoogle Scholar
  22. 22.
    Holland, J.H.: Hidden Orderhow Adaptation Builds Complexity. Reading, Addison-Wesley (1995). No. 003.7 H6Google Scholar
  23. 23.
    Johnson, N.: Simply Complexity: A Clear Guide to Complexity Theory. Oneworld Publications, Oxford (2009)Google Scholar
  24. 24.
    Keller, J.P., Desouza, K.C., Lin, Y.: Dismantling terrorist networks: evaluating strategic options using agent-based modeling. Technol. Forecast. Soc. Chang. 77(7), 1014–1036 (2010)CrossRefGoogle Scholar
  25. 25.
    Levy, M.: Social phase transitions. J. Econ. Behav. Organ. 57(1), 71–87 (2005)CrossRefGoogle Scholar
  26. 26.
    Ligon, G.S., Simi, P., Harms, M., Harris, D.J.: Putting the “o” in veos: What makes an organization? Dyn. Asymmetric Confl. 6(1–3), 110–134 (2013)CrossRefGoogle Scholar
  27. 27.
    MacKerrow, E.P.: Understanding why-dissecting radical islamist terrorism with agent-based simulation. Los Alamos Sci. 28, 184–191 (2003)Google Scholar
  28. 28.
    Makarenko, T.: The crime-terror continuum: tracing the interplay between transnational organised crime and terrorism. Glob. Crime 6(1), 129–145 (2004)MathSciNetCrossRefGoogle Scholar
  29. 29.
    McCauley, C., Moskalenko, S.: Mechanisms of political radicalization: pathways toward terrorism. Terror. Polit. Violence 20(3), 415–433 (2008)CrossRefGoogle Scholar
  30. 30.
    McCauley, C., Moskalenko, S.: Understanding political radicalization: the two-pyramids model. Am. Psychol. 72(3), 205 (2017)CrossRefGoogle Scholar
  31. 31.
    Mintzberg, H., Waters, J.A.: Of strategies, deliberate and emergent. Strateg. Manag. J. 6(3), 257–272 (1985)CrossRefGoogle Scholar
  32. 32.
    Moon, I.C., Carley, K.M.: Modeling and simulating terrorist networks in social and geospatial dimensions. IEEE Intell. Syst. 22(5), 40–49 (2007)CrossRefGoogle Scholar
  33. 33.
    Morselli, C., Giguère, C., Petit, K.: The efficiency/security trade-off in criminal networks. Soc. Netw. 29(1), 143–153 (2007)CrossRefGoogle Scholar
  34. 34.
    Prokopenko, M., Boschetti, F., Ryan, A.J.: An information-theoretic primer on complexity, self-organization, and emergence. Complexity 15(1), 11–28 (2009)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Pruyt, E., Kwakkel, J.H.: Radicalization under deep uncertainty: a multi-model exploration of activism, extremism, and terrorism. Syst. Dyn. Rev. 30(1–2), 1–28 (2014)CrossRefGoogle Scholar
  36. 36.
    Ríos, V.: Why did mexico become so violent? a self-reinforcing violent equilibrium caused by competition and enforcement. Trends Organ. Crime 16(2), 138–155 (2013)CrossRefGoogle Scholar
  37. 37.
    Taylor, S.: Crime and Criminality: A Multidisciplinary Approach. Routledge (2015)Google Scholar
  38. 38.
    Singh, K., Sajjad, M., Ahn, C.W.: Towards full scale population dynamics modelling with an agent based and micro-simulation based framework. In: 2015 17th International Conference on Advanced Communication Technology (ICACT), pp. 495–501. IEEE (2015)Google Scholar
  39. 39.
    Spapens, T.: Macro networks, collectives, and business processes: an integrated approach to organized crime. Eur. J. Crime Crim. L. Crim. Just. 18, 185 (2010)CrossRefGoogle Scholar
  40. 40.
    Vecht, B., Barros, A., Boltjes, B., Keijser, B., de Reus, N.: A multi-methodology framework for modelling opponent organisations in the operational context. In: Proceedings 11th NATO Operations Research & Analysis Conference, pp. 6.21–6.2.20. NATO (2017)Google Scholar
  41. 41.
    Von Lampe, K., Ole Johansen, P.: Organized crime and trust: on the conceptualization and empirical relevance of trust in the context of criminal networks. Glob. Crime 6(2), 159–184 (2004)CrossRefGoogle Scholar
  42. 42.
    Walker, J.T.: Advancing science and research in criminal justice/criminology: complex systems theory and non-linear analyses. Justice Q. 24(4), 555–581 (2007)CrossRefGoogle Scholar
  43. 43.
    Windisch, S., Simi, P., Ligon, G.S., McNeel, H.: Disengagement from ideologically-based and violent organizations: a systematic review of the literature. J. Deradicalization (9), 1–38 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Koen van der Zwet
    • 1
    • 2
    • 4
    Email author
  • Ana Isabel Barros
    • 2
    • 4
  • Tom M. van Engers
    • 1
    • 2
    • 3
  • Bob van der Vecht
    • 4
  1. 1.Science FacultyUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.Institute for Advanced StudyAmsterdamThe Netherlands
  3. 3.Leibniz CenterUniversity of AmsterdamAmsterdamThe Netherlands
  4. 4.TNO Defence, Safety and SecurityThe HagueThe Netherlands

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