Trends in Organized Crime

, Volume 20, Issue 1–2, pp 85–99 | Cite as

GLODERS-S: a simulator for agent-based models of criminal organisations

  • Luis Gustavo Nardin
  • Áron Székely
  • Giulia Andrighetto


Computer simulation has recently been recognised by criminologists as a useful tool for bridging the gap between theoretical and empirical analyses of organised crime and for supplementing their weaknesses. GLODERS-S is an innovative and configurable agent-based simulator specialised in reproducing the dynamics of a specific type of criminal organisations: protection racketeering groups. The simulator adopts an event-based approach that provides a more realistic operation of the agents, which integrated with its configurability provides policy-makers with a highly flexible platform for analysing multiple scenarios and assessing policies to counter organised crime. In this paper, we describe the principles of the simulator design, its features and limitations, and possible applications.


GLODERS-S Protection racketeering Criminal organisations Agent-based simulation 


Compliance with Ethical Standards

Conflict of interest

The authors declare they have no conflict of interest.


This study was partially funded by the FP7-ICT Science of Global Systems programme of the European Commission (grant number 315,874).

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


  1. Albin P, Foley DK (1992) Decentralized, dispersed exchange without an auctioneer: A simulation study. J Econ Behav Organ 18(1):27–51. doi: 10.1016/0167–2681(92)90051-C CrossRefGoogle Scholar
  2. Andrighetto G, Brandts J, Conte R, Sabater-Mir J, Solaz H, Villatoro D (2013) Punish and voice: Punishment enhances cooperation when combined with norm-signalling. PLoS One 8(6):e64941. doi: 10.1371/journal.pone.0064941 CrossRefGoogle Scholar
  3. Andrighetto, G., Nardin, L. G., Lotzmann, U., & Neumann, M. (2014). D3.1 Report on adaptations made to the EMIL simulator. Retrieved from
  4. Axelrod R (1984) The evolution of cooperation. New York. Basic Books, NYGoogle Scholar
  5. Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci U S A 99(3):7280–7287CrossRefGoogle Scholar
  6. Conte R, Hegselmann R, Terna P (eds) (1997) Simulating social phenomena. Springer, BerlinGoogle Scholar
  7. Conte R, Andrighetto G, Campennì M (eds) (2013) Minding norms: Mechanisms and dynamics of social order in agent societies. Oxford University Press, OxfordGoogle Scholar
  8. Davidsson, P. (2002). Agent based social simulation: a computer science view. J Artif Soc Soc Simulat, 5(1), 7Google Scholar
  9. Dray A, Mazerolle L, Perez P, Ritter A (2008) Drug law enforcement in an agent-based model: Simulating the disruption to street-level drug markets. In: Liu L, Eck JE (eds) Artificial crime analysis systems: Using computer simulations and geographic information systems. Hershey, PA, IGI GlobalGoogle Scholar
  10. Eck JE, Liu L (2008) Contrasting simulated and empirical experiments in crime prevention. J Exp Criminol 4:195–213. doi: 10.1007/s11292–008-9059-z CrossRefGoogle Scholar
  11. Epstein JM, Axtell R (1996) Growing artificial societies: Social science from the bottom up. Brookings Institute Press, Washington, D.C.Google Scholar
  12. Farmer JD, Foley D (2009) The economy needs agent-based modelling. Nature 460:685–686. doi: 10.1038/460685a CrossRefGoogle Scholar
  13. Gambetta D (1993) The Sicilian mafia: The business of private protection. Harvard University, Cambridge, MAGoogle Scholar
  14. Gerritsen C (2015) Agent-based modelling as a research tool for criminological research. Crime. Science 4(2):1–12Google Scholar
  15. Gilbert N (2007) Agent-based models. SAGE Publications, LondonGoogle Scholar
  16. Gilbert N, Conte R (eds) (1995) Artificial societies: The computer simulation of social life. UCL Press, LondonGoogle Scholar
  17. Groff E, Mazerolle L (2008) Simulated experiments and their potential in criminology and criminal justice. J Exp Criminol 4:187–193. doi: 10.1007/s11292–008–9058-0 CrossRefGoogle Scholar
  18. Hill PBE (2006) The Japanese mafia: yakuza, law, and the state. Oxford University Press, OxfordGoogle Scholar
  19. Leech G (2009) Beyond Bogotá. Beacon Press, Boston, MAGoogle Scholar
  20. Li X, Mao W, Zeng D, Wang F-Y (2008) Agent-based social simulation and modeling in social computing In Intelligence and Security Informatics (Vol 5075, pp 401–412). Springer-Verlag, BerlinGoogle Scholar
  21. Liu L, Eck JE (2008) Artificial crime analysis systems: Using computer simulations and geographic information systems. IGI Global, Hershey, PACrossRefGoogle Scholar
  22. Liu F, Enanoria WT, Zipprich J, Blumberg S, Harriman K, Ackley SF, et al. (2015) The role of vaccination coverage, individual behaviors, and the public health response in the control of measles epidemics: an agent-based simulation for California. BMC Public Health 15(1). doi: 10.1186/s12889–015–1766-6
  23. Luke S, Cioffi-Revilla C, Panait L, Sullivan K, Balan G (2005) MASON: A multi-agent simulation environment. Simulat: Transac Soc Model Simulat Intern 82(7):517–527CrossRefGoogle Scholar
  24. Malleson N (2012) Using agent-based models to simulate crime. In: Heppenstall AJ, Crooks AT, See LM, Batty M (eds) Agent-Based Models of Geographical Systems. Springer, Berlin, pp. 411–434CrossRefGoogle Scholar
  25. Malleson N, Birkin M (2012) Analysis of crime patterns through the integration of an agent-based model and a population microsimulation. Comput Environ Urban Syst 36(6):551–561CrossRefGoogle Scholar
  26. Malleson N, Evans A (2013) Agent-based models to predict crime at places. In: Bruinsma G, Weisburd D (eds) Encyclopedia of Criminology and Criminal Justice. Springer-Verlag, New York, NY, pp. 243–252Google Scholar
  27. Malleson N, Evans A, Jenkins T (2009) An agent-based model of Burglary. Environ Plann B: Planning Design 36:1103–1123CrossRefGoogle Scholar
  28. Malleson N, Heppenstall A, See L (2010) Crime reduction through simulation: An agent-based model of burglary. Comput Environ Urban Syst 31(3):236–250CrossRefGoogle Scholar
  29. Malleson N, Heppenstall A, See L (2013) Using an agent-based crime simulation to predict the effects of urban regeneration on individual household burglary risk. Environ Planning B: Planning Design 40:405–426CrossRefGoogle Scholar
  30. Marathe A, Lewis B, Barrett C, Chen J, Marathe M, Eubank S, Ma Y (2011) Comparing effectiveness of top-down and bottom-up strategies in containing influenza. PLoS One 6(9):e25149. doi: 10.1371/journal.pone.0025149 CrossRefGoogle Scholar
  31. Melo A, Belchior M, Furtado V (2006) Analyzing police patrol routes by simulating the physical reorganization of agents. In Multi-Agent-Based Simulation VI. Springer, BerlinGoogle Scholar
  32. Militello, V., La Spina, A., Frazzica, G., Punzo, V., & Scaglione, A. (2014). D1.1 Quali-quantitative summary of data on extortion rackets in Sicily. Retrieved from
  33. Morgan WP (1960) Triad societies in Hong Kong. The Government Printer, Hong KongGoogle Scholar
  34. Nardin, L. G., Andrighetto, G., Székely, Á., & Troitzsch, K. G. (2015). D3.4 Simulator GLODERS-S. Retrieved from
  35. Nardin, L. G., Andrighetto, G., Conte, R., Székely, Á., Anzola, D., Elsenbroich, C., … Troitzsch, K. G. (2016a). Simulating protection rackets: A case study of the Sicilian Mafia. J Autonomous Agents Multi-Agent Syst, 1–31. doi:10.1007/s10458–016-9330-zGoogle Scholar
  36. Nardin LG, Andrighetto G, Székely Á, Conte R (2016b) Modelling extortion racket systems: Preliminary results. In: Cecconi F (ed) New Frontiers in the Study of Social Phenomena: Cognition, Complexity, Adaptation. Springer, BerlinGoogle Scholar
  37. North, M. J., Collier, N. T., Ozik, J., Tatara, E. R., Macal, C. M., Bragen, M., & Sydelko, P. (2013). Complex adaptive systems modeling with Repast Simphony. Comp Adapt Syst Model, 1(3), 1–26. doi:10.1186/2194–3206–1-3Google Scholar
  38. Schelling TC (1978) Micromotives and macrobehavior. New York. Norton, NYGoogle Scholar
  39. Siegel D (2008) Conversations with Russian mafiosi. Trends Organized Crime 11(1):21–29CrossRefGoogle Scholar
  40. Troitzsch KG (2015) Distribution effects of extortion racket systems. In: Amblard F, Miguel FJ, Blanchet A, Gaudou B (eds) Advances in Artificial Economics, vol 676. Springer, Switzerland, pp. 181–193Google Scholar
  41. Varese F (1996) What is the Russian mafia? Low Intensity Conflict and Law Enforcement 5(2):129–138Google Scholar
  42. Varese F (2001) The Russian mafia: private protection in a new market economy. Oxford University Press, OxfordCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Italian National Research CouncilInstitute of Cognitive Science and TechnologyRomeItaly
  2. 2.Center for Modeling Complex InteractionsUniversity of IdahoMoscowUSA
  3. 3.Department of Political and Social SciencesEuropean University InstituteFiesoleItaly

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