Pitfalls in the Development of Agent-Based Models in Social Sciences: Avoiding Them and Learning from Them

  • Carlos M. LemosEmail author
Part of the New Approaches to the Scientific Study of Religion book series (NASR, volume 7)


The references on the principles and methodology for developing agent-based models of social phenomena usually describe general principles and illustrate the process using worked examples, but seldom focus on the pitfalls and errors that make practical model building a tortuous and difficult task. This chapter contains a discussion of the positive and negative aspects of my personal experience in a PhD work on simulation of large scale social conflict. The purpose will be to describe the process from the initial plan to the final dissertation, analyze the pitfalls and their overcoming in terms of principles of model development, and summarize the ideas that I found useful for practical development of agent-based models of social phenomena. The most serious pitfalls usually occur at the conception and design stages, when seemingly trivial points can be easily overlooked. These include starting with excessive ambition but unclear ideas on whether the purpose is understanding or prediction (i.e. what is the level of abstraction), poor knowledge of the relevant theories, and failure to identify which entities, variables and mechanisms must be considered. Several practical hints for avoiding these issues are presented, such as writing a reduced version of the “Overview, Design Concepts and Details” template that includes the bare minimum of items for a first working version, and devising efficient strategies for exploring the parameter space. This chapter will be of interest to MSc and PhD students working on social simulation, and to researchers developing projects on agent-based modeling of social phenomena, either individually or in teamwork.


Social conflict Agent-based modeling Arab Spring Model development Validation Pitfalls Hints Practical ideas 



Funding by the Research Council of Norway (grant #250449) is gratefully acknowledged. I also wish to acknowledge the comments of three reviewers, which contributed significantly to the improvement of the manuscript.


  1. Barash, V. 2011. The dynamics of social contagion. PhD thesis, Faculty of the Graduate School.Google Scholar
  2. Bischof, D. 2012. Why arabs rebel – Relative deprivation revisited. Master’s thesis, Fakultät Sozial und Wirtschaftswissenschaften der Otto-Friedrich-Universität Bamberg.Google Scholar
  3. Cioffi-Revilla, C. 2017. Introduction to computational social science: Principles and applications, Texts in computer science, 2nd ed. Cham: Springer.CrossRefGoogle Scholar
  4. Collins, R. 2008. Violence. A micro-sociological theory. Princeton: Princeton University Press.CrossRefGoogle Scholar
  5. Collins, R. 2009. Micro and macro causes of violence. International Journal of Conflict and Violence 3(1): 9–22.Google Scholar
  6. Dodds, P., and D. Watts. 2005. A generalized model of social and biological contagion. Journal of Theoretical Biology 232: 587–604.CrossRefGoogle Scholar
  7. Dollard, J., L.W. Doob, N.E. Miller, O.H. Mowrer, and R.R. Sears. 1939. Frustration and aggression. New Haven: Yale University Press.CrossRefGoogle Scholar
  8. Doran, J. 2005. Iruba: An agent-based model of the Guerrilla war process. In Representing social reality, Volume pre-proceedings of the third conference of the European social simulation association (ESSA), Koblenz, ed. K.G. Troitzsch, 198–205. European Social Simulation Association. Koblenz: Germany.Google Scholar
  9. Epstein, J.M. 2002. Modeling civil violence: An agent-based computational approach. Proceedings of the National Academy of Sciences of the United States of America 99: 7243–7250.CrossRefGoogle Scholar
  10. Epstein, J.M. 2008. Why model? Journal of Artificial Societies and Social Simulation 11(4): 12. Scholar
  11. Epstein, J.M. 2013. Agent_zero. Toward neurocognitive foundations for generative social science. Princeton: Princeton University Press.Google Scholar
  12. Epstein, J.M., J.D. Steinbruner, and M.T. Parker. 2001. Modeling civil violence: An agent-based computational approach. Center on Social and Economic Dynamics, Working Paper No. 20, Jan 2001.Google Scholar
  13. Fonoberova, M., V.A. Fonoberov, I. Mezic, J. Mezic, and P.J. Brantingham. 2012. Nonlinear dynamics of crime and violence in urban settings. Journal of Artificial Societies and Social Simulation 15(1): 2.CrossRefGoogle Scholar
  14. Freedom House. 2015. Freedom in the world, individual country ratings and status. Accessed 13 July 2015.Google Scholar
  15. Gilbert, N. 2007. Agent-based models (Quantitative applications in the social sciences). Califormia: Thousand Oaks.Google Scholar
  16. Gilbert, N., and K.G. Troitzsch. 2005. Simulation for the social scientist, 2nd ed. New York: Open University Press.Google Scholar
  17. Gilley, B. 2006. The meaning and measure of state legitimacy: Results for 72 countries. European Journal of Political Science 45: 499–525.CrossRefGoogle Scholar
  18. Gilley, B. 2009. The right to rule. How states win and lose legitimacy. New York: Columbia University Press.Google Scholar
  19. Grimm, V., U. Bergern, D.L. DeAngelis, J.G. Polhill, J. Giskee, and S.F. Railsback. 2010. The ODD protocol: A review and first update. Ecological Modelling 221(221): 2760–2768.CrossRefGoogle Scholar
  20. Gurr, T.R. 1968. Psychological factors in civil violence. World Politics 20(2): 245–278.CrossRefGoogle Scholar
  21. Gurr, T.R. 2011. Why men rebel, Anniversary Edition. London: Paradigm Publishers.Google Scholar
  22. Hamill, J.T. 2012. Analysis of layered social networks. BiblioScholar. United States.Google Scholar
  23. Ilachinsky, A. 2004. Artificial war: Multiagent-based simulation of combat. River Edge: World Scientific Publishing Co. Pte. Ltd.CrossRefGoogle Scholar
  24. Jackson, M.O. 2010. Social and economic networks. Princeton: New Jersey.CrossRefGoogle Scholar
  25. Jager, W., R. Popping, and H. van de Sande. 2001. Clustering and fighting in two-party crowds: Simulating the approach-avoidance conflict. Journal of Artificial Societies and Social Simulation 4(3).
  26. Klandermans, B. 1997. The social psychology of protest. Cambridge: Massachusetts.Google Scholar
  27. Lemos, C.M. 2016. On agent-based modelling of large scale conflict against a central authority: From mechanisms to complex behaviour. PhD thesis, ISCTE – University Institute of Lisbon and Faculty of Sciences of the University of Lisbon.Google Scholar
  28. Lemos, C.M., H. Coelho, and R.J. Lopes. 2017. ProtestLab: A computational laboratory for studying street protests. In Advances in complex societal, environmental and engineered systems, Nonlinear systems and complexity, vol. 18, 3–29. Cham: Springer.CrossRefGoogle Scholar
  29. Lopes, Rui Jorge and Luis Antunes (Cord). 2017. International MSc and PhD Programs in Complexity Sciences. Accessed 2 Dec 2017.Google Scholar
  30. Lorenz, K. 2002. On aggression. London/New York: Routledge Classics.Google Scholar
  31. Macal, C.M., and M.J. North. 2010. Tutorial on agent-based modelling and simulation. Journal of Simulation 4(3): 151–162.CrossRefGoogle Scholar
  32. Milanovic, B. 2014. Description of “All the Ginis” Dataset Oct. 2014. The World Bank: Washington, DC.Google Scholar
  33. Miller, J.H., and S.L. Page. 2007. Complex adaptive systems. Princeton: Princeton University Press.Google Scholar
  34. Mobus, G.E., and M.C. Kalton. 2015. Principles of systems science. Springer: New York.CrossRefGoogle Scholar
  35. Moro, A. 2016. Understanding the dynamics of violent political revolutions in an agent-based framework. PLoS ONE 11(4): 1–17.CrossRefGoogle Scholar
  36. North, M.J., N.T. Collier, and J.R. Vos. 2006. Experiences creating three implementations of the repast agent modeling toolkit. ACM Transactions on Modeling and Computer Simulation 16(1): 1–25.CrossRefGoogle Scholar
  37. Reicher, S. 2001. The psychology of crowd dynamics. In Blackwell handbook of social psychology: Group processes, 182–208. Malden: Blackwell Publishing.Google Scholar
  38. Rummel, R.W. 1976. Understanding conflict and war volume 2: The conflict helix. Beverly Hills: SAGE Publications.Google Scholar
  39. Runciman, W.G. 1972. Relative deprivation and social justice. A study of attitudes to social inequality in twentieth century England. Harmondsworth: Penguin Books Ltd.Google Scholar
  40. Sayama, H. 2015. Introduction to the modeling and analysis of complex systems. Geneseo: New York.Google Scholar
  41. Sharp, G. 2010. From dictatorship to democracy, 4th ed. East Boston, MA: USA.Google Scholar
  42. Siegfried, R. 2014. Modeling and simulation of complex systems. A framework for efficient agent-based modeling and simulation. Wiesbaden: Springer.Google Scholar
  43. Squazzoni, F. 2012. Agent-based computational sociology. Hoboken: Wiley.CrossRefGoogle Scholar
  44. Railsback, Steven F., and Volker Grimm. 2011. Agent-based and individual-based modeling: A practical introduction. Princeton: Princeton University Press.Google Scholar
  45. The Fund for Peace. 2015. Fragile states index. Accessed 9 Nov 2015.
  46. The Robert S. Strauss Center. 2015. Social conflict analysis database. Accessed 25 July 2015.
  47. Thiele, J.C. 2014. R Marries NetLogo: Introduction to the RNetLogo package. Journal of Statistical Software 58(2): 1–41.CrossRefGoogle Scholar
  48. Torrens, P.M., and A.W. McDaniel. 2013. Modeling geographic behavior in riotous crowds. Annals of the Association of American Geographers 103(1): 20–46.CrossRefGoogle Scholar
  49. Wilensky, Uri, and William Rand. 2015. An introduction to agent-based modeling. Modeling natural, social, and engineered complex systems with NetLogo. Cambridge: The MIT Press.Google Scholar
  50. Wikström, P.-O.H., and K.H. Treiber. 2009. Violence as situational action. International Journal of Conflict and Violence 3(1): 75–96.Google Scholar
  51. Wilensky, U. 1999. NetLogo. Technical report, Center for Connected Learning and Computer-Based Modeling. Evanston: Northwestern University.Google Scholar
  52. Wilensky, U. 2004. NetLogo rebellion model. Evanston: Northwestern University.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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