Different Modelling Purposes

  • Bruce EdmondsEmail author
Part of the Understanding Complex Systems book series (UCS)


How one builds, checks, validates and interprets a model depends on its ‘purpose’. This is true even if the same model is used for different purposes, which means that a model built for one purpose but now used for another may need to be rechecked, revalidated and maybe even rebuilt in a different way. Here we review some of the different purposes for building a simulation model of complex social phenomena, focussing on five in particular: theoretical exposition, prediction, explanation, description and illustration. The chapter looks at some of the implications in terms of the ways in which the intended purpose might fail. In particular, it looks at the ways that a confusion of modelling purposes can fatally weaken modelling projects, whilst giving a false sense of their quality. This analysis motivates some of the ways in which these ‘dangers’ might be avoided or mitigated.


Analogy Assumptions Bounding outcomes Calibration Data collection Education Explanation Generative explanation Guiding data collection Perturbation Plausibility Policy options Prediction Qualitative behaviour Question discovery Robustness Science Tradeoffs Training Understanding Validation 



Many thanks to all those with whom I have discussed these matters, including Scott Moss, David Hales, Bridget Rosewell and all those who attended the workshop on validation held in Manchester.


  1. Axelrod, R. (1984). The evolution of cooperation. New York, NY: Basic Books.zbMATHGoogle Scholar
  2. Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation. Science, 211, 1390–1396.MathSciNetCrossRefzbMATHGoogle Scholar
  3. Cartwright, N. (1983). How the laws of physics lie. Oxford: Oxford University Press.CrossRefGoogle Scholar
  4. Cohen, P. R. (1984a). Heuristic reasoning about uncertainty: an artificial intelligence approach. International Journal of Approximate Reasoning, 1(2), 243–245.Google Scholar
  5. Cohen, P. R. (1984b). Heuristic reasoning about uncertainty: an artificial intelligence approach. Marshfield, MA: Pitman Publishing.Google Scholar
  6. Edmonds, B. (2001). The use of models - making MABS actually work. In S. Moss & P. Davidsson (Eds.), Multi agent based simulation, Lecture notes in artificial intelligence (Vol. 1979, pp. 15–32). Berlin: Springer-Verlag.CrossRefGoogle Scholar
  7. Edmonds, B. (2010). Bootstrapping knowledge about social phenomena using simulation models. Journal of Artificial Societies and Social Simulation, 13(1), 8. MathSciNetCrossRefGoogle Scholar
  8. Edmonds, B., Lucas, P., Rouchier, J., & Taylor, R. (2017). Understanding human societies. doi:
  9. Epstein, J. M. (2008). Why model? Journal of Artificial Societies and Social Simulation, 11(4), 12. Google Scholar
  10. Galán, J. M., Izquierdo, L. R., Izquierdo, S. S., Santos, J. I., del Olmo, R., & López-Paredes, A. (2017a). Checking simulations: Detecting and avoiding errors and artefacts. doi:
  11. Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., et al. (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198, 115–126.CrossRefGoogle Scholar
  12. Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback, S. F. (2010). The ODD protocol: A review and first update. Ecological Modelling, 221, 2760–2768.CrossRefGoogle Scholar
  13. Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago, IL: University of Chicago Press.Google Scholar
  14. Lansing, J. S., & Kramer, J. N. (1993). Emergent properties of balinese water temple networks: coadaptation on a rugged fitness landscape. American Anthropologist, 1, 97–114.CrossRefGoogle Scholar
  15. Meese, R. A., & Rogoff, K. (1983). Empirical exchange rate models of the seventies - Do they fit out of sample? Journal of International Economics, 14, 3–24.CrossRefGoogle Scholar
  16. Moss, S. (1998). Critical incident management: An empirically derived computational model. Journal of Artificial Societies and Social Simulation, 1(4), 1. Google Scholar
  17. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  18. Norling, E., Meyer, R., & Edmonds, B. (2017). Informal approaches to developing simulations. doi:
  19. Schelling, T. C. (1969). Models of segregation. The American Economic Review, 59(2), 488–493.Google Scholar
  20. Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143–186.CrossRefzbMATHGoogle Scholar
  21. Silver, N. (2012). The signal and the noise: the art and science of prediction. London: Penguin.Google Scholar
  22. Thorngate, W., & Edmonds, B. (2013). Measuring simulation-observation fit: An introduction to ordinal pattern analysis. Journal of Artificial Societies and Social Simulation, 16(2), 14. CrossRefGoogle Scholar
  23. Watts, D. J. (2014). Common sense and sociological explanations. American Journal of Sociology, 120(2), 313–351.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Centre for Policy Modelling, Manchester Metropolitan UniversityManchesterUK

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