, Volume 169, Issue 3, pp 593–613 | Cite as

The philosophy of simulation: hot new issues or same old stew?

  • Roman FriggEmail author
  • Julian Reiss


Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science, but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before.


Simulation Models Computer experiments Representation Epistemology of simulation 


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  1. Black M. (1962) Models and archetypes. Models and metaphors: Studies in language and philosophy. Cornell University Press, Ithaca, NY, pp 219–243Google Scholar
  2. Boumans M. (1999) Built-in justification. In: Morgan M., Morrison M. (eds) Models as mediators. CUP, Cambridge, pp 66–96Google Scholar
  3. Cartwright N. (1983) How the laws of physics lie. Oxford University Press, OxfordCrossRefGoogle Scholar
  4. Cartwright N. (1999) The dappled world. CUP, CambridgeGoogle Scholar
  5. Cartwright N. (2007) The vanity of rigour in economics: Theoretical models and Galileian experiments. Hunting causes and using them. Cambridge University Press, Cambridge, pp 217–235Google Scholar
  6. Edwards P. (2001) Representing the global atmosphere: Computer models, data, and knowledge about climate change. In: Miller C., Edwards P. (eds) Changing the atmosphere: Expert knowledge and environmental governance. MIT Press, Cambridge, MA, pp 31–66Google Scholar
  7. Fox Keller E. (2003) Models, simulation, and ’computer experiments.’ In: Radder H. (eds). The philosophy of scientific experimentation. University of Pittsburgh Press, Pittsburgh, pp 198–216Google Scholar
  8. Frigg A., Frigg R., Hintermann B., Barg A., Valderrabano V. (2007) The biomechanical influence of tibio-talar containment on stability of the ankle joint. Journal of Knee Surgery, Sports Traumatology and Arthroscopy 15: 1355–1362CrossRefGoogle Scholar
  9. Frigg, R., & Hartmann, S. (2006, Spring). Models in science. In E. Zalta (Ed.), Stanford Encyclopedia of philosophy. Downloadable at:
  10. Galison P. (1996) Computer simulation and the trading zone. In: Galison P., Stump D. (eds) Disunity of science: Boundaries, contexts, and power. Stanford University Press, California, pp 118–157Google Scholar
  11. Guala F. (1998) Experiments as mediators in the non-laboratory sciences. Philosophica 62: 901–918Google Scholar
  12. Guala F. (2005) The methodology of experimental economics. Cambridge University Press, CambridgeGoogle Scholar
  13. Hartley J., Hoover K., Salyer K. (1997) The limits of business cycle research: Assessing the real business cycle model. Oxford Review of Economic Policy 13(3): 34–54CrossRefGoogle Scholar
  14. Hartmann S. (1996) The world as a process: Simulation in the natural and social sciences. In: Hegselmann R., Müller U., Troitzsch K. (eds) Modelling and simulation in the social sciences from the philosophy of science point of view. Kluwer, Dordrecht, pp 77–100Google Scholar
  15. Humphreys P. (1991) Computer simulations. Philosophy of Science PSA 19902: 497–506Google Scholar
  16. Humphreys P. (1993) Numerical experimentation. In: Humphreys P. (eds) Patrick Suppes: Scientific philosopher (Vol 2). Kluwer, DordrechtGoogle Scholar
  17. Humphreys P. (1995) Computational science and scientific method. Mind and Machines 5: 499–512CrossRefGoogle Scholar
  18. Humphreys P. (2004) Extending ourselves: Computational science, empiricism, and scientific method. OUP, OxfordGoogle Scholar
  19. Latour B. (1988) The pasteurisation of France. Harvard University Press, Cambridge, MAGoogle Scholar
  20. Little D., (eds) (1995) On the reliability of economic models: Essays in the philosophy of economics. Kluwer, DordrechtGoogle Scholar
  21. Lucas R. (1982) Studies in business cycle theory. MIT Press, Cambridge, MAGoogle Scholar
  22. Mäki U. (2005) Models are experiments, experiments are models. Journal of Economic Methodology 12(2): 303–315CrossRefGoogle Scholar
  23. Morgan M. (2003) Experiments without material intervention: Model experiments, virtual experiments, and virtually experiment. In: Radder H. (eds) The philosophy of scientific experimentation. University of Pittsburgh Press, Pittsburgh, PA, pp 216–235Google Scholar
  24. Morgan M. (2004) Simulation: The birth of a technology to create “evidence” in economics. Revue d’Histoire des Sciences 57: 341–377Google Scholar
  25. Morgan M. (2005) Experiments versus models: New phenomena, inference and surprise. Journal of Economic Methodology 12: 317–329CrossRefGoogle Scholar
  26. Morgan M., Morrison M. (1999) Models as mediating instruments. Models as mediators: Perspectives on natural and social science. Cambridge University Press, Cambridge, pp 10–37Google Scholar
  27. Morgan M., Morrison M. (1999) Models as mediators: Perspectives on natural and social science. Cambridge University Press, CambridgeGoogle Scholar
  28. Morton A. (1993) Mathematical models: Questions of trustworthiness. British Journal for the Philosophy of Science 44: 659–674CrossRefGoogle Scholar
  29. Norton S., Suppe F. (2000) Why atmospheric modeling is good science. In: Miller C., Edwards P. (eds) Changing the atmosphere: Expert knowledge and environmental governance. MIT Press, Cambridge, MAGoogle Scholar
  30. Reiss J. (2007) Error in economics: Towards a more evidence-based methodology. Routledge, LondonGoogle Scholar
  31. Rohrlich, F. (1991). Computer simulation in the physical sciences. PSA 1990, II, 507–518.Google Scholar
  32. Sismondo S. (1999) Models, simulations and their objects. Science in Context 12: 247–260CrossRefGoogle Scholar
  33. Smith P. (1998) Explaining chaos. Cambridge University Press, CambridgeGoogle Scholar
  34. Sorensen R. (1992) Thought experiments. Oxford University Press, OxfordGoogle Scholar
  35. Srivastava N., Kaufman C., Müller G. (1990) Hamiltonian chaos. Computers in Physics 4(5): 549–553Google Scholar
  36. Sterman J. (2006) Learning from evidence in a complex world. American Journal of Public Health 96(3): 505–514CrossRefGoogle Scholar
  37. Stöckler M. (2000) On modelling and simulations as instruments for the study of complex systems. In: Carrier M. (eds) Science at century’s end: Philosophical questions on the progress and limits of science. University of Pittsburgh Press, PittsburghGoogle Scholar
  38. Sugden R. (2000) Credible worlds: The status of theoretical models in economics. Journal of Economic Methodology 7(1): 1–31CrossRefGoogle Scholar
  39. Winsberg, E. (1999, Summer). Sanctioning models: The epistemology of simulation. Science in Context.Google Scholar
  40. Winsberg, E. (2001). Simulations, models, and theories: Complex physical systems and their representations. Philosophy of Science, 68(Proceedings), S442–S454.Google Scholar
  41. Winsberg E. (2003) Simulated experiments: Methodology for a virtual world. Philosophy of Science 70: 105–125CrossRefGoogle Scholar

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© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Department of PhilosophyLondon School of EconomicsLondonUK

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