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Part of the book series: Philosophy of Engineering and Technology ((POET,volume 18))

Abstract

Computer simulations are everywhere in science today, thanks to ever increasing computer power. By discussing similarities and differences with experimentation and theorizing, the two traditional pillars of scientific activities, this paper will investigate what exactly is specific and new about them. From an ontological point of view, where do simulations lie on this traditional theory-experiment map? Do simulations also produce measurements? How are the results of a simulation deem reliable? In light of these epistemological discussions, the paper will offer a requalification of the type of knowledge produced by simulation enterprises, emphasizing its modal character: simulations do produce useful knowledge about our world to the extent that they tell us what could be or could have been the case, if not knowledge about what is or was actually the case. The paper will also investigate to what extent technological progress in computer power, by promoting the building of increasingly detailed simulations of real-world phenomena, shapes the very aims of science.

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Notes

  1. 1.

    See Humphreys (2009) and Reiss and Frigg (2009) for discussions of the second kind of assertions.

  2. 2.

    See also Barberousse et al. (2009) and Norton and Suppe (2001).

  3. 3.

    This section (and the following) directly draws on Ruphy (2011).

  4. 4.

    My discussion is based on an analysis of the Millennium run, a cosmological simulation run in 2005 (Springel et al. 2005), but similar lessons could be drawn from more recent ones such as the project DEUS: full universe run (see www.deus-consortium.org). Accessed 22 June 2013.

  5. 5.

    See for instance Epstein and Forber (2013) for an interesting analysis of the perils of using macrodata to set parameters in a microfoundational simulation.

  6. 6.

    This is the case for instance for the astrophysical and cosmological simulations discussed in Ruphy (2011) and for the Earth climate simulations analyzed in Lenhard and Winsberg (2010).

  7. 7.

    See the exchange on this topic between Frigg and Reiss (2009) and Humphreys (2009).

  8. 8.

    I borrow this quotation from Grim et al. (2013), which, in another framework, also discusses the modal character of the knowledge produced by simulation.

  9. 9.

    As attested by the fact that the HBP project will dedicate some funds to the creation of a European Institute for Theoretical Neuroscience.

  10. 10.

    I draw here on documents provided by the Human Brain Project at www.humanbrainproject.eu. Accessed 25 June 2013.

References

  • Barberousse, A., Franceschelli, S., & Imbert, C. (2009). Computer simulations as experiments. Synthese, 169, 557–574.

    Article  Google Scholar 

  • Dowling, D. (1999). Experimenting on theories. Science in Context, 12(2), 261–273.

    Article  Google Scholar 

  • Ellis, G. (2006). Issues in the philosophy of cosmology. http://arxiv.org/abs/astro-ph/0602280. (Reprinted in the Handbook in Philosophy of Physics, pp. 1183–1286, by J. Butterfield & J. Earman, Ed., 2007, Amsterdam: Elsevier)

  • Epstein, B., & Forber, P. (2013). The perils of tweaking: How to use macrodata to set parameters in complex simulation models. Synthese, 190, 203–218.

    Article  Google Scholar 

  • Franklin, A. (1986). The neglect of experiment. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Frigg, R., & Reiss, J. (2009). The philosophy of simulations: Hot new issues or same old stew? Synthese, 169, 593–613.

    Article  Google Scholar 

  • Giere, R. N. (2009). Is computer simulation changing the face of experimentation? Philosophical Studies, 143, 59–62.

    Article  Google Scholar 

  • Godfrey-Smith, P. (2009). Models and fictions in science. Philosophical Studies, 143, 101–126.

    Article  Google Scholar 

  • Grim, P., Rosenberger, R., Rosenfeld, A., Anderson, B., & Eason, R. E. (2013). How simulations fail. Synthese, 190, 2367–2390.

    Article  Google Scholar 

  • Guala, F. (2005). The methodology of experimental economics. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Humphreys, P. (2004). Extending ourselves. New York: Oxford University Press.

    Book  Google Scholar 

  • Humphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese, 169, 615–626.

    Article  Google Scholar 

  • Lenhard, J., & Winsberg, E. (2010). Holism, entrenchment, and the future of climate model pluralism. Studies in History and Philosophy of Modern Physics, 41, 253–262.

    Article  Google Scholar 

  • Morgan, M. (2005). Experiments versus models: New phenomena, inference, and surprise. Journal of Economic Methodology, 12(2), 317–329.

    Article  Google Scholar 

  • Morrison, M. (2009). Models, measurement and computer simulation: The changing face of experimentation. Philosophical Studies, 143, 33–57.

    Article  Google Scholar 

  • Norton, S., & Suppe, F. (2001). Why atmospheric modeling is good science. In C. Miller & P. N. Edward (Eds.), Changing the atmosphere: Expert knowledge and environmental governance (pp. 67–105). Cambridge: MIT Press.

    Google Scholar 

  • Paola, C. (2011). Simplicity versus complexity. Nature, 469, 38.

    Article  Google Scholar 

  • Parker, W. (2009). Does matter really matter? Computer simulations, experiments, and materiality. Synthese, 169, 483–496.

    Article  Google Scholar 

  • Ruphy, S. (2011). Limits to modeling: Balancing ambition and outcome in astrophysics and cosmology. Simulation and Gaming, 42, 177–194.

    Article  Google Scholar 

  • Schweber, S., & Wächter, M. (2000). Complex systems, modelling and simulation. Studies in History and Philosophy of Science Part B: History and Philosophy of Modern Physics, 31, 583–609.

    Article  Google Scholar 

  • Smith, J. M. (1995). Life at the edge of chaos? New York Review of Books, 42(4), 28–30.

    Google Scholar 

  • Springel, V., et al. (2005). Simulations of the formation, evolution and clustering of galaxies and quasars. Nature, 435, 629–636.

    Article  Google Scholar 

  • Winsberg, E. (2013). Simulated experiments: Methodology from a virtual world. Philosophy of Science, 70, 105–125.

    Article  Google Scholar 

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Correspondence to Stéphanie Ruphy .

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Ruphy, S. (2015). Computer Simulations: A New Mode of Scientific Inquiry?. In: Hansson, S. (eds) The Role of Technology in Science: Philosophical Perspectives. Philosophy of Engineering and Technology, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9762-7_7

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