, Volume 180, Issue 1, pp 19–32 | Cite as

Modeling reality



My aim in this paper is to articulate an account of scientific modeling that reconciles pluralism about modeling with a modest form of scientific realism. The central claim of this approach is that the models of a given physical phenomenon can present different aspects of the phenomenon. This allows us, in certain special circumstances, to be confident that we are capturing genuine features of the world, even when our modeling occurs independently of a wholly theoretical motivation. This framework is illustrated using a recent debate from meteorology.


Realism Idealization Models Representation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Batterman, R. (2002). The devil in the details: Asymptotic reasoning in explanation, reduction, and emergence. New York: Oxford University Press.Google Scholar
  2. Butterfield, J. (2006). Against pointillisme about mechanics. British Journal for the Philosophy of Science, 57, 709–753.CrossRefGoogle Scholar
  3. Cartwright, N. (1983). How the laws of physics lie. New York: Oxford University Press.CrossRefGoogle Scholar
  4. Cartwright, N. (1999). The dappled world: A study of the boundaries of science. New York: Cambridge University Press.Google Scholar
  5. Cat, J. (2005). Modeling cracks and cracking models. Synthese, 146, 447–487.CrossRefGoogle Scholar
  6. Chakravartty, A. (2009). Informational versus functional theories of scientific representation. Synthese. doi: 10.1007/s11229-009-9502-3.
  7. Curry, J. A., Webster, P. J., & Holland, G. J. (2006). Mixing politics and science in testing the hypothesis that Greenhouse warming is causing a global increase in hurricane intensity. Bulletin of the American Meteorological Society, 87, 1025–1037.CrossRefGoogle Scholar
  8. Emanuel, K. (2005). Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686–688.CrossRefGoogle Scholar
  9. Giere, R. (2006). Scientific perspectivism. Chicago: University of Chicago Press.Google Scholar
  10. Godfrey-Smith, P. (2006). The strategy of model-based science. Biology and Philosophy, 21, 725–740.CrossRefGoogle Scholar
  11. Hoyos, C. D., Agudelo, P. A., Webster, P. J., & Curry, J. A. (2006). Deconvolution of the factors contributing to the increase in global hurricane intensity. Science, 312, 94–97.CrossRefGoogle Scholar
  12. Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54, 421–431.Google Scholar
  13. Maue, R. N., & Hart, R. E. (2007). Comment on R. Sriver and M. Huber (2006). Geophysical Research Letters, 34, L11703.CrossRefGoogle Scholar
  14. Morrison, M. (2005). Approximating the real: The role of idealizations in physical theory. In M. R. Jones & N. Cartwright (Eds.), Idealization XII: Correcting the model. Idealization and abstraction in the sciences (pp. 145–172). Amsterdam: Rodopi.Google Scholar
  15. Oreskes, N., & Belitz, K. (2001). Philosophical issues in model assessment. In M. G. Anderson & P. D. Bates (Eds.), Model validation: Perspectives in hydrological science (pp. 23–41). London: Wiley.Google Scholar
  16. Oreskes, N., Schrader-Frechette, K., & Belitz, K. (1994). Verification, validation and confirmation of numerical models in the Earth sciences. Science, 263, 641–646.CrossRefGoogle Scholar
  17. Parker, W. (2007). Understanding pluralism in climate modeling. Foundations of Science, 11, 349–368.CrossRefGoogle Scholar
  18. Pincock, C. (2005). Conditions on the use of the one-dimensional Heat equation. In G. Sica (Ed.), Essays on the foundations of mathematics and logic (Vol. 2, pp. 67–79). Monza, Italy: Polimetrica.Google Scholar
  19. Pincock, C. (forthcoming). Towards a philosophy of applied mathematics. In O. Bueno & Ø. Linnebo (Eds.), New waves in philosophy of mathematics. Palgrave Macmillan.Google Scholar
  20. Psillos, S. (1999). Scientific realism. New York: Routledge.Google Scholar
  21. Sriver, R., & Huber, M. (2006). Low frequency variability in globally integrated tropical cyclone power dissipation. Geophysical Research Letters, 33, L11705.CrossRefGoogle Scholar
  22. Sriver, R., & Huber, M. (2007). Reply to comment by R.N. Maue and R.E. Hart. Geophysical Research Letters, 34, L11704.CrossRefGoogle Scholar
  23. Suárez, M. (1999). The role of models in the application of scientific theories: Epistemological implications. In M. Morgan & M. Morrison (eds) Models as mediators (pp. 168–196). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  24. Suárez, M., & Cartwright, N. (2008). Theories: Tools versus models. Studies in the History and Philosophy of Modern Physics, 39, 62–81.CrossRefGoogle Scholar
  25. Van Fraassen, B. (1980). The scientific image. Oxford: Oxford University Press.CrossRefGoogle Scholar
  26. Webster, P. J., Holland, G. J., Curry, J. A., & Chang, H. R. (2005). Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, 1844–1846.CrossRefGoogle Scholar
  27. Weisberg, M. (2006). Robustness analysis. Philosophy of Science (Proceedings), 73, 730–743.Google Scholar
  28. Weisberg, M. (2007). Who is a modeler? British Journal for the Philosophy of Science, 58, 207–233.CrossRefGoogle Scholar
  29. Wilson, M. (2006). Wandering significance: An essay on conceptual behavior. Oxford: Oxford University Press.CrossRefGoogle Scholar
  30. Wimsatt, W. (2007). Re-engineering philosophy for limited beings: Piecewise approximations to reality. Cambridge: Harvard University Press.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Department of PhilosophyPurdue UniversityWest LafayetteUSA

Personalised recommendations