Synthese

, Volume 191, Issue 8, pp 1831–1845 | Cite as

Idealized and perspectival representations: some reasons for making a distinction

Article

Abstract

I argue that an adequate understanding of the practice of constructing models in physics requires a distinction between two strategies that are commonly both labeled ‘idealization’. The formal characteristic of both methods is to let a parameter in the equations for a target system go to zero. But the discussion of examples from various applications of perturbation theory shows that there is in general a difference with respect to the aims such limiting procedures are supposed to serve; and with different aims comes the need to characterize the means (the interpretation of the limits) differently. I therefore suggest that we distinguish ‘idealizations’ from ‘perspectives’ or perspectival representations.

Keywords

Modeling in physics Scientific representation Perturbation theory Singular limits 

References

  1. Barenblatt, G. (1996). Scaling, self-similarity, and intermediate asymptotics. Cambridge: Cambridge University Press.Google Scholar
  2. Batterman, R. (2009). Idealization and modelling. Synthese, 169, 427–446.CrossRefGoogle Scholar
  3. Batterman, R. (2013). The tyranny of scales. In R. Batterman (Ed.), Oxford handbook of the philosophy of physics (pp. 256–286). Oxford: Oxford University Press.CrossRefGoogle Scholar
  4. Cartwright, N. (1989). Nature’s capacities. Oxford: Oxford University Press.Google Scholar
  5. Castiglione, P., Falcioni, M., Lesne, A., & Vulpiani, A. (2008). Chaos and coarse graining in statistical mechanics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  6. Chorin, A., & Marsden, J. (1990). A mathematical introduction to fluid mechanics (2nd ed.). New York: Springer.CrossRefGoogle Scholar
  7. Giere, R. (2006). Scientific perspectivism. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  8. Hinch, E. J. (1991). Perturbation methods. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  9. Holmes, M. (1995). Introduction to perturbation methods. New York: Springer.CrossRefGoogle Scholar
  10. Pincock, C. (2013). How to avoid inconsistent idealizations. Synthese (in press).Google Scholar
  11. Pincock, C., et al. (2009). Towards a philosophy of applied mathematics. In O. Bueno (Ed.), New waves in the philosophy of mathematics (pp. 173–194). London: Palgrave.Google Scholar
  12. Rueger, A. (2005). Perspectival models and theory unification. British Journal for the Philosophy of Science, 56, 579–594.Google Scholar
  13. Torquato, S. (2002). Random heterogeneous materials. New York: Springer.CrossRefGoogle Scholar
  14. Van Fraassen, B. (2008). Scientific representation. Oxford: Oxford University Press.CrossRefGoogle Scholar
  15. Weisberg, M. (2007). Three kinds of idealization. Journal of Philosophy, 104, 639–659.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of AlbertaEdmontonCanada

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