pp 1–22 | Cite as

Partial truth versus felicitous falsehoods

  • Soazig Le BihanEmail author
Themes from Elgin
Part of the following topical collections:
  1. True Enough? Themes from Elgin


Elgin has argued that scientific models that are, strictly speaking, inaccurate representations of the world, are epistemically valuable because the “falsehoods” they contain are “felicitous”. Many, including Elgin herself, have interpreted this claim as offering an alternative to scientific realism and “veritism”. In this paper, I will argue that there is a more felicitous interpretation of Elgin’s work: “felicitous falsehoods” do play a role in the epistemic value of inaccurate models, but that role is of instrumental value. Elgin’s view is not best understood as claiming that falsehoods provide scientific understanding in and of themselves, only that they facilitate epistemic access to the fundamental, even if partial, truths that are contained within models. While falsehoods may be felicitous in that they facilitate exemplification, the epistemic value of inaccurate models ultimately relies on their partial accuracy.


Understanding Idealization Models Factivity Realism 



I would like to thank Armond Duwell, Kareem Khalifa, Henk de Regt, Stephen Grimm, and of course Kate Elgin for multiple illuminating and friendly conversations on scientific understanding. I would also like to recognize various institutions for the support I received from them while developing my views on scientific understanding: the University of Pittsburgh Center for Philosophy of Science, the IHPST in Paris, SND (FRE 3593 Paris-Sorbonne), and Labex Transfer (Ecole Normale Supérieure, Paris).


  1. da Costa, N., & French, S. (2003). Science and partial truth: a unitary approach to models and scientific reasoning. Oxford: Oxford University Press.CrossRefGoogle Scholar
  2. de Regt, H. W., & Dieks, D. (2005). A contextual approach to scientific understanding. Synthese, 144, 137–170.CrossRefGoogle Scholar
  3. Easwaran, K. (2011). Bayesianism I: Introduction and arguments in favor. Philosophy Compass, 6(5), 312–320.CrossRefGoogle Scholar
  4. Elgin, C. (2010). Telling instances. In R. Frigg & M. Hunter (Eds.), Beyond mimesis and convention, Vol. 1 (262) of Boston studies in the philosophy and history of science (pp. 1–17). Berlin: Springer.Google Scholar
  5. Elgin, C. Z. (2004). True enough. Philosophical issues, 14, 113–131.CrossRefGoogle Scholar
  6. Elgin, C. Z. (2006). From knowledge to understanding. In S. Hetherington (Ed.), Epistemology futures (pp. 199–215). Oxford: Oxford University Press. Google Scholar
  7. Elgin, C. Z. (2007). Understanding and the facts. Philosophical Studies, 132, 33–42.CrossRefGoogle Scholar
  8. Elgin, C. Z. (2008). Exemplification, idealization, and scientific understanding. In M. Suárez (Ed.), Fictions in science (pp. 85–98). Philadelphia: Rouledge. Google Scholar
  9. Elgin, C. Z. (2009). Is understanding factive? In A. Haddock, A. Millar, & D. Pritchard (Eds.), Epistemic value (pp. 322–30). Oxford: Oxford University Press. Google Scholar
  10. Elgin, C. Z. (2018). True enough. Cambridge: MIT Press.Google Scholar
  11. Grimm, S. (2006). Is understanding a species of knowledge? British Journal for the Philosophy of Science, 57, 515–36.CrossRefGoogle Scholar
  12. Grimm, S., Baumberger, C., & Ammon, S. (2017). Explaining understanding: new perspectives from epistemology and philosophy of science. Philadelphia: Routledge.Google Scholar
  13. Hills, A. (2016). Understanding why. Noûs, 50(4), 661–688.CrossRefGoogle Scholar
  14. Howson, C., & Urbach, P. (2006). Scientific reasoning: the Bayesian approach. Chicago: Open Court Publishing.Google Scholar
  15. Kvanvig, J. (2003). The value of knowledge and the pursuit of understanding. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  16. Le Bihan, S. (2017). Enlightening falsehoods: a modal view of scientic understanding. In S. Grimm, C. Baumberger, & S. Ammon (Eds.), Explaining understanding: new perspectives from epistemology and philosophy of science (pp. 111–35). Philadelphia: Routledge.Google Scholar
  17. Mizrahi, M. (2012). Idealizations and scientific understanding. Philosophical Studies, 160(2), 237–252.CrossRefGoogle Scholar
  18. Norton, J. (2004) . On thought experiments: is there more to the argument?. In Proceedings of the 2002 biennial meeting of the philosophy of science association, Philosophy of Science (71), (pp. 1139–1151).CrossRefGoogle Scholar
  19. Potochnik, A. (2017). Idealization and the aims of science. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  20. Pritchard, D. (2010). Knowledge and understanding. In D. Pritchard, A. Millar, & A. Haddock (Eds.), The nature and value of knowledge: three investigations (Vol. chapter 1–4). Oxford: Oxford University Press.CrossRefGoogle Scholar
  21. Sosa, E. (2007). A virtue epistemology. Oxford: Oxford University Press.CrossRefGoogle Scholar
  22. Strevens, M. (2008). Depth. Cambridge: Harvard University Press.Google Scholar
  23. Thomson-Jones, M. (2012). Modeling without mathematics. Philosophy of Science, 79(5), 761–772.CrossRefGoogle Scholar
  24. Treanor, N. (2013). The measure of knowledge. Noûs, 47, 577–601.CrossRefGoogle Scholar
  25. van Fraassen, B. (2008). Scientific representation: paradoxes of perspective. Oxford: Oxford University Press.CrossRefGoogle Scholar
  26. Wilkenfeld, D. A. (2019). Understanding as compression. Philosophical Studies, 176(10), 2807–2831.CrossRefGoogle Scholar
  27. Williamson, T. (2002). Knowledge and its limits. Oxford: Oxford University Press on Demand.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of MontanaMissoulaUSA

Personalised recommendations