Abstract
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.
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Notes
When referring to scientific representations, it is, strictly speaking, inappropriate to characterize them as “true” or “false”, as representations are neither true or false, but only accurate or inaccurate representations of their target, where accuracy is assessed according to multiple dimensions that crucially depend on the user and usage of the representation (van Fraassen 2008). That said, in this article, I will characterize scientific representations as “true” or “false”, for simplicity and also to align myself with Elgin’s own practice.
Elgin also takes thought experiments to be promising candidates to challenge veritism. She argues that the veritist cannot account for the epistemic value of thought experiments because they are “morally, practically, and physically unfeasible” (Elgin 2018, 15). It is unclear how exactly the moral, practical, or physical feasibility of an experiment bears on the truth of the scientific claims that are made in relation to the thought experiment. I will not discuss this issue any further in this paper: suffice it to say, Elgin’s claim is at least controversial, as many would argue that thought experiments are typically valuable because they afford knowledge, which implies that their epistemic value is related to truth (e.g. Norton (2004)).
The example is inspired by the chapter on “Rods and Clocks” of Norton’s open-source course “Einstein for Everyone”, available at https://www.pitt.edu/~jdnorton/teaching/HPS_0410/chapters/Special_relativity_clocks_rods/index.html.
Note that my argument does not rely on the claim that scientific accounts are best conceived of as sets of propositions. That question is very much open. That said, that scientific accounts, including models, can be (ought to be) conceived as collections of propositions has been defended in the literature (Thomson-Jones 2012). Note that authors who propose alternative views such as the semantic view offer alternative ways to measure of degrees of truth and corresponding epistemic value (da Costa and French 2003).
The extend to which Elgin’s usage of the term “exemplification” is related to Goodman’s views will not be addressed in this paper.
References
da Costa, N., & French, S. (2003). Science and partial truth: a unitary approach to models and scientific reasoning. Oxford: Oxford University Press.
de Regt, H. W., & Dieks, D. (2005). A contextual approach to scientific understanding. Synthese, 144, 137–170.
Easwaran, K. (2011). Bayesianism I: Introduction and arguments in favor. Philosophy Compass, 6(5), 312–320.
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.
Elgin, C. Z. (2004). True enough. Philosophical issues, 14, 113–131.
Elgin, C. Z. (2006). From knowledge to understanding. In S. Hetherington (Ed.), Epistemology futures (pp. 199–215). Oxford: Oxford University Press.
Elgin, C. Z. (2007). Understanding and the facts. Philosophical Studies, 132, 33–42.
Elgin, C. Z. (2008). Exemplification, idealization, and scientific understanding. In M. Suárez (Ed.), Fictions in science (pp. 85–98). Philadelphia: Rouledge.
Elgin, C. Z. (2009). Is understanding factive? In A. Haddock, A. Millar, & D. Pritchard (Eds.), Epistemic value (pp. 322–30). Oxford: Oxford University Press.
Elgin, C. Z. (2018). True enough. Cambridge: MIT Press.
Grimm, S. (2006). Is understanding a species of knowledge? British Journal for the Philosophy of Science, 57, 515–36.
Grimm, S., Baumberger, C., & Ammon, S. (2017). Explaining understanding: new perspectives from epistemology and philosophy of science. Philadelphia: Routledge.
Hills, A. (2016). Understanding why. Noûs, 50(4), 661–688.
Howson, C., & Urbach, P. (2006). Scientific reasoning: the Bayesian approach. Chicago: Open Court Publishing.
Kvanvig, J. (2003). The value of knowledge and the pursuit of understanding. Cambridge: Cambridge University Press.
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.
Mizrahi, M. (2012). Idealizations and scientific understanding. Philosophical Studies, 160(2), 237–252.
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).
Potochnik, A. (2017). Idealization and the aims of science. Chicago: University of Chicago Press.
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.
Sosa, E. (2007). A virtue epistemology. Oxford: Oxford University Press.
Strevens, M. (2008). Depth. Cambridge: Harvard University Press.
Thomson-Jones, M. (2012). Modeling without mathematics. Philosophy of Science, 79(5), 761–772.
Treanor, N. (2013). The measure of knowledge. Noûs, 47, 577–601.
van Fraassen, B. (2008). Scientific representation: paradoxes of perspective. Oxford: Oxford University Press.
Wilkenfeld, D. A. (2019). Understanding as compression. Philosophical Studies, 176(10), 2807–2831.
Williamson, T. (2002). Knowledge and its limits. Oxford: Oxford University Press on Demand.
Acknowledgements
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).
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Le Bihan, S. Partial truth versus felicitous falsehoods. Synthese 198, 5415–5436 (2021). https://doi.org/10.1007/s11229-019-02413-4
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DOI: https://doi.org/10.1007/s11229-019-02413-4