How can a reflective scientist put forward an explanation using a model when they are aware that many of the assumptions used to specify that model are false? This paper addresses this challenge by making two substantial assumptions about explanatory practice. First, many of the propositions deployed in the course of explaining have a non-representational function. In particular, a proposition that a scientist uses and also believes to be false, i.e. an “idealization”, typically has some non-representational function in the practice, such as the interpretation of some model or the specification of the target of the explanation. Second, when an agent puts forward an explanation using a model, they usually aim to remain agnostic about various features of the phenomenon being explained. In this sense, explanations are intended to be autonomous from many of the more fundamental features of such systems. I support these two assumptions by showing how they allow one to address a number of recent concerns raised by Bokulich, Potochnik and Rice. In addition, these assumptions lead to a defense of the view that explanations are wholly true that improves on the accounts developed by Craver, Mäki and Strevens.
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In addition, Bokulich argues that some of these explanations are not causal explanations.
Of course, not all models are mathematical models. For other kinds of models, other kinds of specifications will be involved.
See also Rohwer and Rice (2016). I do not consider here their requirement that an explanatory model be conducive to understanding. See also Lawler and Sullivan (2020) for another useful discussion of how models can “induce, enable, or generate explanations” (23). However, Lawler and Sullivan do not offer any test for when such models should count as explanatory.
See also Saatsi (2012) for a discussion of what he calls “inferentially veridical representations”.
For Strevens, an idealization can enhance the state of understanding of an agent who grasps the explanation. This point is developed in more detail in Strevens (2017). I must postpone consideration of the complex debates about the relationship between truth, explanation and understanding due to reasons of space.
Yablo uses his notion of subject matter to isolate a role for statements S that are generated by a model \(\omega \) but that are false of the actual world \(\alpha \): “S’s truth in \(\omega \) signals its truth in \(\alpha \) about a subject matter m that \(\omega \) and \(\alpha \) agree on” (2020, 144). However, it is not clear what Yablo would say about our question concerning explanatory models. Also, there are various ways to identify the subject matter of a proposition that have implications for debates about models and explanations. I must reserve an examination of these difficult issues for future work.
See Fletcher (2019) for a promising examination of how to connect Newtonian gravitation with general relativity. Fletcher’s conclusion is that “if only in retrospective rational reconstruction, the transition to relativity theory from Newtonian physics involves much more conceptual continuity than is usually emphasized” (Fletcher 2019, 13).
This should make clear that I ultimately agree with Rice and Strevens that many idealizations are useful because they make overly specific, false claims, whose corresponding less specific claims are true. My disagreement with these authors thus turns on how this diagnosis of the usefulness of idealizations is deployed in an analysis of when a model is explanatory.
See Potochnik 2011 for an objection to Strevens based on his use of explanatory frameworks. In a recent paper Strevens has developed an account of “asymptotic idealization” that is considerably more flexible than the “simple idealization” considered in his earlier work (Strevens 2019). In this paper Strevens is clear that he aims to offer “a rational reconstruction” of what scientists have in mind when advancing explanations with models (2019, 1724). So one way to summarize my concerns is that Strevens’ reliance on cohesion is not consistent with the aim of rational reconstruction.
Batterman, R., & Rice, C. (2014). Minimal model explanations. Philosophy of Science, 81, 349–376.
Bokulich, A. (2011). How scientific models can explain. Synthese, 180, 33–45.
Bokulich, A. (2012). Distinguishing explanatory from nonexplanatory fictions. Philosophy of Science, 79, 725–737.
Bokulich, A. (2016). Fiction as a vehicle for truth: Moving beyond the ontic conception. The Monist, 99, 260–279.
Bokulich, A. (2017). Models and explanation. In L. Magnani & T. W. Bertolotti (Eds.), Springer Handbook of Model-Based Science (pp. 103–118). Berlin: Springer.
Bokulich, A. (2018). Representing and explaining: The eikonic conception of scientific explanation. Philosophy of Science, 85, 793–805.
Craver, C. (2014). The ontic account of scientific explanation. In M. Kaiser, O. Scholz, D. Plenge, & A. Hütterman (Eds.), Explanation in the Special Sciences (pp. 27–52). Berlin: Springer.
Fletcher, S. (2019). On the reduction of general relativity to Newtonian gravitation. Studies in the History and Philosophy of Modern Physics, 68, 1–15.
Lawler, I. & E. Sullivan (2020). Model explanation versus model-induced explanation. Foundations of Science, forthcoming.
Mäki, U. (2012). The truth of false idealizations in modeling. In P. Humphreys & C. Imbert (Eds.), Models, Simulations, and Representations (pp. 216–233). Abingdon: Routledge.
McMullin, E. (1985). Galilean idealization. Studies in the History and Philosophy of Science, 16, 247–273.
Newton, I. (1999). The Principia: Mathematical principles of natural philosophy (I. B. Cohen, A. Whitman & J. Budenz, trans.). Oakland, CA: University of California Press.
Potochnik, A. (2011). Explanation and understanding: An alternative to Strevens’ Depth. European Journal for the Philosophy of Science, 1, 29–38.
Potochnik, A. (2015a). Causal patterns and adequate explanations. Philosophical Studies, 172, 1163–1182.
Potochnik, A. (2015b). The diverse aims of science. Studies in the History and Philosophy of Science, Part A, 53, 71–80.
Potochnik, A. (2016). Scientific explanation: Putting communication first. Philosophy of Science, 83, 721–732.
Potochnik, A. (2017). Idealization and the aims of science. Chicago: University of Chicago Press.
Potochnik, A. (2020). Idealization and many aims. Philosophy of Science, 87, 933–943.
Price, H. (2013). Expressivism, pragmatism and representationalism. Cambridge: Cambridge University Press.
Rohwer, Y., & Rice, C. (2013). Hypothetical pattern idealization and explanatory models. Philosophy of Science, 80, 334–355.
Rohwer, Y., & Rice, C. (2016). How are models and explanations related? Erkenntnis, 81, 1127–1148.
Rice, C. (2018). Idealized models, holistic distortions, and universality. Synthese, 195, 2795–2819.
Rice, C. (2019). Models don’t decompose that way: A holistic view of idealized models. British Journal for the Philosophy of Science, 70, 179–208.
Saatsi, J. (2012). Idealized models as inferentially veridical representations: A conceptual framework. In P. Humphreys & C. Imbert (Eds.), Models, Simulations, and Representations (pp. 234–249). Abingdon: Routledge.
Saatsi, J. (2016). On the ‘indispensable explanatory role of mathematics.’ Mind, 125, 1045–1070.
Strevens, M. (2008). Depth: An account of scientific explanation. Cambridge: Harvard University Press.
Strevens, M. (2012). Replies to Weatherson, Hall, and Lange. Philosophy and Phenomenological Research, 84, 492–505.
Strevens, M. (2017). How idealizations provide understanding. In S. Grimm, C. Baumberger, & S. Ammon (Eds.), Explaining understanding: New perspectives from epistemology and philosophy of science (pp. 37–49). Abingdon: Routledge.
Strevens, M. (2019). The structure of asymptotic idealization. Synthese, 196, 1713–1731.
Weatherson, B. (2012). Explanation, idealisation and the Goldilocks problem. Philosophy and Phenomenological Research, 84, 461–473.
Woodward, J. (2003a). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.
Woodward, J. (2003b). Experimentation, causal inference, and instrumental realism. In H. Radder (Ed.), The philosophy of scientific experimentation (pp. 87–118). Pittsburgh: University of Pittsburgh Press.
Woodward, J. (2006). Sensitive and insensitive causation. The Philosophical Review, 115, 1–50.
Yablo, S. (2014). Aboutness. Princeton: Princeton University Press.
Yablo, S. (2020). Models and reality. In A. Levy & P. Godfrey-Smith (Eds.), The Scientific Imagination (pp. 128–153). Oxford: Oxford University Press.
This paper has benefitted enormously from comments and questions by a number of audiences: (i) the Philosophy Colloquium of Oakland University (March 2019), (ii) the 11th Annual Auburn Philosophy Conference (April 2019), (iii) Idealization Across the Sciences, Institute of Philosophy, Prague, Czech Republic (June 2019), and (iv) Mathematics and its Applications: Philosophical Issues, University of Leeds (Sept. 2019). I am especially grateful to Alisa Bokulich, Joyce Havstad, James Nguyen, Angela Potochnik, Collin Rice, Juha Saatsi, and Michael Strevens for critical feedback. Two anonymous referees for this journal also provided very helpful suggestions for improvement.
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Pincock, C. A Defense of Truth as a Necessary Condition on Scientific Explanation. Erkenn (2021). https://doi.org/10.1007/s10670-020-00371-9