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Idealizations and scientific understanding

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Abstract

In this paper, I propose that the debate in epistemology concerning the nature and value of understanding can shed light on the role of scientific idealizations in producing scientific understanding. In philosophy of science, the received view seems to be that understanding is a species of knowledge. On this view, understanding is factive just as knowledge is, i.e., if S knows that p, then p is true. Epistemologists, however, distinguish between different kinds of understanding. Among epistemologists, there are those who think that a certain kind of understanding—objectual understanding—is not factive, and those who think that objectual understanding is quasi-factive. Those who think that understanding is not factive argue that scientific idealizations constitute cognitive success, which we then consider as instances of understanding, and yet they are not true. This paper is an attempt to draw lessons from this debate as they pertain to the role of idealizations in producing scientific understanding. I argue that scientific understanding is quasi-factive.

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Notes

  1. See, e.g., Toulmin (1961), Salmon (1993), Schurz and Lambert (1994), and Weber (1996). Cf. Kosso (2007). Like other philosophers of science, Kosso (2007, p. 173) does not deny the factivity of scientific understanding. Rather, he argues that “Knowledge of many facts does not amount to understanding unless one also has a sense of how the facts fit together” (2007, p. 173).

  2. See, e.g., Zagzebski (2001), Kvanvig (2003), and Elgin (2007).

  3. For more on scientific models, see, e.g., Giere (1988), Godfrey-Smith (2006), and Weisberg (2007).

  4. I will be concerned with the function of idealizations, insofar as they produce scientific understanding, rather than their ontology. For the distinction between the ontology of models and their function in the practice of science, see Contessa (2010). See also the essays collected in Suarez (2009).

  5. In the literature on scientific models, an idealization is usually taken to be a deliberate simplification of something complicated in order to make it more manageable as an object of study. Examples include frictionless planes, point masses, and of course, the billiard ball model of gases. Furthermore, two kinds of idealizations have been given much attention in the literature, namely, Aristotelian and Galilean idealizations. Aristotelian idealization is taken to be an imaginary “stripping away” of properties from a concrete object that are considered to be irrelevant to the question of interest. This kind of idealization is also called “abstraction.” See, e.g., Cartwright (1989). Galilean idealization is taken to involve a deliberate distortion of a concrete system in order to make it more tractable as an object of study. See McMullin (1985). Models that involve significant Galilean idealizations are often called “caricatures.” See, e.g., Gibbard and Varian (1978) and the essays collected in Bovens, et al. (2008).

  6. Ideal gases are a standard example of idealizations in the philosophy science literature as well. See, e.g., Godfrey-Smith (2009). See also Elgin (2009).

  7. See, e.g., Pan et al. (1998).

  8. Chemistry textbooks usually include a chapter discussing deviations from ideal gas behavior. See, e.g., Whitten et al. (2010, pp. 435–438). See also Ashkenazi et al. (2008).

  9. In the case of nitrogen, for example, at 0°C and pressures below 120 atm, it negatively deviates from ideal gas behavior. See Pauling (1988, p. 335).

  10. See Dilworth (2007, chap. 10). See also Losee (2004).

  11. Cf. Godfrey-Smith (2009).

  12. According to Cartwright (1999, p. 23), theoretical statements are not meant to have universal scope; there are no laws or principles that hold across the board and across all domains of inquiry. But this doesn’t rule out “a variety of different kinds of knowledge in a variety of different domains across a range of highly differentiated situations.”

  13. We should also keep in mind the distinction between the ontology of idealizations and their function in the practice of science. See note 4 above.

  14. A similar argument, it seems to me, can be made not only with cognitive success but also with pragmatic success. That is, if scientists were to fail in their attempts to control and manipulate gases under certain conditions, and their attempts were guided by the gas laws, we would be reluctant to attribute to them either pragmatic or cognitive success. Cf. Giere (2004).

  15. Cf. chapter 11 in Suarez (2009), especially what Giere (2009) calls “the argument from imperfect fit.”

  16. According to Contessa (2010, p. 226), “specification of the model occurs whenever one of its users substitutes some indefinite values of some characteristics of the model with definite values or specifies some boundary conditions.”

  17. Available at: http://ebooks.adelaide.edu.au/t/thucydides/crawley/index.html. Accessed 22 Feb 2010.

  18. Cf. Kosso (2007).

  19. In other words, scientific statements are intersubjectively testable, i.e., different researchers should be able to obtain evidence for a statement. See, e.g., Piccinini (2003).

  20. Scientific anti-realists might object that predictive success doesn’t have epistemic import. This debate between scientific realists and anti-realists is beyond the scope of this paper. See, e.g., Psillos (1999).

  21. See Ashkenazi et al. (2008).

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Acknowledgments

Many thanks for feedback on earlier drafts of this paper to Jonathan Adler, Alberto Cordero, and Catherine Wilson.

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  1. Department of Philosophy, Hunter College, The City University of New York, 695 Park Ave., New York, NY, 10065, USA

    Moti Mizrahi

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Mizrahi, M. Idealizations and scientific understanding. Philos Stud 160, 237–252 (2012). https://doi.org/10.1007/s11098-011-9716-3

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