Abstract
In this paper, I first argue against various attempts to justify idealizations in scientific models that explain by showing that they are harmless and isolable distortions of irrelevant features. In response, I propose a view in which idealized models are characterized as providing holistically distorted representations of their target system(s). I then suggest an alternative way that idealized modeling can be justified by appealing to universality.
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Notes
Weisberg, of course, identifies several other kinds of idealization. However, minimalist idealization is the one most closely connected with idealized models that explain. Weisberg also includes several other philosophers whose accounts are close to minimalist idealization. However, for the sake of space, I will not discuss those views here.
Moreover, Strevens tells us, “All idealizations, I suggest, work in the same way” (Strevens 2009, p. 341).
For example, as Kadanoff (2000) puts it, “The existence of a phase transition requires an infinite system. No phase transitions occur in systems with a finite number of degrees of freedom” (238).
As the number of particles approaches infinity, the system’s correlation length diverges to infinity. At this point, all the scales of the system become relevant to its behavior. The divergence of this correlation length leads to the breaking of certain symmetries (or invariances) in the system’s Hamiltonian.
Optimality modeling also includes game-theoretic modeling where the optimal strategy is typically frequency dependent.
The standard view might try to analyze the distortions introduced by these idealizations one at a time in isolation. However, this is almost always impossible since the evolutionary system represented by the mathematical model is a result of a complex and interacting collection of modeling assumptions. As a result, the claim that these idealized models pervasively distort their target system(s) ought to be evaluated by considering the assumptions of the model as an interacting whole. Thanks to an anonymous reviewer for helping me emphasize this point.
While some of these idealizations only distort the first-order processes of selection, others distort the second-order processes, and still others pervasively distort the basic components and causal interactions operating within the model’s target system(s) in order to apply mathematical modeling techniques.
See Rice (2013) for additional details about the counterfactual information provided by optimality explanations.
It is important to note that this methodological prescription does not necessarily entail holism with respect to metaphysical structure, meaning, or confirmation.
This is consistent with the model being an accurate representation with respect to some aspects of its target system(s). However, pervasive distortion involves the misrepresentation most of the features of the model’s target system(s), including many features (e.g. causal factors) that are difference makers for the target explanandum.
As a more specific example, consider how Michael Strevens’s kairetic account of explanation leads directly to his account of how idealized models can be justifiably used to explain only when they accurately represent difference-makers and introduce idealizations that only distort irrelevant causal factors.
Wimsatt ’s (2007) views about false models leading to truer theories are also in line with the approach suggested here.
As I mentioned above, it is important that this is only one additional way to connect idealized models with their target system(s) in ways that allow for explanation. That is, the appeal to universality classes is not meant to provide a replacement univocal account of how idealized models connect with their target system(s). Thanks to an anonymous reviewer for helping me emphasize this point.
These ponds are important because, while ice reflects most incident sunlight, these melt ponds absorb most of it.
References
Ariew, A., Rice, C., & Rohwer, Y. (2015). Autonomous statistical explanations and natural selection. The British Journal for the Philosophy of Science, 66(3), 635–658.
Batterman, R. (2000). Multiple realizability and universality. The British Journal of Philosophy of Science, 51, 115–145.
Batterman, R. W. (2002). The devil in the details: Asymptotic reasoning in explanation, reduction, and emergence. Oxford: Oxford University Press.
Batterman, R. W. (2010). On the explanatory role of mathematics in empirical science. British Journal for the Philosophy of Science, 61, 1–25.
Batterman, R. W., & Rice, C. (2014). Minimal model explanations. Philosophy of Science, 81(3), 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.
Carruthers, P. (2006). The architecture of the mind: Massive modularity and the flexibility of thought. Oxford: Oxford University Press.
Cartwright, N. (1983). How the laws of physics lie. Oxford: Oxford University Press.
Churchland, P. (2013). Touching a nerve: Self as brain. New York: Norton.
Corsano, G., Montagna, J. M., Iribarren, O., & Aguirre, P. (2009). Mathematical modeling approaches for optimizations of chemical processes. New York: Nova Science Publishers.
Elgin, M., & Sober, E. (2002). Cartwright on explanation and idealization. Erkentniss, 57, 441–450.
Fisher, R. A. (1922). On the dominance ratio. Proceeding of the Royal Society of Edinburgh, V, 43, 321–341.
Golden, K. M. (2014). Mathematics of sea ice. In N. J. Higham, M. Dennis, P. Glendinning, J. Tanner, & F. Santosa (Eds.), The Princeton companion to applied mathematics. Princeton: Princeton University Press.
Goldenfeld, N., & Kadanoff, L. P. (1999). Simple lessons from complexity. Science, 284, 87–89.
Hartmann, A. K., & Reiger, H. (2002). New optimization algorithms in physics. Berlin: Wiley.
Heath, J. P., Glichrist, H. G., & Ydenberg, R. C. (2007). Can dive cycle models predict patterns of foraging behavior? Diving by common eiders in an Arctic polynya. Animal Behavior, 73, 877–884.
Heath, J. P., Gilchrist, H. G., & Ydenberg, R. C. (2010). Interactions between rate processes with different timescales explain counterintuitive foraging patterns of arctic wintering eiders. Proceedings of the Royal Society B, 277, 3179–3186.
Hohenegger, C., Alali, B., Steffen, K. R., Perovich, D. K., & Golden, K. M. (2012). Transition in the fractal geometry of Arctic melt ponds. The Cryosphere, 6, 1157–1162.
Huneman, P. (2010). Topological explanations and robustness in biological sciences. Synthese, 177, 213–245.
Huneman, P. (2015). Diversifying the picture of explanation in biological sciences: Ways of combining topology with mechanisms. Synthese. doi:10.1007/s11229-015-0808-z.
Kadanoff, L. P. (2000). Statistical physics: Statics, dynamics, and renormalization. Singapore: World Scientific.
Kadanoff, L. P. (2013). Theories of matter: Infinities and renormalization. In Robert Batterman (Ed.), The Oxford handbook of philosophy of physics (pp. 141–188). Oxford: Oxford University Press.
Kuorikoski, J., Lehtinen, A., & Marchionni, C. (2010). Economic modeling as robustness analysis. The British Journal for the Philosophy of Science, 61, 541–567.
Lehtinen, A., & Kuorikoski, J. (2007). Unrealistic assumptions in rational choice theory. Philosophy of Social Science, 37(2), 115–138.
Levins, R. (1966). The strategy of model building in population biology. In E. Sober (Ed.), Conceptual issues in evolutionary biology (pp. 18–27). Cambridge, MA: MIT Press.
Mäki, U. (1992). On the method of isolation in economics. Poznan Studies in Philosophical Science, 38, 147–168.
Maynard Smith, J. (1978). Optimization theory in evolution. Annual Review of Ecological Systems, 9, 31–56.
Maynard Smith, J. (1982). Evolution and the theory of games. New York: Cambridge University Press.
McMullin, E. (1985). Galilean idealization. Studies in History and Philosophy of Science Part A, 16(3), 247–273.
Morrison, M. (1996). Physical models and biological contexts. Philosophy of Science, 64, S315–S324.
Morrison, M. (2009). Understanding in physics and biology. In Henk W. de Regt, Sabina Leonelli, & Kai Eigner (Eds.), Scientific understanding: Philosophical perspectives. Pittsburgh: Pittsburgh University Press.
Morrison, M. (2015). Reconstruction reality: Models, mathematics, and simulations. Oxford: Oxford University Press.
Orzack, S., & Sober, E. (2001). Adaptationism and optimality. Cambridge: Cambridge University Press.
Pexton, M. (2014). How dimensional analysis can explain. Synthese, 191, 2333–2351.
Pindyck, R. S., & Rubinfeld, D. L. (2009). Microeconomics (7th ed.). London: Pearson.
Potochnik, A. (2007). Optimality modeling and explanatory generality. Philosophy of Science, 74, 680–691.
Potochnik, A. (2010). Explanatory independence and epistemic interdependence: A case study of the optimality approach. The British Journal for the Philosophy of Science, 61, 213–233.
Pyke, G. H. (1984). Optimal foraging theory: A critical review. Annual Review of Ecology and Systematics, 15(1), 523–575.
Rice, C. (2012). Optimality explanations: A plea for an alternative approach. Biology and Philosophy, 27(5), 685–703.
Rice, C. (2013). Moving beyond causes: Optimality models and scientific explanation. Noûs, 49(3), 589–615.
Saatsi, J., & Pexton, M. (2012). Reassessing Woodward’s account of explanation: Regularities, counterfactuals, and non-causal explanations. Philosophy of Science, 80, 613–624.
Seger, J., & Stubblefield, J. W. (1996). Optimization and adaptation. In M. Rose & G. V. Lauder (Eds.), Adaptation. Cambridge: Cambridge University Press.
Sober, E. (2000). The philosophy of biology (2nd ed.). Boulder, CO: Westview Press.
Stephens, D. W., & Krebs, J. R. (1986). Foraging theory. Princeton: Princeton University Press.
Stigler, S. (2010). Darwin, Galton and the statistical enlightenment. The Journal of the Royal Statistical Society, 173(3), 469–482.
Strevens, M. (2009). Depth: An account of scientific explanation. Cambridge: Harvard University Press.
Walsh, D., Lewens, T., & Ariew, A. (2002). Trials of life: Natural selection and random drift. Philosophy of Science, 69(3), 452–473.
Weisberg, M. (2007). Three kinds of idealization. Journal of Philosophy, 104(12), 639–659.
Weisberg, M. (2013). Simulation and similarity. New York: Oxford University Press.
Wimsatt, W. C. (2007). Re-engineering philosophy for limited beings: Piecewise approximates to reality. Cambridge, MA: Harvard University Press.
Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.
Acknowledgements
I would like to thank audiences at the Philosophy of Science Association Meeting, the Munich Center for Mathematical Philosophy, Bryn Mawr College, Lycoming College, and the University of California, Irvine for their helpful feedback on previous versions of this work. I would also like to thank Angela Potochnik, Robert Batterman, André Ariew, and two anonymous reviewers for helpful comments and feedback on earlier versions of the paper. This work was partially supported by Visiting Scholar Funding from the University of California, Irvine’s LPS Department, a Lycoming College Professional Development Grant, and a Senior Visiting Fellowship from the Munich Center for Mathematical Philosophy.
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Rice, C. Idealized models, holistic distortions, and universality. Synthese 195, 2795–2819 (2018). https://doi.org/10.1007/s11229-017-1357-4
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DOI: https://doi.org/10.1007/s11229-017-1357-4