Idealization and abstraction: refining the distinction
Tóm tắt
Idealization and abstraction are central concepts in the philosophy of science and in science itself. My goal in this paper is suggest an account of these concepts, building on and refining an existing view due to Jones (in: Jones MR, Cartwright N (eds) Idealization XII: correcting the model. Idealization and abstraction in the sciences, vol 86. Rodopi, Amsterdam, pp 173–217, 2005) and Godfrey-Smith (in: Barberousse A, Morange M, Pradeu T (eds) Mapping the future of biology: evolving concepts and theories. Springer, Berlin, 2009). On this line of thought, abstraction—which I call, for reasons to be explained, abstractness—involves the omission of detail, whereas idealization consists in a deliberate mismatch between a description (or a model) and the world. I will suggest that while the core idea underlying these authors’ view is correct, they make several assumptions and stipulations that are best avoided. For one thing, they tie abstractness too close to truth. For another, they do not allow sufficient room to the difference between idealization and error. Taking these points into account leads to a refined account of the distinction, in which abstractness is seen in terms of relative richness of detail, and idealization is seen as closely connected with the knowledge and intentions of idealizers. I lay out these accounts in turn, and then discuss the relationship between the two concepts, and several other upshots of the present way of construing the distinction.
Tài liệu tham khảo
Carandini, M., & Heeger, D. J. (2012). Normalization as a canonical neural computation. Nature Reviews Neuroscience, 13, 51–62.
Cartwright, N. (1999). The dappled world: A study of the boundaries of science. Oxford: Oxford University Press.
Elliot-Greaves, A., & Weisberg, M. (2014). Idealization. Philosophy Compass, 9, 176–185.
Frigg, R. (2006). Scientific Representation & the Semantic View of Theories. Theoria, 21(1), 49–65.
Giere, R. N. (1988). Explaining science: A cognitive approach. Chicago: The University of Chicago Press.
Godfrey-Smith, P. (2006). The strategy of model-based science. Biology and Philosophy, 21, 725–740.
Godfrey-Smith, P. (2009). Abstractions, Idealizations, and Evolutionary Biology. In A. Barberousse, M. Morange, & T. Pradeu (Eds.), Mapping the future of biology: Evolving concepts and theories. Berlin: Springer.
Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500–544.
Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569–1572.
Jones, M. (2005). Idealization and abstraction: A framework. In M. R. Jones & N. Cartwright (Eds.), Idealization XII: Correcting the model. Idealization and abstraction in the sciences (Vol. 86, pp. 173–217). Amsterdam: Rodopi.
Jones, N. (2013). Don’t blame the idealizations. Journal for General Philosophy of Science, 44(1), 85–100.
Knuuttila, T., & Leottgers, A. (forthcoming). Modelling as indirect representation? The Lotka–Volterra model revisited. British Journal for Philosophy of Science.
Laymon, R. (1995). Idealizations and the testing of theories by experimentation. In P. Achinstein & O. Hannaway (Eds.), Observation, experiment, and hypothesis in modern physical science. Cambridge: MIT Press.
Levy, A. (2011). Game theory, indirect modeling and the origins of morality. Journal of Philosophy, 108(4), 171–187.
Levy, A. (2015). Modeling without models. Philosophical Studies, 172(3), 781–798.
Levy, A., & Bechtel, W. (2013). Abstraction and the organization of mechanisms. Philosophy of Science, 80(2), 241–261.
Levy, A., & Currie, A. (2015). Model organisms are not (theoretical) models. British Journal for Philosophy of Science, 66(2), 327–348.
McMullin, E. (1985). Galilean idealization. Studies in History and Philosophy of Science, XVI, 247–273.
Nowak, L. (1992). The idealizational approach to science: A survey. In J. Brzeziński & L. Nowak (Eds.), Idealization III: Approximation and truth. Amsterdam: Rodopi.
Railsback, S. F., & Grimm, V. (2011). Agent-based and individual-based modeling: A practical introduction. Princeton: Princeton University Press.
Ramakrishnan, V. (2014). The ribosome emerges from a black box. Cell, 159, 979–984.
Rosen, G. (2012). Abstract objects. In E. N. Zalta (ed.) The stanford encyclopedia of philosophy (Fall 2017). http://plato.stanford.edu/archives/fall2014/entries/abstract-objects/.
Skillings, D. J. (2015). Mechanistic explanation of biological processes. Philosophy of Science, 82, 1139–1151.
Strevens, M. (2008). Depth: An account of scientific explanation. Cambridge: Harvard University Press.
Weisberg, M. (2007). Who is a modeler? British Journal for Philosophy of Science, 58, 207–233.
Weisberg, M. (2013). Simulation and similarity. New York: Oxford University Press.
Weisberg, M., & Reisman, K. (2008). The robust volterra principle. Philosophy of Science, 75(1), 106–131.