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De-Idealization - No Easy 'Reversals'Standard philosophical accounts of scientific models treat them as involving idealizations. That quality is thought to be the result of the different processes that make models simplified, here taken to include processes of abstraction, omission, isolation, distortion and of course, making ideal. There is a considerable literature on idealization, but surprisingly little analysis of de-idealization undertaken for the purposes of applying these models to other domains of theory or to specified empirical cases. It is typically assumed that de-idealization involves reversing those processes of model making: making models more realistic by adding back in elements that were subtracted during those processes and by making the abstract and ideal concrete and particular (see McMullin 1985, Alexandrova 2008, Elliott-Graves and Weisberg 2014, and Svetlova 2013). But there are major difficulties in de-idealizing a model by such means.
One set of problems arises from trying to reverse assumptions made to help make models simple in several different respects. These include adding back factors that are part of the causal set, but assumed not to be the dominant cause(s), or adding back in variables that complicate matters which have been assumed independent when indeed they are not: that is, the causes were not de-composable in the first place. It may include correcting factors which have been set to ideal values rather than real values. Models which appear simple, may not in fact be that simple - as we realise when we try to de-idealize them in such a fashion.
It is equally difficult to address the problems of how to align models that have been idealized separately for different levels. Even when the models can be idealized at each level or for each segment, there is no reason that they can be de-idealized in any easy way to join up. Models may cut nature at the joints, but that does not mean that they can be easily rejoined together in a functional way.
Understanding de-idealization as a process of contextualisation - that is, of reversing abstractions and ideal qualities - is equally open-ended. Any model made relevant by de-idealizing for a particular domain, situation or purpose is likely to need a different de-idealization for every different context: time, place and topic. Finally, the most difficult problem is surely to figure out a set of rules for “concretizing”: scientists represent conceptual elements in their models, but these too have to be de-idealized - made concrete - in different ways for different sites and purposes (see Nowak, 1994).
Alexandrova, A. (2008) "Making models count." Philosophy of Science 75.3 383-404.
Elliott-Graves, A. and M. Weisberg (2014) "Idealization." Philosophy Compass 9.3 176-185.
McMullin, E. (1985), “Galilean Idealization” Studies in the History and Philosophy of Science, 16:3, 247-73.
Nowak, L. (1992), “The Idealizational Approach to Science: A Survey”, in J. Brezinski and L.Nowak (eds.), Idealization III: Approximation and Truth, vol. 25 of Poznán Studies, Rodopi, 9-63.
Svetlova, E, (2013) "De-idealization by commentary: the case of financial valuation models." Synthese 190.2 321-337.
University of South Carolina
London School of Economics