Synthese 192 (12):3943-3959 (2015)

Authors
Elizabeth Irvine
Cardiff University
Abstract
This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and cognitive science. Clearly, defences of non-causal explanation are far from new (e.g. Batterman, Br J Philos Sci 53:21–38, 2002a; The devil in the details: asymptotic reasoning in explanation, reduction, and emergence, 2002b; Pincock, Noûs 41:253–275, 2007; Mathematics and scientific representation 2012; Rice, Noûs. doi:10.1111/nous.12042, 2013; Biol Philos 27:685–703, 2012), so the targets here are focused on a particular type of robust phenomenon and how strong invariance to interventions can block a range of causal explanations. By focusing on a common form of model construction, the paper also ties functional or computational style explanations found in cognitive science and biology more firmly with explanatory practices across model-based science in general.
Keywords Models  Robustness  Explanation  Template  Invariance  Structural explanation  Mathematical explanation  Mechanisms
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DOI 10.1007/s11229-014-0524-0
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References found in this work BETA

Vison.David Marr - 1982 - W. H. Freeman.
Explaining the Brain.Carl F. Craver - 2009 - Oxford University Press.

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Citations of this work BETA

Explanation in Computational Neuroscience: Causal and Non-Causal.M. Chirimuuta - 2018 - British Journal for the Philosophy of Science 69 (3):849-880.
Models and Explanation.Alisa Bokulich - 2017 - In Lorenzo Magnani & Tommaso Wayne Bertolotti (eds.), Springer Handbook of Model-Based Science. Springer. pp. 103-118.

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