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Models, robustness, and non-causal explanation: a foray into cognitive science and biology

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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.

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Notes

  1. For work on controlling for free parameters in comparative tests of predictive accuracy, see Pitt and Myung (2002) and Roberts and Pashler (2000).

  2. Phenomena that converge to optimal states are a subset of Woodward’s robustness from ‘fine-tunedness’ (2013, pp. 59–61), since other systems may have a few stable states that they switch between.

  3. Interventions as defined in Woodward (2003).

  4. This comes from an argument against treating optimality models as ‘censored causal models’ of biological evolution (e.g. Elgin and Sober 2002; Potochnik 2007, 2010; for discussion see Rice 2012, 2013). Here, optimality models are seen as ignoring all causal factors related to evolution (genetic, epigenetic, environmental) apart from natural selection, so optimality models isolate a particular part of a casual process. As Rice (2012, 2013; also Matthen and Ariew 2009; Walsh 2007) argues though, there is very little in optimality models that says anything about processes of change or causal dynamics.

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Acknowledgments

Many thanks to Markus Eronen and Raphael van Riel for organizing the ‘Explanatory Power 2’ workshop that parts of this paper were prepared for, and the audience there for their comments. Thanks also to Matteo Colombo, Kim Sterelny, and Chris Lean for feedback on earlier versions of the paper, and to Catherine Stinson for discussions on models.

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  1. School of Philosophy, Australian National University, Coombs Building, Canberra, ACT , 2601, Australia

    Elizabeth Irvine

  2. School of Philosophy, University of Cardiff, Cardiff, UK

    Elizabeth Irvine

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Correspondence to Elizabeth Irvine.

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Irvine, E. Models, robustness, and non-causal explanation: a foray into cognitive science and biology. Synthese 192, 3943–3959 (2015). https://doi.org/10.1007/s11229-014-0524-0

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