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Robust realism for the life sciences

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Abstract

Although scientific realism is the default position in the life sciences, philosophical accounts of realism are geared towards physics and run into trouble when applied to fields such as biology or neuroscience. In this paper, I formulate a new robustness-based version of entity realism, and show that it provides a plausible account of realism for the life sciences that is also continuous with scientific practice. It is based on the idea that if there are several independent ways of measuring, detecting or deriving something, then we are justified in believing that it is real. I also consider several possible objections to robustness-based entity realism, discuss its relationship to ontic structural realism, and show how it has the potential to provide a novel response to the pessimistic induction argument.

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Notes

  1. The exact structure of Cartwright’s original argument is not clear. For an overview of different interpretations and their problems, see Hitchcock (1992).

  2. Suárez (2008) and Egg (2012, 2016) defend entity realism by arguing that, when the right conditions are satisfied, causal explanation does give warrant for believing that the cause is real. This strategy is interesting, but I will not discuss it in detail here, as it leads to a form of entity realism that is not well suited for accounting for scientific realism in the life sciences—see Sect. 4 for more.

  3. This idea has also many other names, including triangulation, overdetermination, mutual grounding, diverse testing, argument from coincidence, and so on. Note also that this use of the term ’robustness’ should not be confused with the distantly related notion of ’robustness analysis’ that has been much discussed in the context of modeling (e.g., Weisberg 2006; Odenbaugh and Alexandrova 2011; Kuorikoski et al. 2012). See Calcott (2011) and Eronen (2015) for more on the differences between various notions of robustness.

  4. This can also be seen as a common cause argument: The independent pieces of evidence are explained by being due to a common cause, i.e., the robust entity or property (see, e.g., Salmon 1984). However, the account of robustness I offer below is more general and includes dimensions (e.g., derivability and explanatory role) that do not easily fit into the common cause framework.

  5. Strictly speaking, it would be more accurate to always write “robust evidence for X” instead of “X is robust”, but for the sake of readability, I also use the latter kinds of expressions here.

  6. For more discussion on robustness and independence, see Hudson (2014), Kuorikoski and Marchionni (2016), Schupbach (2016) and Stegenga (2009).

  7. However, many other aspects of the constructive empiricism of van Fraassen (1980) are in fact compatible with RER: For example, RER does not require accepting inference to the best explanation as valid for theories and explanations.

  8. The extent to which measuring a property (or believing in the reality of a property) requires belief in theories also depends on how broadly we understand ‘theory’. For more on the relationships between properties, theories and realism, see Chakravartty (2007, chap. 2–3).

  9. See Fahrbach (2011) for a different but complementary argument against pessimistic induction based on the exponential growth of science.

  10. Much of this section was written in response to insightful comments by two anonymous reviewers, for which I am very grateful.

  11. What this means for OSR is an open question that goes beyond the scope of this paper. It is possible, for example, that the ontological commitments that RER supports could somehow be translated to the objectless ontology of OSR (cf. Ladyman and Ross 2007), rendering the two compatible. On the other hand, it has also been argued that there is no need to expand OSR to the special sciences (Lyre 2013). Note also that even if it is true that there are no objects but only structure in the ontology of fundamental physics, this is not a threat to RER, as RER does not imply or require that only entities or properties exist, or that robustness is the only route to ontological commitments and justification (see Sect. 3).

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Acknowledgements

I thank Laura Bringmann, Daniel Brooks and Leah Henderson for very helpful critical comments on earlier drafts of the paper. I am also grateful to two anonymous referees, and the audiences at Philosophy of Science in a Forest 2016 (Utrecht), PSA 2016 (Atlanta) and OZSW 2016 (Groningen), for their constructive feedback. The research that led to this paper was generously funded by Fonds Wetenschappelijk Onderzoek (Grant No. 102627).

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  1. Centre for Logic and Analytical Philosophy, Institute of Philosophy, KU Leuven, Kardinaal Mercierplein 2, PO Box 3200, 3000, Leuven, Belgium

    Markus I. Eronen

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Eronen, M.I. Robust realism for the life sciences. Synthese 196, 2341–2354 (2019). https://doi.org/10.1007/s11229-017-1542-5

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