Abstract
Robustness is often presented as a guideline for distinguishing the true or real from mere appearances or artifacts. Most of recent discussions of robustness have focused on the kind of derivational robustness analysis introduced by Levins, while the related but distinct idea of robustness as multiple accessibility, defended by Wimsatt, has received less attention. In this paper, I argue that the latter kind of robustness, when properly understood, can provide justification for ontological commitments. The idea is that we are justified in believing that things studied by science are real insofar as we have robust evidence for them. I develop and analyze this idea in detail, and based on concrete examples show that it plays an important role in science. Finally, I demonstrate how robustness can be used to clarify the debate on scientific realism and to formulate new arguments.
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Notes
An anonymous referee also pointed out that Husserl discusses criteria resembling robustness. A thorough historical study would probably reveal many further sources for the idea.
The distinction between DR and MA was clearly and succinctly pointed out by Calcott (2011), who called the former “robust theorems” and the latter “robust detection”.
One could argue that also the results of MA are in the end propositions, for example propositions stating that some entity exists or has some property. However, even with this interpretation, there is a difference in kind between DR and MA: the results of the former are propositions of one kind (theorems), while the results of the latter are propositions of a different kind (existential propositions or property attributions).
Results from modeling can also contribute to MA by indicating that the target entity or property plays an explanatory role, but this is neither necessary nor sufficient for MA—see Sect. 3.
One implication of my account presented below is that there is a (low) degree of robustness also in the special case where there are just several independent ways of deriving a phenomenon. In this case, the criticism of non-empirical confirmation may apply. However, this is just a limit case, and nothing important turns on it: if empirical confirmation is taken to be necessary, we can simply add a clause stating that independent ways of derivation alone are not sufficient for robustness.
Wimsatt also mentions these features of robustness; my point is merely that they are not reflected in the rough definition he gives.
Strictly speaking, it would be more exact to say that “evidence for X is robust” or “our access to X is robust” instead of “X is robust”, since the latter expression seems to already imply the existence of X, and rule out by definition the robustness of non-existent entities (such as phlogiston). However, in most contexts nothing important turns on this distinction, so for the sake of readability, I continue to talk of X itself as robust (instead of our evidence for it).
It is also likely that what Wimsatt means by ‘definition’ or ‘definability’ is something weaker than what philosophers usually mean by it. For example, a definition of an electron given by a scientist could be that the electron is an elementary particle that has a negative electric charge of about \(1.602 \times 10^{-19}\) coulombs and a mass of about \(9.11 \times 10^{-28}\,\hbox {g}\). A philosopher would see this as a characterization of an electron, and not as a definition. In the context of robustness, multiple definability makes much more sense if we understand it in the weaker sense, i.e., as multiple characterizability. I thank Paul Teller for pointing this out.
To rule out a further potential source of misinterpretation: detectability, measurability, etc., does not refer to detectability, measurability, etc., in principle, or to physical or metaphysical possibility. It refers to detectability, measurability, etc. with current technology, experimental setups, theories, and so on.
Naturally, the different ways of detecting, measuring, or producing a phenomenon can also vary regarding their inherent relevance: some methods are more reliable and produce stronger evidence than others. However, the issue of evaluating strength of evidence has been discussed in other contexts in philosophy of science (see for example Crupi et al. 2007 or Cartwright 2007, Chap. 3 for more). Since the strength of different lines of evidence can be evaluated independently of considerations of robustness, there is no problematic circularity here: robustness appears at a higher level when different strands of evidence are aggregated. A related point worth mentioning is that the different ways of measuring or detecting should be at least minimally reliable—as Calcott (2011) points out, if the independent means have a probability of \(<\)0.5 of being correct, then adding more independent means does not increase the justification (or in Calcott’s terminology, likelihood of truth).
This definition is not explained in more detail, and thus could also be understood as something closer to DR. However, the example that follows (warming of the climate system) clearly exhibits robustness as MA, as it refers to “observations” and not just models. In any case, the main purpose of this brief example is to demonstrate the importance of robustness for scientific debates. It is not intended to further clarify the notion, or to provide an analysis of the exact kind of robustness reasoning applied by the IPCC (which might be an interesting project in its own right).
I do not intend to dismiss such criteria, but rather to point out that they have a different role than robustness. In fact, these criteria are entirely compatible with the robustness approach. For example, one could argue that the ultimate metaphysical criterion for what is real is the causal criterion, but the source of justification for science-based ontological commitments is robustness.
References
Barlow, H. B., & Levick, W. R. (1969). Changes in the maintained discharge with adaptation level in the cat retina. Journal of Physiology, 202, 699–718.
Bechtel, W. (2008). Mental mechanisms. Philosophical perspectives on cognitive neuroscience. London: Routledge.
Bird, A. (1998). Philosophy of science. London: Routledge.
Boon, M. (2012). Understanding scientific practices: The role of robustness notions. In L. Soler, E. Trizio, T. Nickles, & W. C. Wimsatt (Eds.), Characterizing the robustness of science: After the practice turn in the philosophy of science (pp. 289–315). Dordrecht: Springer.
Boyd, R. (1991). Realism, anti-foundationalism and the enthusiasm for natural kinds. Philosophical Studies, 61, 127–148.
Calcott, B. (2011). Wimsatt and the robustness family: Review of Wimsatt’s re-engineering philosophy for limited beings. Biology & Philosophy, 26, 281–293.
Cartwright, N. (1983). How the laws of physics lie. Oxford: Clarendon Press.
Cartwright, N. (2007). Hunting causes and using them. Approaches in philosophy and economics. Cambridge: Cambridge University Press.
Chang, H. (2004). Inventing temperature. Measurement and scientific progress. Oxford: Oxford University Press.
Crupi, V., Tentori, K., & Gonzalez, M. (2007). On Bayesian measures of evidential support: Theoretical and empirical issues. Philosophy of Science, 74, 229–252.
Culp, S. (1994). Defending robustness: The bacterial mesosome as a test case. In D. Hull, M. Forbes, & R. M. Burian (Eds.), Proceedings of the biennial meeting of the Philosophy of Science Association 1994 (Vol. 1, pp. 46–57). East Lansing: The Philosophy of Science Association.
Do, M. T. H., & Yau, K.-W. (2010). Intrinsically photosensitive retinal ganglion cells. Physiological Reviews, 90, 1547–1581.
Enć, B. (1976). Reference of theoretical terms. Noûs, 10, 261–282.
Eronen, M. I. (2012). Pluralistic physicalism and the causal exclusion argument. European Journal for Philosophy of Science, 2, 219–232.
Esfeld, M., & Sachse, C. (2007). Theory reduction by means of functional sub-types. International Studies in the Philosophy of Science, 21, 1–17.
Fine, A. (1984). The natural ontological attitude. In J. Leplin (Ed.), Scientific realism (pp. 83–107). Berkeley: University of California Press.
Fitelson, B. (2001). A Bayesian account of independent evidence with applications. Philosophy of Science, 68, S123–S140.
Forber, P. (2010). Confirmation and explaining how possible. Studies in History and Philosophy of Biological and Biomedical Sciences, 41, 32–40.
Franklin, A., & Howson, C. (1984). Why do scientists prefer to vary their experiments? Studies in the History and Philosophy of Science, 15, 51–62.
Hacking, I. (1982). Experimentation and scientific realism. Philosophical Topics, 13, 154–172.
Hacking, I. (1983). Representing and intervening. Introductory topics in the philosophy of natural science. New York: Cambridge University Press.
Hudson, R. (2014). Seeing things: The philosophy of reliable observation. Oxford: Oxford University Press.
IPCC. (2007). Climate change 2007: Synthesis report. Contribution of Working Groups I, II and III to the fourth assessment report of the Intergovernmental Panel on Climate Change. Geneva: IPCC.
Kim, J. (1993). The non-reductivist’s troubles with mental causation. In J. Heil & A. Mele (Eds.), Mental causation (pp. 189–210). Oxford: Clarendon Press.
Kosso, P. (1989). Science and objectivity. The Journal of Philosophy, 86, 245–257.
Kumar Nayak, S., Jegla, T., & Panda, S. (2007). Role of a novel photopigment, melanopsin, in behavioral adaption to light. Cellular and Molecular Life Sciences, 64, 144–154.
Kuorikoski, J., Lehtinen, A., & Marchionni, C. (2010). Economic modelling as robustness analysis. British Journal for the Philosophy of Science, 61, 541–567.
Kuorikoski, J., Lehtinen, A., & Marchionni, C. (2012). Robustness analysis disclaimer: Please read the manual before use! Biology & Philosophy, 27, 891–902.
Ladyman, J., & Ross, D. (2007). Every thing must go: Metaphysics naturalised. Oxford: Oxford University Press.
Laudan, L. (1981). A confutation of convergent realism. Philosophy of Science, 48, 19–48.
Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54, 421–431.
Matthewson, J., & Weisberg, M. (2009). The structure of tradeoffs in model building. Synthese, 170, 169–190.
Nederbragt, H. (2003). Strategies to improve the reliability of a theory: The experiment of bacterial invasion into cultured epithelial cells. Studies in History and Philosophy of Biological and Biomedical Sciences, 34, 593–614.
Nederbragt, H. (2012). Multiple derivability and the reliability and stabilization of theories. In L. Soler, E. Trizio, T. Nickles, & W. C. Wimsatt (Eds.), Characterizing the robustness of science: After the practice turn in the philosophy of science (pp. 121–145). Dordrecht: Springer.
Odenbaugh, J., & Alexandrova, A. (2011). Buyer beware: Robustness analyses in economics and biology. Biology & Philosophy, 26, 757–771.
Orzack, S. H., & Sober, E. (1993). A critical assessment of Levins’s the strategy of model building in population biology (1966). The Quarterly Review of Biology, 68, 533–546.
Pettit, P. (1993). A definition of physicalism. Analysis, 53, 213–223.
Quine, W. v O. (1981). Theories and things. Cambridge, MA: Harvard University Press.
Raerinne, J. (2013). Robustness and sensitivity of biological models. Philosophical Studies, 166, 285–303.
Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton, NJ: Princeton University Press.
Soler, L. (2014). Against robustness? Strategies to support the reliability of scientific results. International Studies in the Philosophy of Science, 28, 203–215.
Soler, L., Trizio, E., Nickles, T., & Wimsatt, W. C. (Eds.). (2012). Characterizing the robustness of science: After the practice turn in the philosophy of science. Dordrecht: Springer.
Staley, K. W. (2004). Robust evidence and secure evidence claims. Philosophy of Science, 71, 467–488.
Stegenga, J. (2009). Robustness, discordance, and relevance. Philosophy of Science, 76, 650–661.
Stegenga, J. (2012). Rerum concordia discors: Robustness and discordant multimodal evidence. In L. Soler, E. Trizio, T. Nickles, & W. C. Wimsatt (Eds.), Characterizing the robustness of science: After the practice turn in the philosophy of science (pp. 207–226). Dordrecht: Springer.
Trizio, E. (2012). Achieving robustness to confirm controversial hypotheses: A case study in cell biology. In L. Soler, E. Trizio, T. Nickles, & W. C. Wimsatt (Eds.), Characterizing the robustness of science: After the practice turn in the philosophy of science (pp. 105–120). Dordrecht: Springer.
Trout, J. D. (1998). Measuring the intentional world. Oxford: Oxford University Press.
Van Fraassen, B. (1980). The scientific image. Oxford: Oxford University Press.
Weisberg, M. (2006). Robustness analysis. Philosophy of Science, 73, 730–742.
Weisberg, M., & Reisman, K. (2008). The robust Volterra principle. Philosophy of Science, 75, 106–131.
Wimsatt, W. C. (1981). Robustness, reliability, and overdetermination. In M. Brewer & B. Collins (Eds.), Scientific inquiry and the social sciences (pp. 124–163). San Francisco: Jossey-Bass. (Reprinted in W. C. Wimsatt. (2007). Re-engineering philosophy for limited beings. Piecewise approximations to reality (pp. 43–74). Cambridge, MA: Harvard University Press.)
Wimsatt, W. C. (1994). The ontology of complex systems: Levels of organization, perspectives, and causal thickets. Canadian Journal of Philosophy, S20, 207–274. (Reprinted in W. C. Wimsatt. (2007). Re-engineering philosophy for limited beings. Piecewise approximations to reality (pp. 193–240). Cambridge, MA: Harvard University.)
Wimsatt, W. C. (2007). Re-engineering philosophy for limited beings. Piecewise approximations to reality. Cambridge: Harvard University Press.
Woodward, J. (2003). Making things happen. A theory of causal explanation. Oxford: Oxford University Press.
Woodward, J. (2006). Some varieties of robustness. Journal of Economic Methodology, 13, 219–240.
Acknowledgments
I would like to thank the (five) anonymous referees of this journal, whose insightful and detailed comments were extremely useful in improving the manuscript. I am also very grateful to the following individuals for their helpful comments on earlier drafts: Hugh Desmond, James DiFrisco, Harmen Ghijsen, Chris Kelp, Jaakko Kuorikoski, Jani Raerinne, Paul Teller, and Raphael van Riel, as well as audiences at Ruhr University Bochum, University of Groningen, and KU Leuven. I especially thank Jan Heylen and Laura Bringmann for their very constructive and helpful feedback on several versions of the paper. The research resulting in this paper was funded by the Research Foundation Flanders—FWO (Postdoctoral Fellowship).
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The research resulting in this paper was funded by the Research Foundation Flanders–FWO (Postdoctoral Fellowship).
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Eronen, M.I. Robustness and reality. Synthese 192, 3961–3977 (2015). https://doi.org/10.1007/s11229-015-0801-6
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DOI: https://doi.org/10.1007/s11229-015-0801-6