Abstract
It is common to distinguish three main senses of the term “robustness”: (1) Robustness of models; (2) Robustness as stability or insensitivity of output as against variations in parameter values; (3) Robustness as consilience of results from different and independent hypotheses, procedures or sources of evidence. The purpose of this paper is to discuss the last two meanings of robustness, in order to cope with some difficulties with which robustness as consilience is confronted and which have indirect consequences for the problem of scientific realism. On the one hand, robustness regarded as reproducible stability as against perturbations and variations in parameter values (robustness-as-stability) and robustness as consilience of results from different and independent pieces of evidence (robustness-as-consilience) are conceptually distinct. On the other hand, however, robustness-as-stability is a condition of robustness-as-consilience; and the converse holds also: robustness-as-consilience is an essential ingredient of robustness-as-stability. There is no vicious circle here, but a technical-practical synergy, which is at the heart of the experimental method, and which can help us out of the two main problems for robustness-as-consilience.
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Notes
- 1.
The main reason for this limitation is that this paper is mainly concerned with the role robustness plays in the experimental sciences, while a discussion of the first meaning would require a discussion of the role that computer simulation plays in real world experiments, which is beyond the scope of the present paper. Many authors found interesting analogies between model-based simulations and real-world experiments (cf. for example Norton and Suppe 2001; Parker 2009; Morrison 2009). However, the analogy between real world experiments and computer simulations, though correct within certain limits, fails to the extent that computer simulations may give us information about the actual world only because we have independent and empirical-experimental evidence of the model’s meaning (for a similar objection, see above all Hughes (1999 [2010]): 203; see also Buzzoni 2015, where further references will be found). For this reason, what will be said below may be extended to the first meaning of “robustness” in the measure in which model-based simulations can be considered as falling under the more general concept of experiment.
- 2.
The no miracles argument was already present, at least in its essential points, in Poincaré’s (1905, 154) and Duhem’s ([1906]1914, 36–39) works, and it is well-known that it was taken up by, among others, Smart (1963), Sellars (1963, Chap. 4), Putnam (1975, 60–78), Salmon (1984, 206–207), and Kosso (1989, 247 and 1992).
- 3.
There is no question of here entering into any serious discussion of an experimental-technical theory of truth, which I have briefly outlined elsewhere. For the most general features of such a theory, I must refer the reader to (Buzzoni 2008).
- 4.
- 5.
This is indirectly confirmed by the fact that Hudson 2014 neglects Hacking’s idea of combining robustness-as-stability with robustness-as-consilience—which is our starting point in this paper—, and essentially only finds in this author robustness-as-stability as “a key part” of his critique of robustness-as-consilience (Hudson 2014: 6).
- 6.
Boon (2012) has stressed the importance of this last concept of robustness in Hacking. But precisely because I essentially agree with Boon that robustness should be considered in connection with practical-technical applications (and not merely with statistics, as in Hacking’s case: cf. Hacking 1999: 231, Footnote 6), I think that Hacking’s contribution to robustness-as-consilience is much less important than that to robustness-as-stability and technical reproducibility.
- 7.
- 8.
On this point, cf. Buzzoni (2008, Chap. 1).
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Acknowledgements
I presented an earlier (and much briefer) version of this paper at the Congress of the “Académie International de Philosophie des Sciences” at the University of A Coruña, Ferrol Campus, Spain (September 23–26, 2015). Thanks to all those who contributed to the discussion of the paper during and after the conference, among which Thomas Nickles and Alberto Cordero deserve special mention. I am grateful to Mike Stuart, who read a draft of this article and provided extensive and helpful comments. Italian Ministry for Scientific Research (MIUR) provided funds for this research (PRIN 2012).
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Buzzoni, M. (2017). Robustness, Intersubjective Reproducibility, and Scientific Realism. In: Agazzi, E. (eds) Varieties of Scientific Realism. Springer, Cham. https://doi.org/10.1007/978-3-319-51608-0_7
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