Abstract
I review the scientific situation with the emergence of population biology that led Richard Levins to introduce the idea of looking for robust theorems, and the influences that lead Donald Campbell to introduce “triangulation”. My review tied these two notions together, and looked for other convergent methodologies that showed some of the same characteristics I baptized as “robustness analysis”. I review the main types, and then turn to a further characterization of material robustness, which has become the primary focus of studies in biology and elsewhere in the last decade. I discuss one key source of this robustness—sexual recombination—and then close with some remarks on robustness, complexity, fragility, and generative entrenchment.
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Notes
- 1.
The first textbook in this area that explicitly recognized that the equations presented were models and not established theories, and self-consciously discussed their shortcomings and idealizations, was Wilson and Bossert’s (1971) A Primer of Population Biology. An inexpensive paperback, designed as a supplement to “main” biology texts, it was also the first book published by Andy Sinauer’s new firm which targeted and became the premier publisher in this area.
- 2.
I was surprised at the vehemence they showed in defending this belief. I remember describing scientific practice to Davidson, and saying that biologists must have meant something different by models than he did. His response was simply to say angrily: “Well, they’re wrong!” (end of conversation). Such hubris! This strange excursion from the real world is no-where better described and criticized than in Downes (1992).
- 3.
Brett Calcott (2010) has since produced a nice three-way classification in terms of multiple means between formal methods, detection, and material robustness (lumping the 1st and 3rd senses of the 4 discussed here). I agree with his classification: the distinction between my 1 and 3 can be seen as a distinction between kinds of detection methods, though it is also true that a model (from the “false model” perspective) can be seen as a kind of selective filter for the kind of pattern explored in the model. With that, only the kinds of exploration of the functions of multiple derivational pathways in a single formal system (like those investigated by Kromer here and by Corfield 2010) would stand out as a distinct kind. But to regard models as kinds of selective pattern detectors might violate too many intuitions!
- 4.
Twenty years after Campbell and Fiske (1959), that paper was the most cited in the journal, which asked Fiske to review the 2000+ citations it had received to assess the role the methodology might have had in generating progress. Most citations were obligatory “field” reviews that cited but made no use of it, and the success from the much smaller number that did discuss or use it was very disappointing. (The original was in the domain of personality theory, which has been a minefield for attempts at “objective” or “valid” classification scales, so perhaps it would have been better in other areas.) I wondered whether a particularly severe problem in the studies reviewed was in separating the phenomenon being investigated from the tools of analysis, and Fiske agreed (in conversation, 1982). We usually have clearer conceptions where one leaves off and the other begins in the biological and natural sciences, but when this fails, it should be problematic for any robustness analyses.
- 5.
In Wimsatt (1991), I discuss how multiple views (in this case, 4 different graphical representations of a chaotic phenomenon) were required to understand it. These views are not independent (they are analytically related), but the different representations are crucial to being able to see different aspects of the phenomenon. This is a kind of visual robustness, made relevant by the limitations of our visual and cognitive apparatus.
- 6.
The relation between molecular and classical conceptions of the genes is heterogeneous on both sides, and much more complex than between micro-state and macro-state descriptions in classical mechanics—roughly because there are many mechanisms between micro- and macro- in genetics and many more kinds of anomalies possible (see, e.g., Beurton et al. 2000).
- 7.
There are surprisingly few papers estimating this, and fewer recently. Roughly 15% of eggs fail to implant. Estimates of the frequency of first trimester abortions varies substantially between 15 and 30%. Boue et al. (1975) is the most common citation.
- 8.
This raises questions about Wagner’s assumption that environmental fluctuations should be a stronger driver than genetic compatibilities. As I noted in (2006) he considered only mutation and ignored recombination, which could produce variances that would be orders of magnitude higher than those produced by mutation—though of course not necessarily larger than environmental variance.
- 9.
When I worked at the National Cash Register Company (NCR) in 1962 designing adding machine components, there was a sudden interest in hydraulic logic computers, which mimicked electron flows with water streams, exhibited by the defense department. These would be isomorphic to electronic ones (with diodes, adders, and registers), but would not be sensitive to electromagnetic fields induced by nuclear weapons. Their multiple other disadvantages ruled them out however.
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Acknowledgement
I wish to thank Lena Soler for her imaginative conception of a conference on robustness, for inviting me, and for the productivity of her resulting vision and Tom Nickles for continuing productive interactions over many years.
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Wimsatt, W.C. (2012). Robustness: Material, and Inferential, in the Natural and Human Sciences. In: Soler, L., Trizio, E., Nickles, T., Wimsatt, W. (eds) Characterizing the Robustness of Science. Boston Studies in the Philosophy of Science, vol 292. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2759-5_3
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