Abstract
In this paper, I show how idealizations contribute to social activities in science, such as the recruitment of experts to a research project. These contributions have not been explicitly discussed by recent philosophical accounts of scientific idealization (e.g., Potochnik in Idealization and the aims of science, University of Chicago Press, Chicago, 2017; Weisberg in Simulation and similarity: using models to understand the world, OUP, Oxford, 2013; Wimsatt in Re-engineering philosophy for limited beings: Piecewise approximations to reality, Harvard University Press, Cambridge, 2007). These accounts have focused on how idealizations influence activities like scientific theorization, explanation, and modeling. Other accounts focus on how idealizations influence policy-making and science communication (Gilbert and Justi in Modelling-based teaching in science education, Springer international publishing, Basel, Switzerland, 2016; Roussos in Philos Sci, 2020. https://doi.org/10.1086/712818). I expand these accounts by exploring the uses of idealized phylogenetic trees in science. Trees are not only useful for improving our understanding and public communication of evolutionary history, but they also help with the organization of laboratories and collaboration among scientists. Attending to the relation between idealizations and these social practices in science matters. It can help us understand why idealized models become entrenched and why certain social practices change over time.
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Source: adapted from Pardo et al. (2017)
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Notes
In this paper, I use the term ‘representation’ to refer to what philosophers of science usually treat as scientific representations, such as diagrams, equations, different types of models, but also theories and verbal descriptions or statements (Frigg and Nguyen 2016). Representations are things that can be deemed true, ‘true enough,’ false, veridical, accurate, inaccurate, or correct. In this sense, ‘representational activities’ are practices that directly result in these scientific representations. I explain terminological choice in the next section (also footnote 3).
Potochnik argues that the primary epistemic aim of science is understanding rather than truth. Her argument follows Catherine Elgin’s work (2004).
The label “representational activity” might cause confusion. In a basic sense, all activities involving models are representational. Models represent target systems and, thus, one must comprehend, interpret, manipulate, or extract information and content from these representations. In this sense, one can say that all uses of models are “representational activities” (I return to this point in Sect. 6). My thesis in this paper is that such uses often lead to the social organization of science. Yet, to make this thesis clearer, I employ “representational activities” when referring only to activities aiming at producing new scientific representations. Hence, my thesis can be re-stated as follows: models are often used in important social activities of scientists; activities that do not necessarily and directly generate new scientific representations.
Hence, the focus on truth implies the focus on representational activities but not vice-versa. One might argue that idealized models are useful for enabling scientists to arrive at new models, but this does not necessarily mean that such idealizations are enabling scientists to get the truth.
The group formed by the most recent common ancestor and all and only its descendants is called a monophyletic group (Hennig 1966; Wiley and Lieberman 2011). In Fig. 1, there are at least two monophyletic groups. The first one consists of A, B, and their most recent common ancestor, while the second consists of C, D, and their most recent common ancestor. Some phylogeneticists might also identify a third monophyletic group formed by every taxon (A, B, C, D) in the tree and their most recent common ancestor.
A welcome exception to this trend is Sterner and Lidgard (2018). This work analyzes the so-called “Systematics War”—the dispute between phylogenetic systematics and other schools of taxonomy in the second half of the twentieth Century. This analysis suggests that “systematists' theories about how to classify or infer evolutionary trees did not merely or even primarily aim to encapsulate representational knowledge about nature" (p. 33). According to this view, classification methods did not only contribute to the representation of knowledge, but also offered guidelines for producing and organizing this knowledge. Sterner and Lidgard (2018) focus on technical and procedural aspects in the production and organization of knowledge. In contrast, the present paper focuses on explicitly social and institutional aspects of this production and organization. I thank one of the reviewers for bringing this paper to my attention.
When biologists construct supertrees, they frequently use methods that involve pruning (Bininda-Emonds 2004, p. 316; Whidden et al. 2014). To prune a phylogenetic tree is to cut off some of its terminal branches or tips. When biologists prune a phylogenetic tree, they assume the isolation assumption. This assumption suggests that each terminal branch is attached to the tree only at one single point (its immediate common ancestor). Hence, the isolation assumption assures that, by pruning, one can cut a branch of the tree off and everything else will remain the same. This way of manipulating trees is relevant when combining source trees into supertrees.
In principle, the conflict between source trees might result from other things rather than conflicting molecular data sets. For example, different assumptions or method might generate conflicting trees. While Bininda-Emonds and other scholars seem aware of this fact, they are interested in occasions when the conflict between source trees results and is evidence of conflicting molecular data set.
Despite the similarity in the example, Woody’s contribution is different from mine in several respects. First and foremost, she proposes an account of explanation and argues that models (such as the periodic table) have an explanatory role. One of the roles would be guiding the creation of scientific norms and, thus, the social organization of scientific communities. In contrast, this paper is not committed to any account of explanation and does not argue that models must have an explanatory role in Woody’s sense. This paper explores the connection of models and practices more broadly, irrespective of whether one thinks these models explain or not (and what counts as explanation).
For examples, see the Mike Benton laboratory at the University of Bristol, the Per Ahlberg laboratory at the University of Uppsala, the Jason Anderson laboratory at the University of Calgary, and the Robert Reisz laboratory at the University of Toronto. For decades, Jenny Clack’s laboratory at the University of Cambridge was also important in this research (see Clack 2012).
To read about other aspects of this collaborative research, visit https://natureecoevocommunity.nature.com/users/51782-jason-s-anderson/posts/17990-datasets-revisited.
One might worry that my description of (3) relies on a problematic assumption about expertise. The assumption is that members of a laboratory become experts on specific parts of the phylogenetic trees rather than on animal groups independently from where these animals are on the tree. My description of (3) does not contain this assumption. Rather, I only assume that laboratories in vertebrate paleontology are well-known for working on animal groups in one or a few specific regions of the tree. Trees are convenient tools for identifying these groups.
The difference between a strictly branching phylogenetic tree and a network might be a matter of degree. If one includes one or two new branches connecting different parts of the tetrapod tree (Fig. 2), one will be turning a tree into a network. Nevertheless, networks typically contain many branches, which make them way less convenient for the activities (1)–(3) above.
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Acknowledgements
I thank Marc Ereshefsky, Angela Potochnik, Ken Waters, and the audience at POBAMz 2020 for helpful feedback on early versions of this work. I thank Lorena Machado for the figures. The Izaak Waltom Killam Memorial Scholarship funded part of this research during the final year of my PhD at the University of Calgary. Finally, I thank Ford Doolittle and Letitia Meynell for my Postdoctoral Fellowship at the Dalhousie University.
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Neto, C. From idealizations to social practices in science: the case of phylogenetic trees. Synthese 199, 10865–10884 (2021). https://doi.org/10.1007/s11229-021-03271-9
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DOI: https://doi.org/10.1007/s11229-021-03271-9