The growing importance of online knowledge commons to all science and industry — whether in personalized medicine, machine learning, or climate science — illustrates the importance of understanding how computational formalisms impact our ability to address societal problems through collective action. The social dimension of formalization, however, is largely absent from major debates in philosophy of science, e.g. on the nature of statistical evidence, natural kinds, and particular biological phenomena such as individuality or function. My research in philosophy of science demonstrates important epistemic costs and benefits to formalization in pluralistic settings where researchers differ in fundamental ways, such as in aims, metaphysics, or methodology.

Today, climate change and biodiversity loss dominate the broader social relevance of research in systematics, but the established historical narrative of post-WWII systematics is entirely focused on heated battles within the field over different methodological theories for classification and phylogenetic inference. In prior work, Scott Lidgard and I have argued that the mathematization of systematics provides a better historical account of these battles (Sterner and Lidgard 2014, 2018). We showed in particular that historians have overlooked important methodological problems where otherwise antagonistic research programs could cooperate and learn from each other due to shared mathematical abstractions in their computational procedures. Our work on the unexpected effects of computerization in systematics provides a foundation for future research into how computational software and databases have shaped systematics’ contributions to the emergence of a globalized biodiversity knowledge commons.

Together with Nico Franz, we are developing a novel philosophy for decentralized but globally coordinated biodiversity data science (inspired by the Web’s organization into “small pieces loosely joined” (Weinberger 2008). Our approach is decentralized in that it encourages the emergence of multiple, locally specialized languages, datasets, and methods for describing and reasoning about biodiversity data. It nonetheless achieves global coordination through computational reasoning tools that enable translation across data classifications, such as alternative metadata languages or taxonomies.