Chance as an abstractor in life science explanations

Since Darwin first articulated the theory of evolution, chance in biology has been considered a source of variability. With the advent of the new synthesis in the 1940s and 50s, this notion found further confirmation through the study of random mutations and drift. The question that intrigues me about this topic is, what about chance underneath evolution? First, by saying “underneath” I am not dismissing evolution but rather referring to a mode of enquiry that examines what chance could mean at a finer-grained timescale. Evolutionary biology typically, but not exclusively, focuses at the levels above genetic populations. This project aims to explore molecular and cellular-level domains, which are beneath (underneath) the main evolutionary exploration. Second, “underneath” in my framework also indicates metaphorically the willingness to make a study that frames chance with epistemological tools other than the classical evolutionary ones. Looking “under” evolution means looking under (but not beyond) classical evolutionary tools used to study chance in biology. Contemporary work in biology deals extensively with stochasticity, and the questions addressed are not always evolutionarily centered. In these works, it seems that chance has some explanatory role that is rarely made explicit. Evidently, there is still much work to be done in the study of the different roles and meanings that chance can have underneath evolution. In my presentation, instead of asking if chance has a role in evolutionary theory and/or in certain biological systems, I will address the question of whether chance can have a role in certain biological explanations. In order to do that, I will propose an account of stochastic explanation (SE). SE is a philosophical category that refers to any biological explanation with a chancy element that provides (explicitly or implicitly) (part of) its explanatory power. My argument is that chance as an abstraction can make an explanation a “good” one. In the presentation, I will unpack what I mean by a “good” explanation, I will show the differences between SE and certain accounts of statistical explanations (Ariew et al 2017; Lange 2013, Matthen 2009), and I will highlight the philosophical interest of SE and its “potential usefulness” (Kaiser 2015) for biological practice