Cory Lewis

A key issue in the philosophy of biology is evolutionary contingency, the degree to which evolutionary outcomes could have been different. Contingency is typically contrasted with evolutionary convergence, where different evolutionary pathways result in the same or similar outcomes. Convergences are given as evidence against the hypothesis that evolutionary outcomes are highly contingent. But the best available treatments of contingency do not, when read closely, produce the desired contrast with convergence. Rather, they produce a picture in which any degree of contingency is compatible with any degree of convergence. This is because convergence is the repeated production of a given outcome from different starting points, and contingency has been defined without reference to the size of the space of possible outcomes. In small spaces of possibilities, the production of repeated outcomes is almost assured. This paper presents a definition of contingency which includes this modal dimension in a way that does not reduce it to the binary notion of contingency found in standard modal logic. The result is a conception of contingency which properly contrasts with convergence, given some domain of possibilities and a measure defined over it. We should therefore not ask whether evolution is contingent or convergent simpliciter, but rather about the degree to which it is contingent or convergent in various domains, as measured in various ways.

Frederick Grinnell’s “Everyday Practice of Science” is an ambitious attempt to survey the methodological issues facing practicing scientists. His examples and anecdotes are mainly drawn from his own field of biochemistry, which he argues is representative of the scientific method in general because, quoting Nobel Laureate Sir Peter Medawar, “Biologists work very close to the frontier between bewilderment and understanding.”(p.4) Grinnell’s goal is to explore the ambiguity and messiness of actual scientific practice, but not with an eye to undermine its credibility. Rather, he tries to show how the day-to-day practice of science functions to generate reliable hypotheses from the complexity of reality

Scientific models are often said to be more or less general depending on how many cases they cover. In this paper we argue that the cardinality of cases is insufficient as a metric of generality, and we present a novel account based on measure theory. This account overcomes several problems with the cardinality approach, and additionally provides some insight into the nature of assessments of generality. Specifically, measure theory affords a natural and quantitative way of describing local spaces of possibility. The generality of models can be understood as the measure of possibilities to which the models apply. In order to illustrate our view, we consider the example of structural genomics, the ongoing project of building three-dimensional models of biological macromolecules like proteins. Using measure theory, we interpret the practice of homology modeling, where such models are understood to apply widely but imperfectly to the space of possible proteins