Ankeny & Leonelli (2011) recently spelled out a number of epistemological characteristics of model organisms which, they think, make them special in the more general category of experimental organisms. In this seminar I show how some similar epistemological characteristics apply to a theoretical model, i.e. the Mendelian population, making it special in respect to other theoretical constructs (equations). Both cases seem to suggest restrictions in the usage of the term “model” to the advantage of a defined model notion. Here I aim to refine and broaden such notion of a model, and explore the epistemological issues it raises.
Ankeny & Leonelli define model organisms as “non-human species that are extensively studied in order to understand a range of biological phenomena, with the hope that data and theories generated through use of the model will be applicable to other organisms, particularly those that are in some way more complex than the original model” (p. 313).
Mathematical population genetics – a major pillar of neo-Darwinian evolutionary theory – is often referred to as a great set or family of models, where “models” mean, arguably, equations of gene frequencies or phenotypic change.
The glaring discrepancy between organisms and equations seems to characterize experimental biology and population genetics by two irreducibly different “modeling strategies”: the material and the empirical (cf. Leonelli 2006). By diverting the attention away from equations, in this seminar I challenge such classical distinction.
I present population genetics in a uncommon way: I dismiss the term “model” for equations, and save it for the Mendelian population, i.e. the fundamental formal combination space population genetics equations are about.
One interesting result of my approach is to liken a formal system to an organic system – at least for some “key epistemological characteristics” (cf. Ankeny & Leonelli, cit.). I explore the notion of a model as a stable target of explanation (cf. Keller 2002) that I think captures both objects, and the related epistemological problems about representation, explanation, and prediction. Models as stable targets of explanation are systems selected for intensive research, yielding their stability and a cost-effective apparatus of experimental resources; they feature some degree of artificiality, and are never exhaustively known, even in case of complete artificiality.
Ankeny, R. a & Leonelli, S., 2011. What’s so special about model organisms? Studies In History and Philosophy of Science Part A, 42(2):313-323.
Keller, E. F. (2002). Making sense of life: Explaining biological development with models, metaphors and machines. Cambridge, MA: Harvard University Press.
Look for it in the Talks page (with additional links):
2012, Jan 23 (h.3:00-4:30 PM) – The ESRC Centre for Genomics in Society (Egenis), University of Exeter: Model as a “stable target of explanation”: Mendelian population like model organisms?. Seminar.