Martin Zach (Charles University)
It has long been argued that idealized model schemas cannot provide us with factive scientific understanding, precisely because these models employ various idealizations; hence, they are false, strictly speaking (e.g., Elgin 2017, Potochnik 2015). Others defend a middle ground (e.g., Mizrahi 2012), but only few espouse (in one way or another) the factive understanding account (e.g., Reutlinger et al. 2017, Rice 2016).
In this talk, and on the basis of the model schema of metabolic pathway inhibition, I argue for the conclusion that we do get factive understanding of a phenomenon through certain idealized and abstract model schemas.
As an example, consider a mechanistic model of metabolic pathway inhibition, specifically the way in which the product of a metabolic pathway feeds back into the pathway and inhibits it by inhibiting the normal functioning of an enzyme. It can be said that such mechanistic model abstracts away from various key details. For instance, it ignores the distinction between competitive and non-competitive inhibition. Furthermore, a simple model often disregards the role of molar concentrations. Following Love and Nathan (2015) I submit to the view that neglecting concentrations from a model is an act of idealization. Yet, models such as these do provide us with factive understanding when they tell us something true about the phenomenon, namely the way in which it is causally organized, i.e. by way of negative feedback (see also Glennan 2017). This crucially differs from the views of those (e.g., Strevens 2017) who argue that idealizations highlight causal irrelevance of the idealized factors. For the phenomenon to occur, it makes all the difference precisely what kind of inhibition is at play and what the molar concentrations are.
Finally, I will briefly distinguish my approach to factive understanding from those of Reutlinger et al. (2017) and Rice (2016). In Reutlinger et al. (2017), factive (how-actually) understanding is achieved by theory-driven de-idealizations, however, as such it importantly differs from my view which is free of such need. Rice (2016) suggests that optimization models provide factive understanding by providing us with true counterfactual information about what is relevant and irrelevant, which, again, is not the case in the example discussed above.
