Abstract
It has long been argued that idealized model sketches cannot provide us with factive scientific understanding, precisely because these models employ various idealizations; hence, they are false, strictly speaking (e.g. Elgin 2004, 2007, Potochnik 2015).
Others espouse the view that understanding is quasi-factive (e.g. Mizrahi 2012), acknowledging the role of simplifying assumptions and the need to relax the standards, though there are well known issues surrounding this position.
Few have defended (in one way or another) the factive understanding account despite the objections raised against it (e.g. Reutlinger et al 2017, Rice 2016).
In this talk I argue for the claim that there is a way in which we can maintain the position of factive understanding. All it takes is to accept that there are different "levels of abstraction" which still can (and do) give us factive understanding.
In addition to that, it should be noted that the whole debate on factive understanding also suffers from an inadequate distinction between the processes of abstraction and idealization (see Godfrey-Smith 2009 for a widely held account). As an example, consider a mechanistic model of an enzyme regulation, 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, or alternatively that it idealizes various factors. For instance, it ignores the distinction between competitive and non-competitive inhibition which can be considered both as abstraction and idealization.
Furthermore, a simple model often disregards the role of molar concentration. 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 concentration of the product is (see also Love and Nathan 2015 who consider ignoring concentrations as a case of idealization). Finally, I will briefly distinguish my approach to factive understanding from those of Reutlinger et al (2017) and Rice (2016).
I take it that in the terminology of Reutlinger et al (2017), a specific kind of models, embedded toy models, give us how-actually understanding (i.e. factive understanding) even though these models are highly idealized and simple. Their notion rests on the need for theory-driven de-idelization of the assumptions, however, and as such it importantly differs from my view which is free of such need.
Rice (2016) suggests that optimization models give us 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. Note that I am not disputing their views, but rather I am adding another type of cases in favor of factive understanding.