This paper adds to the philosophical literature on mechanistic explanation by elaborating two related explanatory functions of idealisation in mechanistic models. The first function involves explaining the presence of structural/organizational features of mechanisms by reference to their role as difference-makers for performance requirements. The second involves tracking counterfactual dependency relations between features of mechanisms and features of mechanistic explanandum phenomena. To make these functions salient, we relate our discussion to an exemplar from systems biological research on the mechanism for countering heat shock—the heat shock response (HSR) system—in Escherichia coli (E. coli) bacteria. This research also reinforces a more general lesson: ontic constraint accounts in the literature on mechanistic explanation provide insufficiently informative normative appraisals of mechanistic models. We close by outlining an alternative view on the explanatory norms governing mechanistic representation.
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Contrast this with the term abstraction, which is often used to refer to the omission of details without such intentional misrepresentation (Jones 2005). If omissions result in misrepresentations of a target system, and if these misrepresentations are intended, argues Godfrey-Smith (2006), they also counts as an idealisation. We will follow this usage as well as the qualification, which looms large in the case studies we discuss. More generally, such idealisations by omission are frequent in scientific practice (Potochnik 2017).
HSR can also be triggered by non-thermal stress and pharmacological agents. For present purposes, focusing on thermal stress-induced HSR is sufficient.
Let us stress that the mutant models in this research should not be understood as representations of alternative ways in which the E.coli HSR system could be organized, and thus have different non-existing targets. The mutant models do misrepresent wild type E.coli HSR systems because they are used to say something about that target, viz. that certain characteristics are present in the target system because these improve functionality. The researchers’ aim is not to explore possible design variants so as to come up with a story about what the actual design looks like and how it functions. That is known from the start in their research; rather, they want to know why the target system functions the way it does. Given this request for explanation, it seems clear that the E.coli HSR wild type system is the target system driving the misrepresentations in the more unrealistic models.
We do not claim completeness for our analysis: likely, there are other explanatory functions served by intentional misrepresentations of mechanistic difference-makers than the ones we and Love and Nathan (2015) identified. Our aim here was just to elaborate two salient and important ones.
Determining which variables and dependencies ought to be articulated in a given mechanistic model is a different matter. Levy and Bechtel (2013) pitch their account of explanatory relevance against the views of e.g., Craver (2007). One of us argues (van Eck 2015a, 2017) that these sets of authors in fact endorse compatible rather than competing positions; they subscribe to different notions of difference-making, which are suitable for different explanatory requests.
It might be that theorists committed to this accuracy perspective sanction the use of idealizations that distort irrelevant features; and it might also be that some of those theorists concede that idealizations may distort explanatorily relevant features as long as this is required to accurately represent more important explanatorily relevant features. But the concession contradicts the norm that mechanistic explanations should describe as accurately as possible the relevant ontic structures in the world; what is then minimally required is a theory of ‘relative’ explanatory importance that explains when, and when not, the distortion of explanatorily relevant features is sanctioned. As long as such a theory is not on offer, the second concession seems to entail giving up on the accuracy constraint. Also, given that organization is key to the operation of mechanisms, key features are distorted in the cases analysed here, not relatively minor details. Thus, the accuracy perspective would still be hard pressed to account for the cases of idealization discussed here.
The term explain here refers both to design explanations that hinge on the comparison of mechanistic models and to the articulation of counterfactual dependencies using mechanistic models. On a side note, models that represent target mechanisms can be used both for mechanistic explanatory purposes (as in the latter case) and for non-mechanistic explanatory purposes (as in the former case). Also, when one is inclined to think that both explanatory purposes are mechanistic (cf. Matthiessen 2017), explain still has a twofold sense.
We hasten to say that we do not take our suggestions to be the only way to spell out explanatory norms on (mechanistic) explanations. We do take our suggestions to embody widely endorsed commitments in the explanation literature and to enable accounting for the positive role of idealizations in mechanistic modeling, something which ontic constraint views have a hard time accommodating.
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Funding was provided to Dingmar van Eck by the Research Foundation Flanders (FWO). We thank our reviewers and the guest editors of this special issue for very useful comments and suggestions.
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van Eck, D., Wright, C. Mechanist idealisation in systems biology. Synthese (2020). https://doi.org/10.1007/s11229-020-02816-8
- Mechanistic model
- Heat shock response
- Systems biology
- Mechanistic explanation