Abstract
Catherine Elgin has recently argued that a nonfactive conception of understanding is required to accommodate the epistemic successes of science that make essential use of idealizations and models. In this paper, I argue that the fact that our best scientific models and theories are pervasively inaccurate representations can be made compatible with a more nuanced form of scientific realism that I call Understanding Realism. According to this view, science aims at (and often achieves) factive scientific understanding of natural phenomena. I contend that this factive scientific understanding is provided by grasping a set of true modal information about the phenomenon of interest. Furthermore, contrary to Elgin’s view, I argue that the facticity of this kind of scientific understanding can be separated from the inaccuracy of the models and theories used to produce it.
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Notes
I do not claim that understanding is the only aim of science. I only claim that it is the primary epistemic aim that realists should be concerned with.
An important exception here is Potochnik (2017) that focuses on the way that diverse communities focused on different causal patterns can produce understanding. However, Potochnik’s discussion never really addresses the realism debate and, in contrast with the view I defend here, she argues that the understanding produced by science is nonfactive.
Bas van Fraassen nicely summarizes this realist line of argument: “Science aims to find explanation, but nothing is an explanation unless it is true (explanation requires true premises); so science aims to find true theories about what the world is like. Hence scientific realism is correct” (van Fraassen 1980, p. 97). van Fraassen, of course, goes on to deny that in order to explain a theory must be true, but he is correct in characterizing the standard realist reasoning as requiring that explanations be provided by true theories (or models).
More generally, for mechanistic accounts, “the goal is to describe correctly enough (to model or mirror more or less accurately) the relevant aspects of the mechanisms under investigation” (Craver and Darden 2013, p. 94).
Moreover, Kvanvig claims that a key relationship between knowledge and understanding “is that both imply truth, that both are factives. To say that a person understands that p therefore requires that p is true” (Kvanvig 2003, p. 190). Stephen Grimm also suggests that, “our understanding of natural phenomena seems conspicuously factive—what we are trying to grasp is how things actually stand in the world” (Grimm 2006, p. 518).
As Markus Eronon and Raphael van Reil summarize the challenge: “On the one hand, understanding provided by scientific models seems to be genuine understanding, but on the other hand, it often seems to be non-factive, as the models involved are known to be literally false.” (Eronen and van Reil 2015, p. 3777).
Indeed, Massimi (2018) has recently argued that the supposed incompatibility of the use of multiple inconsistent models and realism depends on the implicit assumptions that the goal of modeling is “to establish a one-to-one mapping between relevant (partial) features of the model and relevant (partial)—actual or fictional—states of affairs about the target system” (Massimi 2018, p. 342).
Thanks to an anonymous reviewer for pushing me to make this point clearer.
Soazig Le Bihan explicates this idea in more detail in terms of knowing “how to navigate some of the possibility space associated with the phenomena (Le Bihan 2017, p. 112). Much of what follows is in agreement with that view although I focus more on how idealized scientific models can provide the kind of modal information required to understand.
There are, of course, other ways to improve one’s understanding as well.
While explanations might be better or worse, or perhaps can be deepened, whether or not an explanation has been provided is typically treated as a threshold concept.
Indeed, if someone had an extensive set of justified true beliefs about the Roman Empire (and various related counterfactual situations), but also believed that Rome was currently on the northern border of Italy, we would not thereby claim that they failed to understand the subject matter at all—although their understanding might be improved by correcting this false belief.
This is very close to de Regt and Gijsbers’s (2017) idea that non-veridical models can promote understanding by being useful for moving science forward.
Thanks to an anonymous reviewer for pressing me to make the connection with realism and the distinction with instrumentalism clearer here.
As physicist Leo Kadanoff puts it, “Whenever two systems show an unexpected or deeply rooted identity of behavior they are said to be in the same universality class” (Kadanoff 2013, p. 178).
I refer to a model system as the abstract system represented by a scientific model that includes all and only the features specified by the model (within a particular modeling context).
The challenge here is to say precisely which pieces of modal information ought to be retained across radical changes to the models and theories adopted by the scientific community.
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Acknowledgements
I am grateful to two anonymous reviewers whose comments on the paper greatly improved the final version. I would also like to thank Catherine Elgin for several discussions that have helped improve my thinking on these topics.
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Rice, C. Understanding realism. Synthese 198, 4097–4121 (2021). https://doi.org/10.1007/s11229-019-02331-5
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DOI: https://doi.org/10.1007/s11229-019-02331-5