Abstract
Scientists appeal to models when explaining phenomena. Such explanations are often dubbed model explanations or model-based explanations (short: ME). But what are the precise conditions for ME? Are ME special explanations? In our paper, we first rebut two definitions of ME and specify a more promising one. Based on this analysis, we single out a related conception that is concerned with explanations that are induced from working with a model. We call them ‘model-induced explanations’ (MIE). Second, we study three paradigmatic cases of alleged ME. We argue that all of them are MIE, upon closer examination. Third, we argue that this undermines the building consensus that model explanations are special explanations that, e.g., challenge the factivity of explanation. Instead, it suggests that what is special about models in science is the epistemology behind how models induce explanations.
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Notes
Examples for explorative functions of models are the following (cf. Rohwer and Rice 2016, pp. 1141–1144): (i) Some models enable the modeler to view the phenomenon of interest from a novel perspective. (ii) Some models function as aids to discovering the right kind of explanations needed for the phenomenon at hand. (iii) Some models are used to justify important background beliefs for formulating an explanation.
We do not consider the particularity of materialized models here. For an overview of different kinds of models, see, e.g., Frigg and Hartmann (2012) or Gelfert (2016). One might also consider model organisms, such as the fruit fly Drosophila melanogaster, to be models (cf. Gelfert 2016, pp. 2–3). They can be considered a simplified form of the organisms in question.
For an analysis of the nature of such according-to propositions, see, e.g., van Riel (2015).
One issue to be discussed is whether every proposition that is entailed by a proposition that is true according to the model is also part of the model’s propositional content. We remain neutral here.
There is much discussion about whether or how models can be considered a representation of their target objects (for an overview see Frigg and Hartmann 2012; for particular accounts see, e.g., Hughes 1997; Bailer-Jones 2003; Giere 2004; Elgin 2007; Suárez 2010; Downes 2011; Frigg and Nguyen 2018), whether models are akin to fiction (e.g., Godfrey-Smith 2009; Frigg 2010; Toon 2012) or concerned with possibilities (e.g., Grüne-Yanoff 2013), etc. However, as we argue below, these issues can be separated from dealing with the nature of model(-based) explanations.
The account Rohwer and Rice describe seems to be in line with van Riel’s definition of ME as explanations that are true according to a model (van Riel 2017).
It is controversial whether models represent their target phenomena (see fn. 6). But if so: The representational content could be an explanation if it can be expressed in terms of propositions.
In some cases of explanations of singular occurrences of phenomena, the model’s content might not contain descriptions of the phenomenon itself.
For a similar reason, we think that it is misleading to call such an analysis of model explanation a ‘representationalist account of model explanation’ (cf. Kennedy 2012; Jebeile and Kennedy 2015; Fang 2019). The basic idea of a representationalist account is that the model accurately represents the phenomena of interest (or at least a substantial part thereof). However, ME_Identity is not concerned with accurate or complete representation. Moreover, what Kennedy proposes as a ‘non-representationalist account of model explanation’ picks out model-induced explanations, as we argue further below.
Recall that the propositional content of a model might include all the entailed propositions, as well (cf. footnote 5).
ME_Core and ME_Identity are concerned with the case of a single model. In cases where one explains a phenomenon using multiple models at the same time, one would need to revise the definition such that a conjunction of the models’ core contents is identical to the core of the explanation. (Note that multi-scalar models with inconsistent sub-model assumptions typically explain different aspects of a larger phenomenon and thus do not provide a joint explanation.)
As one reviewer remarked, another interesting epistemic role might be the role of models in justifying the explanations of interest. Discussing the relation between justification and models is a topic in its own right. We don’t discuss it here.
Marchionni, for example, describes conceptions of ME as being between two opposite sides of a continuum (cf. Marchionni 2017, p. 609). We think that they are better described as two different conceptions for the reasons given in what follows.
This distinction is roughly related to Rohwer and Rice’s proposal to draw “[...] a distinction between a model being a stand-alone explanation [model explanation] versus merely being explanatory [model-induced explanation]” (Rohwer and Rice 2013, p. 335). But their notion of an ‘explanatory model’ is much weaker than our notion of a model-induced explanation. According to them, “[a]n explanatory model is one that produces scientific understanding relevant to answering a why question [...]” (Rohwer and Rice 2013, p. 335). By contrast, we demand that the results of working with the model are parts of the answers to the why-question and that using the model is decisive for obtaining the answers.
A brief methodological remark: Philosophers when discussing idealized models often assume that scientists actually succeed in doing what they claim they do. In particular, they take for granted that scientists correctly explain with at least some models (cf., e.g., Wayne 2011, pp. 831–832; Rice 2018, p. 2799). In this paper, we do not discuss whether this assumption is apt. Instead, we evaluate a conditional question: If scientists provide us with correct explanations: How do the models figure into such explanations?
Optimization models are also used in other disciplines, as Rice points out (Rice 2018, p. 2803).
We thank an anonymous reviewer for emphasizing this point.
There is a debate about the thermodynamic limit in philosophy of physics. Some argue that it is dispensable (e.g., Butterfield 2011; Norton 2012; for an overview see, e.g., Shech 2017). For some useful discussion see also, e.g., Shech (2013), Feintzeig (2017). For the sake of argument, we take for granted here that the thermodynamic limit is necessary.
(ii) might also give us a good reason to believe that we only need one explanation for the variety of the systems which exhibit the pattern.
Batterman and Rice also suggest that we can explain particular behaviors of fluids by pointing out that the fluids are in a particular universality class where all members exhibit these behaviors (Batterman and Rice 2014, p. 364). But, again, the explanatory information is the membership in the universality class and not some model information.
The more precise definitions of these kinds of model-based explanations are not important here and we also do not discuss the taxonomy’s adequacy. For criticisms of Bokulich’s account of structural model explanations, see, e.g., King (2016).
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Acknowledgements
Discussions with colleagues and advisors contributed to shaping the view that we defend in this article. We’re grateful to (in alphabetical order) Mark Alfano, Finnur Dellsén, Anna-Maria Asunta Eder, Catherine Elgin, Benjamin Feintzeig, Roman Frigg, Kareem Khalifa, Christian Nimtz, Juha Saatsi, Henrik Sova, Thomas Spitzley, Michael Strevens, Raphael van Riel, Kate Vredenburgh, and the participants of Thomas Spitzley’s and Christian Nimtz’s research groups. We also thank the audiences in Aarhus, Atlanta, Barcelona, Bochum, Bordeaux, Exeter, Ghent, Lund, Pärnu, and Seattle, as well as three anonymous reviewers for their constructive criticisms and suggestions. Insa Lawler gratefully acknowledges that part of her research for this article was funded by the Volkswagen Foundation for the project ’A Study in Explanatory Power’, by the German Academic Exchange Service (DAAD) for a research stay at New York University (2015–2016), and by an Emmy Noether Grant from the German Research Council (DFG), Reference No. BR 5210/1-1.
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Lawler, I., Sullivan, E. Model Explanation Versus Model-Induced Explanation. Found Sci 26, 1049–1074 (2021). https://doi.org/10.1007/s10699-020-09649-1
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DOI: https://doi.org/10.1007/s10699-020-09649-1