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How are Models and Explanations Related?

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Abstract

Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is related to the explanation of interest. Our analysis reveals that the epistemological relationships are not always dependent on the metaphysical relationships, contrary to what has been assumed by many philosophers of science. Moreover, we identify several importantly different ways that scientific models instantiate these relationships. We argue that our framework provides novel insights concerning the nature of models, explanation, idealization, and understanding.

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Notes

  1. This is not to suggest that Salmon and others who hold an ontic view of explanation are claiming that explanation always produces understanding—only that scientific understanding is typically produced by grasping scientific explanations.

  2. If one rejects a “big tent” view of explanation, and believes, for example, that only by citing causes can one explain, then our framework will simply be restricted to models that include causal information.

  3. We understand that this assumption is contentious, but below we discus how our analysis can be reworked for any individual who holds the ontic view of explanation.

  4. In addition, an important difference between these two views of explanation is that the epistemic view often ties explanation with the production of understanding in an audience, where as the ontic view suggests that explanations will sometimes provide little in the way of understanding. However, both approaches agree that the discovery of explanations typically produce understanding—though perhaps not in every particular case. In this paper we will adopt the view that explanations typically produce understanding.

  5. For example physical models like a model car or boat.

  6. For example, this would seem to follow if one adopts the Giere/Weisberg view that models are mere abstract objects and not propositions. However, there would still remain the question of how models are able to provide explanations or pick out the propositions that provide explanations.

  7. We would like to thank an anonymous reviewer for suggesting that we discus the Giere/Weisberg view.

  8. In addition, if one maintains that models are and only are abstract entities, then models cannot be explanations, since they are not propositional. However, one could easily modify the framework that follows, by focusing on the propositions invoked when applying models to real-world systems that can provide explanations. For the sake of simplicity, we will not discuss this settle distinction further here.

  9. While this rough characterization will allow just about anything to be a model—e.g. clouds, sticks in the dirt, salt and pepper shakers—many other characterizations of models are just as permissive. For example, if a model just is something that instantiates a relationship of similarity that emphasizes certain features (Weisberg 2013), then this would allow all the above examples to be models given the appropriate context. We think it would be rather presumptuous for philosophers to dictate what can count as a scientific model.

  10. For instance, when defining adaptationism, Elliott Sober claims that phenotypic traits of populations “can be explained by a model in which selection is described and nonselective processes are ignored” (Sober 2000, p. 124).

  11. Law is here being used in a very liberal sense, one that entails a causal relationship between the cannonball and the breaking of the window.

  12. Indeed, the fact that this example involves deduction from causal laws should not be taken to imply that we think deduction or causation is essential to how models provide explanations.

  13. Some accounts of explanation may prohibit the inclusion of superfluous propositions in an explanation. We think this requirement is too strong. Instead, we maintain that superfluous propositions can make an explanation worse than one that includes only necessary propositions—but both are able to successfully explain the event.

  14. This is similar to what Nancy Cartwright claims in her seminal book How the Laws of Physics Lie—if the laws are never true, then they cannot provide genuine scientific explanations (Cartwright 1983). Similarly if models are ubiquitously idealized, perhaps they are never explanations.

  15. The method of replacing idealizations with other assumptions comes from the literature discussing “robustness analysis” (Levins 1966; Weisberg 2006). In this literature, however, the replacement is not always tied to the model’s explanatory power as it is in Lehtinen and Kuorikoski’s account.

  16. Weisberg would actually disagree with the claim that a model is partially constituted or determined by the construal. However, the subtle differences between his view and ours will have to explicated elsewhere.

  17. Another option would be to maintain the truth requirement on explanations, but claim that these models are able to explain because they express the propositions needed to explain the phenomenon of interests—they just do so without accurately representing the model’s target system(s). For example, the model might provide the required information about which features matter and which features are irrelevant even if it does not accurately represent any dynamical process that produced the explanandum (Bokulich 2011; Rice 2015). In this way, the model could still express the propositions required to explain the phenomenon and so could still be identified as an explanation.

  18. As another example, Mäki (1992) discusses how idealizations in economics can be used to isolate the operation of some causal factor. The accurate representation of this isolated factor would, on our account, constitute a partial explanation.

  19. However, there is a debate in the epistemological literature about whether understanding implies (or is a species of) knowledge. A discussion of that debate, however, is beyond the scope of this paper.

  20. This is similar to Peter Railton’s (1981) notion of “explanatory information”, which provides information that reduces uncertainty about some part of the ideal explanatory text.

  21. Still, such models may turn out to be a partial explanation or an explanation, but, within the context of discovery, the model is epistemologically related to the explanation in that it provides important background beliefs.

  22. Pincock refers to these as “anchors” of the research program. We will refrain from making any strong claims about the necessity of anchors for demarcating research programs. However, Pincock’s notion of an anchor certainly seems to be one sufficient way of doing so in many cases of model-based science.

  23. Another excellent example is the widespread and successful use of the Price equation in numerous disciplines (Price 1970).

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Acknowledgments

This research was partially supported by a Lycoming College Professional Development Grant. In addition, the authors would like to thank Jason Leddington, Matthew Slater, Franz-Peter Griesmaier, Kyle Stanford, André Ariew, Christopher Pincock, and two anonymous reviewers for helpful comments and feedback on earlier versions of this work.

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  1. Department of Humanities and Social Sciences, Oregon Institute of Technology, 3201 Campus Drive, Klamath Falls, OR, 97601, USA

    Yasha Rohwer

  2. Department of Philosophy, Lycoming College, 700 College Place, Williamsport, PA, 17701, USA

    Collin Rice

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Correspondence to Yasha Rohwer.

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Rohwer, Y., Rice, C. How are Models and Explanations Related?. Erkenn 81, 1127–1148 (2016). https://doi.org/10.1007/s10670-015-9788-0

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