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How to Reconcile a Unified Account of Explanation with Explanatory Diversity

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Abstract

The concept of explanation is central to scientific practice. However, scientists explain phenomena in very different ways. That is, there are many different kinds of explanation; e.g. causal, mechanistic, statistical, or equilibrium explanations. In light of the myriad kinds of explanation identified in the literature, most philosophers of science have adopted some kind of explanatory pluralism. While pluralism about explanation seems plausible, it faces a dilemma (Pincock in: Reutlinger A, Saatsi J (eds) Explanation beyond causation, Oxford University Press, Oxford, pp 39–56, 2018). Either there is nothing that unifies all instances of scientific explanation that makes them count as explanations, or there is some set of unifying features, which seems incompatible with explanatory pluralism. Different philosophers have adopted different horns of this dilemma. Some argue that no unified account of explanation is possible (Morrison in Reconstructing reality, Oxford University Press, Oxford, 2015). Others suggest that there is a set of necessary features that can unify all explanations under a single account (Potochnik in Idealization and the aims of science, Chicago University Press, Chicago, 2017; Reutlinger in Reutlinger A, Saatsi J (eds) Explanation beyond causation, Oxford University Press, Oxford, pp 74–95, 2018; Strevens in Depth: an account of scientific explanation, Harvard University Press, Cambridge, 2008). In this paper, we argue that none of the features identified by existing accounts of explanation are necessary for all explanations. However, we argue that a unified account can still be provided that accommodates pluralism. This can be accomplished, we argue, by reconceiving of scientific explanation as a cluster concept: there are multiple subsets of features that are sufficient for providing an explanation, but no single feature is necessary for all explanations. Reconceiving of explanation as a cluster concept not only accounts for the diversity of kinds of explanations, but also accounts for the widespread disagreement in the explanation literature and enables explanatory pluralism to avoid Pincock’s dilemma.

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Notes

  1. For example, some authors have focused on identifying distinctive features of particular kinds of explanation (Ariew et al. 2015; Batterman and Rice 2014; Lange 2012, 2013; Pincock 2012; Rice 2015; Sober 1983).

  2. Similarly, Lange (2012) distinguishes really statistical explanations from causal explanations.

  3. Reutlinger’s view is similar to counterfactual views defended by Rice (2015) and Saatsi and Pexton (2013) that aim to unify causal and noncausal explanations. However, only Reutlinger argues that a counterfactual account of explanation can unify all explanations under a single account.

  4. According to Wittgenstein these family resemblances provide “a complicated network of similarities overlapping and criss-crossing” (PI 66).

  5. For example, there are compelling counterexamples that seem to show that Hempel’s covering law and unificationist accounts are insufficient on their own.

  6. In addition, the CM-model was originally proposed by Salmon in 1984, but is similar to the account outlined by David Lewis in his Causal Explanation (Lewis 1986) and more contemporary process theories (Dowe 2007).

  7. While this condition is emphasized by process and mechanistic accounts, it is also present in any causal account that requires causes to precede their effects.

  8. For our purposes here, we will not be defending accounts of any of the particular kinds of explanation identified here. We only assume that some of these lists of features are sufficient to explain.

  9. Somewhat similarly, Lewis (2000) argues that preemption cases can be captured by counterfactual accounts (of causation) by suggesting that there will counterfactual dependencies between certain whether, when and how the cause occurs and whether, when and how the effect occurs. This might be so even if some of those changes to the cause fail to change the occurrence of the effect.

  10. What is more, assuming that Michael does not throw a rock in order to evaluate the counterfactual relations between Julie’s throwing a rock and the breaking of the window just removes the preemption from the scenario in order to establish a counterfactual dependence between Julie’s throwing the rock and the window breaking.

  11. Thanks to two anonymous reviewers for encouraging us to make this line of argument more explicit.

  12. It is also worth noting that, similar to our response to the preemption cases above, Lange (2012) argues that although there is causal information available in this case, that causal information is incidental to the explanation.

  13. It is worth noting that at least some mechanistic explanations are constitutive explanations that seem to be synchronic rather than diachronic (Craver 2006). However, as we will argue below, we think this is just further evidence that citing a temporal order of events is not necessary for all explanations. Thanks to an anonymous reviewer for bringing this other kind of synchronic explanation to our attention.

  14. While Lange (2014) and Povich (2018) have raised various objections to Batterman and Rice’s (2014) account of minimal model explanations, they do not disagree with the claim that the explanation that appeals to renormalization requires the thermodynamic limit. While we disagree with many of the criticisms raised by those authors, we don’t think this paper would be well served by getting into a more detailed debate about how minimal model explanations work given that all parties agree to the essential use of idealizations in these explanations. Indeed, the essential use of idealizations within explanations has been widely adopted by many authors who have focused on cases besides minimal model explanations (e.g. see Batterman 2002; Morrison 2015; or Wayne 2011). Our point here isn’t that these explanations provide no accurate information, but that they necessarily require a falsehood. As a result, we use the example to show that being completely true/accurate is not a necessary condition for all explanations.

  15. Or as physicist Leo Kadanoff (2000) puts it, “The existence of a phase transition requires an infinite system. No phase transitions occur in systems with a finite number of degrees of freedom” (238).

  16. There is a crucial difference here between arguing that explanations can involve essential idealizations and suggesting that explanations need not involve any truths. We argue only that it is not necessary for an explanation to contain only true statements. Moreover, we address the possibility that all explanations must involve some truth in Sect. 5.2.

  17. An anonymous reviewer suggested that perhaps a necessary feature of all explanations is that they provide information about the world. However, we think this feature is insufficient to distinguish explanations from non-explanations. After all, many things provide information about the world without explaining. Moreover, we think it is important to recognize that the reason explanations are valuable is because they provide the kinds of information about the world that are valuable to scientists—i.e. explanations are valuable because scientists find them valuable not just because they provide information.

  18. Moreover, if our account is correct, then we can provide an explanation for why the psychology of explanation literature suggests that there are multiple concepts of explanation employed in our everyday reasoning (Colombo 2017). That different philosophical theories of explanation all track some cases of our judgments of explanatory power is just what one would expect if explanation is a cluster concept.

  19. For example, Woody suggests that explanation “enforces communal norms regarding what sorts of information are to be considered intelligible and enlightening and the types of reasoning that are legitimate within the community” (Woody 2015, 86).

  20. For example, Pincock argues that “when a contrast is tied to a difference that could have been made through causes changing events, while fixing the constitutive character and the broader abstract structure, then a causal explanation in mandated…when a contrast invokes a difference between types of systems, then only an abstract explanation will cite the right kind of factor that is responsible for those differences across systems. Looking at the operations of causes or the internal constitution of the elements of the actual system will fail to make sense of that sort of contrast” (Pincock 2018, 51–52).

  21. There is, of course, much more to Pincock’s account of explanation. However, our focus here is on the feature he proposes as being the unifying feature of all explanations.

  22. While Reutlinger (2016) argues that his account can accommodate RG explanations, we think his presentation of that case rests on a misunderstanding of the crucial role played by the thermodynamic limit in allowing the renormalization transformation to arrive at a fixed point which enables physicists to identify the critical exponents that are essential to explaining the universality of the critical behaviors we observe. Rather than working through those details here, we refer interested readers to Batterman (2002, 2010) and Morrison (2009, 2015).

  23. We also think our account connects explanations with understanding; e.g. see Rohwer and Rice 2016 for a more detailed discussion of those connections.

  24. We actually think that Hochstein is committed to a kind of monism in that he argues that all explanations must satisfy the same criteria despite arguing that no single goal is most important. In other words, Hochstein seems to reject the explanatory pluralism that we aim to account for that claims that there are multiple different kinds of explanation provided in science.

References

  • Ariew, A., Rice, C., & Rohwer, Y. (2015). Autonomous statistical explanations and natural selection. The British Journal for the Philosophy of Science, 66(3), 635–658.

    Article  Google Scholar 

  • Ariew, A., Rohwer, Y., & Rice, C. (2017). Galton, reversion and the quincunx: The rise of statistical explanation. Studies in History and Philosophy of Biological and Biomedical Sciences, 66, 63–72.

    Article  Google Scholar 

  • Baker, A. (2009). Mathematical explanation in science. British Journal for the Philosophy of Science, 60, 611–633.

    Article  Google Scholar 

  • Batterman, R. W. (2002). The devil in the details: Asymptotic reasoning in explanation, reduction, and emergence. Oxford: Oxford University Press.

    Google Scholar 

  • Batterman, R. W., & Rice, C. (2014). Minimal model explanations. Philosophy of Science, 81(3), 349–376.

    Article  Google Scholar 

  • Bechtel, W., & Richardson, R. C. (1993). Discovering complexity: Decomposition and localization as strategies in scientific research. Princeton: Princeton University Press.

    Google Scholar 

  • Bokulich, A. (2011). How scientific models can explain. Synthese, 180, 33–45.

    Article  Google Scholar 

  • Bokulich, A. (2012). Distinguishing explanatory from nonexplanatory fictions. Philosophy of Science, 79, 725–737.

    Article  Google Scholar 

  • Bromberger, S. (1966). Questions. The Journal of Philosophy, 63(20), 597–606.

    Article  Google Scholar 

  • Colombo, M. (2017). Experimental philosophy of explanation rising: The case for a plurality of concepts of explanation. Cognitive Science, 41, 503–517.

    Article  Google Scholar 

  • Craver, C. F. (2006). When mechanistic models explain. Synthese, 153(3), 355–376.

    Article  Google Scholar 

  • Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Craver, C., & Darden, L. (2013). In search of mechanisms: Discoveries across the life sciences. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • Dowe, P. (2007). Physical causation. Cambridge: Cambridge University Press.

    Google Scholar 

  • Fagan, M. B. (2015). Collaborative explanation and biological mechanisms. Studies in History and Philosophy of Science, 52, 67–78.

    Article  Google Scholar 

  • Friedman, M. (1974). Explanation and scientific understanding. Journal of Philosophy, 71, 5–19.

    Article  Google Scholar 

  • Gijsbers, V. (2007). Why unification is neither necessary nor sufficient for explanation. Philosophy of Science, 74(4), 481–500.

    Article  Google Scholar 

  • Glennan, S. (2017). The new mechanical philosophy. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Hempel, C. (1965). Aspects of scientific explanation. New York: Free Press.

    Google Scholar 

  • Hempel, C., & Oppenheim, P. (1948). Studies in the logic of explanation. Philosophy of Science, 15(2), 135–175.

    Article  Google Scholar 

  • Hochstein, E. (2017). Why one model is never enough: a defense of explanatory holism. Biology and Philosophy, 32, 1105–1125.

    Article  Google Scholar 

  • Huneman, P. (2010). Topological explanations and robustness in biological sciences. Synthese, 177, 213–245.

    Article  Google Scholar 

  • Kadanoff, L. P. (2000). Statistical physics: Statics, dynamics, and renormalization. Singapore: World Scientific.

    Book  Google Scholar 

  • Khalifa, K., Doble, G., & Millson, J. (forthcoming). Counterfactuals and explanatory pluralism. The British Journal for the Philosophy of Science. https://doi-org.proxy.brynmawr.edu/10.1093/bjps/axy048.

  • Kaplan, D. M., & Craver, C. F. (2011). The explanatory force of dynamical and mathematical models in neuroscience: A mechanistic perspective. Philosophy of Science, 78, 601–627.

    Article  Google Scholar 

  • Kitcher, P. (1981). Explanatory unification. Philosophy of Science, 48(4), 507–531.

    Article  Google Scholar 

  • Kitcher, P. (1989). Explanatory unification and the causal structure of the world. In Philip Kitcher & Wesley Salmon (Eds.), Scientific explanation, minnesota studies in the philosophy of science (Vol. 13, pp. 410–505). Minneapolis: University of Minnesota Press.

    Google Scholar 

  • Lange, M. (2012). What makes a scientific explanation distinctively mathematical? The British Journal for the Philosophy of Science, 64(3), 485–511.

    Article  Google Scholar 

  • Lange, M. (2013). Really statistical explanations and genetic drift. Philosophy of Science, 80(2), 169–188.

    Article  Google Scholar 

  • Lange, M. (2014). On ‘minimal model explanations’: A reply to Batterman and Rice. Philosophy of Science, 82, 292–305.

    Article  Google Scholar 

  • Lewis, D. (1986). Causal explanation. In Philosophical papers (Vol. II). Oxford: Oxford University Press.

  • Lewis, D. (2000). Causation as influence. Journal of Philosophy, 97, 182–197.

    Article  Google Scholar 

  • Machamer, P. K., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.

    Article  Google Scholar 

  • Matthen, M., & Ariew, A. (2009). Selection and causation. Philosophy of Science, 76, 201–224.

    Article  Google Scholar 

  • Maynard Smith, J. (1982). Evolution and the theory of games. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Morrison, M. (2009). Understanding in physics and biology. In Henk W. de Regt, Sabina Leonelli, & Kai Eigner (Eds.), Scientific understanding: Philosophical perspectives. Pittsburgh: Pittsburgh University Press.

    Google Scholar 

  • Morrison, M. (2015). Reconstructing reality. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Pigliucci, M. (2003). Species as family resemblance concepts: the (dis-)solution of the species problem? BioEssays, 25(6), 596–602.

    Article  Google Scholar 

  • Pincock, C. (2012). Mathematics and scientific representation. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Pincock, C. (2018). Accommodating explanatory pluralism. In A. Reutlinger & J. Saatsi (Eds.), Explanation beyond causation (pp. 39–56). Oxford: Oxford University Press.

    Google Scholar 

  • Potochnik, A. (2007). Optimality modeling and explanatory generality. Philosophy of Science, 74(5), 680–691.

    Article  Google Scholar 

  • Potochnik, A. (2015). Causal patterns and adequate explanations. Philosophical Studies, 172(5), 1163–1182.

    Article  Google Scholar 

  • Potochnik, A. (2017). Idealization and the aims of science. Chicago: Chicago University Press.

    Book  Google Scholar 

  • Povich, M. (2018). Minimal models and the generalized ontic conception of scientific explanation. British Journal for the Philosophy of Science, 69, 117–137.

    Article  Google Scholar 

  • Reutlinger, A. (2016). Is there a monistic theory of causal and noncausal explanations? The counterfactual theory of scientific explanation. Philosophy of Science, 83(5), 733–745.

    Article  Google Scholar 

  • Reutlinger, A. (2018). Extending the counterfactual theory of explanation. In A. Reutlinger & J. Saatsi (Eds.), Explanation beyond causation (pp. 74–95). Oxford: Oxford University Press.

    Chapter  Google Scholar 

  • Rice, C. (2012). Optimality explanations: A plea for an alternative approach. Biology and Philosophy, 27(5), 685–703.

    Article  Google Scholar 

  • Rice, C. (2015). Moving beyond causes: Optimality models and scientific explanation. Noûs, 49(3), 589–615.

    Article  Google Scholar 

  • Rice, C. (2017). Models don’t decompose that way: A holistic view of idealized models. The British Journal for the Philosophy of Science. https://doi.org/10.1093/bjps/axx045.

    Article  Google Scholar 

  • Rice, C. (2018). Idealized models, holistic distortions and universality. Synthese, 195(6), 2795–2819.

    Article  Google Scholar 

  • Rice, C., Rohwer, Y., & Ariew, A. (2018). Explanatory schema and the process of model building. Synthese. https://doi.org/10.1007/s11229-018-1686-y.

    Article  Google Scholar 

  • Rohwer, Y., & Rice, C. (2016). How are models and explanations related? Erkenntnis, 81, 1127–1148.

    Article  Google Scholar 

  • Saatsi, J., & Pexton, M. (2013). Reassessing woodward’s account of explanation: Regularities, counterfactuals, and non-causal explanations. Philosophy of Science, 80, 613–624.

    Article  Google Scholar 

  • Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Salmon, W. (1989). Four decades of scientific explanation. Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

  • Salmon, W. C. (1994). Causality without counterfactuals. Philosophy of Science, 16, 297–312.

    Article  Google Scholar 

  • Salmon, W. C. (1997). Causality and explanation: A reply to two critiques. Philosophy of Science, 64(3), 461–477.

    Article  Google Scholar 

  • Sober, E. (1983). Equilibrium explanation. Philosophical Studies, 43, 201–210.

    Article  Google Scholar 

  • Strevens, M. (2004). The causal and unification approaches to explanation unified-causally. Nous, 38(1), 154–176.

    Article  Google Scholar 

  • Strevens, M. (2008). Depth: An account of scientific explanation. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • van Fraassen, B. C. (1980). The scientific image. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Walsh, D. M. (2007). The pomp of superfluous causes: The interpretation of evolutionary theory. Philosophy of Science, 74(3), 281–303.

    Article  Google Scholar 

  • Walsh, D. M. (2010). Not a sure thing: Fitness, probability, and causation. Philosophy of Science, 77(2), 147–171.

    Article  Google Scholar 

  • Walsh, D. M., Lewens, T., & Ariew, A. (2002). Trials of life: Natural selection and random drift. Philosophy of Science, 72, 311–333.

    Google Scholar 

  • Wayne, A. (2011). Expanding the scope of explanatory idealization. Philosophy of Science, 78, 83–841.

    Article  Google Scholar 

  • Wittgenstein, L. (1953/1973). Philosophical investigations. New York, NY: Macmillan.

  • Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.

    Google Scholar 

  • Woody, A. (2015). Re-orienting discussions of scientific explanation: A functional perspective. Studies in History and Philosophy of Science, 52, 79–87.

    Article  Google Scholar 

  • Wright, R. (2008). Environmental science: Toward a sustainable future. Upper Saddle River, NJ: Pearson Prentice Hall.

    Google Scholar 

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Authors and Affiliations

  1. Department of Philosophy, Bryn Mawr College, 101 N. Merion Ave, Bryn Mawr, PA, 19010, USA

    Collin Rice

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

    Yasha Rohwer

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  1. Collin Rice
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Rice, C., Rohwer, Y. How to Reconcile a Unified Account of Explanation with Explanatory Diversity. Found Sci 26, 1025–1047 (2021). https://doi.org/10.1007/s10699-019-09647-y

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