Abstract
Why do we value higher-level scientific explanations if, ultimately, the world is physical? An attractive answer is that physical explanations often cite facts that don’t make a difference to the event in question. I claim that to properly develop this view we need to commit to a type of deterministic chance. And in doing so, we see the theoretical utility of deterministic chance, giving us reason to accept a package of views including deterministic chance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
There are other versions of this idea present in the literature. For example, Franklin-Hall (fort.) gives an ‘informational economy account’ that favors less informative explanations, though, unlike the difference-making account, it doesn’t come with a story about why less informative explanations should be favored. Clarke (2016, section 3) also favors less informative explanations, but his story is based on Gricean norms about the pragmatics of conversation, and as such the account is probably better classified as a version of the pragmatic approach mentioned above. Though clearly, a fair consideration of these accounts would take much, much longer.
There are a vast number of accounts of the value of higher-level explanations that I am putting aside. For example, there are approaches that emphasize how certain high-level explanations are appropriate answers to certain ‘why-questions’ and not to others [e.g. van Fraassen (1980, pp. 129–130), Potochnik (2010)]. There are accounts that focus on the generality of some law, or similar principle, that is part of the explanation [e.g. Clarke (2016, section 2), Hitchcock and Woodward (2003)]. There are those that appeal to the the institutional structure of science the corresponding division of scientific labor [e.g. Strevens (2016), Potochnik (2017)], and there are other approaches. I think there are reasons to favor the difference-making approach over these accounts, but clearly, these accounts cannot be properly investigated here.
There has to be a restriction on the type of entailment here in order to avoid derivations—like that of the height of the flagpole from the length of the shadow—that are obviously not relevant to explanation.
Strevens does go on to add further nuances to his account, allowing that in some situations explanations need not entail the explanandum—for example, he develops a theory of probabilistic explanation. But none of these seem to help in the cases under consideration.
We would have to change the form of this argument very slightly if the focus of the discussion was Woodward’s (2010) account of proportionality since his view takes variables to be the relata of explanations. But this is a simple fix.
Another way to put the suggestion: Add a ceteris paribus clause. So, for example we could say that ceteris paribus the fact that the peas were bred from parent plants that were homozygous in the allele associated with smoothness explains their smoothness. The idea being that we push some of the difference-makers—like the fact that there was no mutation—into the ceteris paribus clause. This is just another way of saying that we don’t need to explicitly cite all the difference-makers.
robustness (and precision which will be discussed in the next section) were stated and used, though not fully developed or defended (and not connected to issues of difference-making) in Bhogal (2020, pp. 687–689). The discussion of the rest of this section, and the next, develops the discussion there. (Or, perhaps it’s more accurate to say that the discussion there follows the discussion here.)
This dimension is similar to the account of ‘explanatory depth’ given by Weslake (2010).
Of course, the good economic explanation here doesn’t score maximally, or close to maximally on robustness, since there are other economic reasons that the dollar could decline, other than a fall in interest rates. But still, it’s clear that it scores much better on robustness than the microphysical explanation. More generally, good explanations that we see in the special sciences will typically not score close to maximally on robustness, but will score much better that explanations that are pitched at too low a level.
This is only true ‘typically’ because it is possible that the explanans holds in a similar (or wider) range of worlds as the explanandum, but only explains the explanandum in a few of these worlds. In this case, the explanation would score low on robustness. Even in such cases, though, I think it’s correct to say that the explanans is not required or close to required for the explanandum, in the sense of ‘required’ that is natural in discussions of explanation.
However, given the extremely minimal account of explanatory correctness we are working with, these cases will be very rare so, for the most part, I will ignore them in what’s to come. (Though see footnote 14 for a closely related point about the next dimension of explanatory goodness.
Or at least, the simple version of Strevens’ account does this, ignoring the nuances mentioned in footnote 5.
Again, assuming determinism.
In fact, precision is a particularly attractive measure of this sense of expectability—more attractive, for example, than the probability of the explanandum conditional on the explanans. The expectability intuition is that the explanation should render the explanandum expectable. If B has a high probability conditional on A this might be true for reasons that are totally independent of any explanation of A from B. But, if the precision of an explanation of B from A is high then this means that given A we should expect B in virtue of the explanatory connection between A and B.
Relatedly, the account avoids certain Hempelian problems for this reason. Take, for example, the classic case of an attempting to explain the high of a flagpole by appealing to the position of the sun and the length of the shadow that flagpole casts. Because precision builds in the notion of explanatory correctness it gets the right result here. In none of the physically possible worlds where the sun is in that position and the shadow is that length do those facts explain the height of the flagpole. So, this putative explanation scores zero on precision.
In this way the view is reminiscent of Sober (1999) in that there are two dimensions that scientists weight differently.
A brief methodological aside: Ultimately, we are trying to understand and account for a feature of scientific practice—we are trying to make sense of the way certain levels of explanation are taken in scientific practice to be acceptable for a particular explanandum and some are not. This is not a particularly sharp phenomenon—there are no bright lines to be seen in the practice between explanations that clearly acceptable and those that are not. Disagreement and vagueness abound. So, we should not expect, and not desire, our philosophical account to be totally free of vagueness and to draw bright lines.
References
Albert, D. Z. (2000). Time and chance. Harvard: Harvard University Press.
Bhogal, H. (2020). Coincidences and the grain of explanation. Philosophical and Phenomenological Research, 100(3), 677–694.
Bigelow, J., Collins, J., & Pargetter, R. (1993). The Big Bad Bug: What Are the Humean’s chances? British Journal for the Philosophy of Science, 44(3), 443–462.
Clarke, C. (2016). The explanatory virtue of abstracting away from idiosyncratic and messy detail. Philosophical Studies, 173(6), 1429–1449. https://doi.org/10.1007/s11098-015-0554-6.
Emery, N. (2017). The metaphysical consequences of counterfactual skepticism. Philosophy and Phenomenological Research, 94(2), 399–432.
Franklin-Hall, L. R. (2016). High-level explanation and the interventionist’s ’variables problem. British Journal for the Philosophy of Science, 67(2), 553–577.
Franklin-Hall, L. R. (fort.). The casual economy account of scientific explanation. Minnesota Studies in the Philosophy of Science.
Garfinkel, A. (1981). Forms of explanation. New Haven: Yale University Press.
Glynn, L. (2010). Deterministic chance. British Journal for the Philosophy of Science, 61(1), 51–80.
Hempel, C. (1965). Aspects of scientific explanation and other essays in the philosophy of science. New York: The Free Press.
Hitchcock, C., & Woodward, J. (2003). Explanatory generalizations, part II: Plumbing explanatory depth. Noûs, 37(2), 181–199.
Ismael, J. T. (2009). Probability in deterministic physics. Journal of Philosophy, 106(2), 89–108.
Jackson, F., & Pettit, P. (1992). In defense of explanatory ecumenicalism. Economics and Philosophy, 8(1), 1–21.
Kitcher, P. (1984). 1953 and all that. A tale of two sciences. Philosophical Review, 93(3), 335–373.
Kitcher, P. (2001). Science, Truth, and Democracy. Oxford University Press.
Lewis, D. (1986a). Causal explanation. In D. Lewis (Ed.), Philosophical papers (Vol. II, pp. 214–240). Oxford: Oxford University Press.
Lewis, D. (1986b). Philosophical papers (Vol. 2). Oxford: Oxford University Press.
Loewer, B. (2001). Determinism and chance. Studies in History and Philosophy of Science Part B, 32(4), 609–620. https://doi.org/10.1016/S1355-2198(01)00028-4.
Loewer, B. (2004). David Lewis’s Humean theory of objective chance. Philosophy of Science, 71, 1115–1125. https://doi.org/10.1086/428015.
Lyon, A. (2011). Deterministic probability: Neither chance nor credence. Synthese. http://www.springerlink.com/index/P52NG7RN8374W41P.pdf.
Maudlin, T. (2011). Three roads to objective probabilty. In S. Hartmann & C. Beisbart (Eds.), Probabilities in physics. Oxford: OUP.
Meacham, C. (2010). Contemporary approaches to statistical mechanical probabilities: A critical commentary—Part I: The indifference approach. Philosophy Compass. https://doi.org/10.1111/j.1747-9991.2010.00356.x/full.
Potochnik, A. (2010). Levels of explanation reconceived. Philosophy of Science, 77(1), 59–72.
Potochnik, A. (2017). Idealization and the aims of science. Chicago: University of Chicago Press.
Salmon, W. C. (1989). Four decades of scientific explanation. Minnesota Studies in the Philosophy of Science, 13, 3–219.
Schaffer, J. (2007). Deterministic chance? British Journal for the Philosophy of Science, 58(2), 113–140.
Sober, E. (1999). The multiple realizability argument against reductionism. Philosophy of Science, 66(4), 542–564.
Strevens, M. (2008). Depth: An account of scientific explanation. Harvard: Harvard University Press.
Strevens, M. (2016). Special-science autonomy and the division of labor. In M. Couch & J. Pfeifer (Eds.), The philosophy of Philip Kitcher. Oxford: Oxford University Press.
van Fraassen, B. (1980). The scientific image. Oxford: Oxford University Press.
Weslake, B. (2010). Explanatory depth. Philosophy of Science, 77(2), 273–294.
Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.
Woodward, J. (2010). Causation in biology: Stability, specificity, and the choice of levels of explanation. Biology and Philosophy, 25(3), 287–318.
Woodward, J. (2018). Explanatory autonomy: The role of proportionality, stability, and conditional irrelevance. Synthese. https://doi.org/10.1007/s11229-018-01998-6.
Yablo, S. (1992). Mental causation. The Philosophical Review, 101(2), 245–280.
Acknowledgements
Thanks to Rosa Cao, Laura Franklin-Hall, Ben Holguin, Barry Loewer, Tim Maudlin, Sam Scheffler, Brad Weslake, Mike Zhao and to audiences at the BSPS annual conference, the University of Maryland, and NYU Thesis Prep. Special thanks to Michael Strevens.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bhogal, H. Difference-making and deterministic chance. Philos Stud 178, 2215–2235 (2021). https://doi.org/10.1007/s11098-020-01538-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11098-020-01538-4