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High-Level Exceptions Explained

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Abstract

Why are causal generalizations in the higher-level sciences “inexact”? That is, why do they have apparent exceptions? This paper offers one explanation: many causal generalizations cite as their antecedent—the \(F\) in \(Fs\,\, {\textit{are}}\,\, G\)—a property that is not causally relevant to the consequent, but which is rather “entangled” with a causally relevant property. Entanglement is a relation that may exist for many reasons, and that allows of exceptions. Causal generalizations that specify entangled but causally irrelevant antecedents therefore tolerate exceptions.

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Notes

  1. On some of these accounts, the generalizations will be what Earman et al. (2002) call “non-lazy” and what Reutlinger et al. (2011) call “indefinite”; on others, they will be lazy/definite. The issues are, however, beyond the scope of the present paper.

  2. For Spohn and Nickel, what counts as normal depends in part on practitioners’ aims and assumptions. Schurz proposes that science should “reconstruct” generalizations framed in terms of normal conditions so as to eliminate their normality riders in favor of a stochastic connection between antecedent and consequent, or in other words, that narrowing generalizations should be converted into what I will call soft generalizations; see also Schurz (2014).

  3. That is, two dice yield two ‘1’s. The Romans called this the dog throw, though non-black dogs are hardly rarities.

  4. Armstrong’s connections are not in the first instance causal; they are rather relations of necessitation that (according to his later work) when instantiated manifest themselves as relations of singular causation.

  5. It is an open question whether obesity itself reinforces the mechanism by which glucose is over-produced. If it does then obesity is after all directly causal, but the important point here is that the causal generalization is assertible even if obesity is merely an effect of the real cause(s) of type 2 diabetes.

  6. On the evidence for the existence of this causal belief in naive biology, see Strevens (2000) and Gelman (2003). Some psychologists have thought that biological naïfs are essentialists who understand the property of ravenhood itself as causing the characteristic properties of ravens: their color, their behavior, and so on. Strevens provides evidence against this claim and in favor of the view stated in the main text: normal, biologically uninformed individuals believe that there is something about ravens that causes blackness, but they are not committed to the cause’s being the essence of ravenhood.

  7. On the grounding of these and other such counterfactuals in actual, scientifically significant properties of ravens and their environment, and their connection to the counterfactual support offered by the causal generalization as a whole, see Strevens (2008b).

  8. It does not logically follow that they lack \(G\): they may have \(G\) for some other reason, like a leucistic raven dyed black. Such individuals conform to the generalization but are not instances of the generalization, because their \(G\)-ness is not explained in the right way.

  9. Except in a perverse scenario in which \(C\) holds only in situations where the entanglement falls through.

  10. I further suggest that the term “exception” should be applied, in a stochastic context, not to cases where an \(F\) lacks \(G\) but to cases where, because of local circumstances, an \(F\)’s probability of being \(G\) departs from the probability asserted by the generalization—for example, where the probability is small though the generalization asserts that it is large. Using this terminology, negative instances and exceptions both arise from softness, but softness of different sorts.

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Acknowledgments

Thank you to Laura Franklin-Hall, the editors, and two anonymous referees.

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Correspondence to Michael Strevens.

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Strevens, M. High-Level Exceptions Explained. Erkenn 79 (Suppl 10), 1819–1832 (2014). https://doi.org/10.1007/s10670-014-9644-7

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