Fenton-Glynn, L; (2017) Is There High-Level Causation? Ergo , 4 , Article 30. 10.3998/ergo.12405314.0004.030.

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Abstract

The discovery of causal relations seems a central activity of the high-level sciences, including the special sciences and certain branches of macrophysics. Those same sciences are less successful in formulating exceptionless laws. If causation must be underwritten by exceptionless laws, we are faced with a puzzle. Attempts have been made to dissolve this puzzle by showing that non-exceptionless generalizations can underwrite causal relations. The trouble is that many of these attempts fail to distinguish between two importantly different types of exception of which high-level scientific generalizations admit. Roughly speaking, one is where the values of high-level variables not represented in the generalization are abnormal: call these 'background factor' (bf) exceptions. For example, the Ideal Gas Law (IGL) may be significantly violated by a gas if a strong electric current is passed through it. Another is where the high-level states that are represented by variables in the generalization are realized in certain abnormal ways: call these 'mr exceptions' (exceptions having to do with the multiple realizability of high-level states). For example, the pressure of a gas may not be proportional to its temperature and volume in the way that the IGL describes if the initial macrostate of the gas is realized in a certain unusual microphysical way. While existing attempts to show that non-exceptionless generalizations can underwrite causal relations tend to work well where the generalization admits only of bf exceptions, they work less well when the generalizations in question admit—as most high-level scientific generalizations do—of mr exceptions. I argue that the best prospect for resolving the apparent problem posed by mr exceptions is to regard the generalizations which admit of them as approximations to probabilistic generalizations which don't, and which are themselves able to support relations of probabilistic causation.

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