Scientific Reasoning Is Material Inference: Combining Confirmation, Discovery, and Explanation

Abstract

Whereas an inference (deductive as well as inductive) is usually viewed as being valid in virtue of its argument form, the present paper argues that scientific reasoning is material inference, i.e., justified in virtue of its content. A material inference is licensed by the empirical content embodied in the concepts contained in the premises and conclusion. Understanding scientific reasoning as material inference has the advantage of combining different aspects of scientific reasoning, such as confirmation, discovery, and explanation. This approach explains why these different aspects (including discovery) can be rational without conforming to formal schemes, and why scientific reasoning is local, i.e., justified only in certain domains and contingent on particular empirical facts. The notion of material inference also fruitfully interacts with accounts of conceptual change and psychological theories of concepts.

Acknowledgements

I am indebted to Alan Love and two anonymous referees of ISPS for helpful comments on earlier versions of this paper. The work on this essay was funded with Standard Research Grant 410–2008–0400 by the Social Sciences and Humanities Research Council of Canada.

Notes

[1] For an account of what the problems with each inductive approach is, the reader is referred to Norton's detailed discussion, which groups different concrete inductive schemes into different families. In the case of Bayesianism, the issue is that it is a very general but thereby weak system, which becomes useful only once a large number of conditional probabilities are specified, based on concrete empirical content.

[2] As a referee pointed out, there are more sophisticated formal schemes of analogical reasoning than the one cited above, e.g., approaches in artificial intelligence that model relevance relations by quantitative measures along several dimensions (Ashley 1988). The application of such formal frameworks to concrete cases also requires empirical content about a specific domain, where the number of relevance dimensions and the measures of relevant similarity are computationally determined from information provided about known cases from this domain.

[3] In this context, Sellars (1953) criticized Carnap (1937) as a proponent of the standard notion of formal inference, as Carnap introduced empirical principles as explicit premises, more precisely, as P‐rules in his axiomatic system.