Abstract
ABSTRACT According to the hypothesis-generator account, valid extrapolations from a source to a target system are circular, since they rely on knowledge of relevant similarities and differences that can only be obtained by investigating the target, thus removing the need to extrapolate; hence, extrapolative reasoning can only be useful as a method for generating hypotheses. I reject this view in favour of an inferential account, focused on extrapolations underpinning the aggregation of experimental results, and explore two lines of argumentation supporting the conclusion that these extrapolations can be validated in a noncircular manner. The first argument relies on formal proofs of inferential validity demonstrating that it is possible to reason from prior knowledge of causal structures in order to determine whether a claim can be extrapolated. The second argument builds on the fact that the hypothesis-generator account overlooks key inferential and experimental practices resulting in progressively better-informed extrapolations.
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