When is conceptual change so significant that we should talk about a new theory, not a new version of the same theory? We address this problem here, starting from Gould’s discussion of the individuation of the Darwinian theory. He locates his position between two extremes: ‘minimalist’—a theory should be individuated merely by its insertion in a historical lineage—and ‘maximalist’—exhaustive lists of necessary and sufficient conditions are required for individuation. He imputes the minimalist position to Hull and attempts a reductio : (...) this position leads us to give the same ‘name’ to contradictory theories. Gould’s ‘structuralist’ position requires both ‘conceptual continuity’ and descent for individuation. Hull’s attempt to assimilate into his general selectionist framework Kuhn’s notion of ‘exemplar’ and the ‘semantic’ view of the structure of scientific theories can be used to counter Gould’s reductio , and also to integrate structuralist and population thinking about conceptual change. (shrink)

We argue for a naturalistic account for appraising scientific methods that carries non-trivial normative force. We develop our approach by comparison with Laudan’s (American Philosophical Quarterly 24:19–31, 1987, Philosophy of Science 57:20–33, 1990) “normative naturalism” based on correlating means (various scientific methods) with ends (e.g., reliability). We argue that such a meta-methodology based on means–ends correlations is unreliable and cannot achieve its normative goals. We suggest another approach for meta-methodology based on a conglomeration of tools and strategies (from statistical modeling, (...) experimental design, and related fields) that affords forward looking procedures for learning from error and for controlling error. The resulting “error statistical” appraisal is empirical—methods are appraised by examining their capacities to control error. At the same time, this account is normative, in that the strategies that pass muster are claims about how actually to proceed in given contexts to reach reliable inferences from limited data. (shrink)