This paper describes the development of theories of scientific explanation since Hempel's earliest models in the 1940ies. It focuses on deductive and probabilistic whyexplanations and their main problems: lawlikeness, explanation-prediction asymmetries, causality, deductive and probabilistic relevance, maximal specifity and homogenity, the height of the probability value. For all of these topic the paper explains the most important approaches as well as their criticism, including the author's own accounts. Three main theses of this paper are: (1) Both deductive and probabilistic explanations (...) are important in science, not reducible to each other. (2) One must distinguish between (cause giving) explanations and (reason giving) justifications and predictions. (3) The adequacy of deductive as well as probabilistic explanations is relative to a pragmatically given background knowledge-which does not exclude, however, the possibility of purely semantic models. (shrink)

The present work constitutes an attempt to make explicit those pragmatic norms successfully operating in empirical science. I will first comment on the initial presuppositions of the discussion, in particular, on those concerning the instrumental character of scientific practice and the nature of scientific goals. Then I will depict the moderately naturalistic frame in which, from this approach, the pragmatic norms make sense. Third, I will focus on the specificity of the pragmatic norms, making special emphasis on what I regard (...) as a key idea underlying them, namely, the view, vigorously advocated by classical pragmatists like C. S. Peirce and G. Vailati, that the best test for objectivity is the test of action. Finally, I am going to put forward a tentative list of pragmatic norms that can be abstracted from a careful observation and analysis of scientific practice as provided by current philosophers of experimentation (A. Franklin and F. Steinle among others). The norms will be divided into four classes corresponding to four aspects of science in which they rule, that is, self-correction, prediction, explanation and both experimentation and computation. In the following account, the formulation of those pragmatic norms successfully governing science will be understood as a contribution that scientifically-oriented pragmatism can make to the normative naturalistic project in epistemology. (shrink)

The inference from determinism to predictability, though intuitively plausible, needs to be qualified in an important respect. We need to distinguish between two different kinds of predictability. On the one hand, determinism implies external predictability , that is, the possibility for an external observer, not part of the universe, to predict, in principle, all future states of the universe. Yet, on the other hand, embedded predictability as the possibility for an embedded subsystem in the universe to make such predictions, does (...) not obtain in a deterministic universe. By revitalizing an older result—the paradox of predictability —we demonstrate that, even in a deterministic universe, there are fundamental, non-epistemic limitations on the ability of one subsystem embedded in the universe to predict the future behaviour of other subsystems embedded in the same universe. As an explanation, we put forward the hypothesis that these limitations arise because the predictions themselves are physical events which are part of the law-like causal chain of events in the deterministic universe. While the limitations on embedded predictability cannot in any direct way show evidence of free human agency, we conjecture that, even in a deterministic universe, human agents have a take-it-or-leave-it control over revealed predictions of their future behaviour. (shrink)