Three decades ago, James Bogen and James Woodward argued against the possibility and usefulness of scientific explanations of data. They developed a picture of scientific reasoning where stable phenomena were identified via data without much input from theory. Rather than explain data, theories ‘save the phenomena’. In contrast, I argue that there are good reasons to explain data, and the practice of science reveals attempts to do so. I demonstrate that algorithms employed to address inverse problems in remote-sensing applications should (...) be understood as attempts to identify phenomena by explaining the data. Thus, this paper furthers understanding of data-to-phenomena reasoning in science, and demonstrates theory may play a more central role in phenomena identification than previously recognized. (shrink)

The basic theory of scientific understanding presented in Sections 1–2 exploits three main ideas.First, that to understand a phenomenonP (for a given agent) is to be able to fitP into the cognitive background corpusC (of the agent).Second, that to fitP intoC is to connectP with parts ofC (via arguments in a very broad sense) such that the unification ofC increases.Third, that the cognitive changes involved in unification can be treated as sequences of shifts of phenomena inC. How the theory fits (...) typical examples of understanding and how it excludes spurious unifications is explained in detail. Section 3 gives a formal description of the structure of cognitive corpuses which contain descriptive as well as inferential components. The theory of unification is then refined in the light of so called puzzling phenomena, to enable important distinctions, such as that between consonant and dissonant understanding. In Section 4, the refined theory is applied to several examples, among them a case study of the development of the atomic model. The final part contains a classification of kinds of understanding and a discussion of the relation between understanding and explanation. (shrink)

Ist der Unterschied zwischen wissenschaftlichen Gesetzen und Theorien ein qualitativer oder lediglich von quantitativer Art? Der Autor versucht zu zeigen, daß Gesetze und Theorien fundamental verschieden sind und daß die Kenntnis ihrer verschiedenen Natur notwendig für ein richtiges Wissenschaftsverständnis ist. Aus seiner Sicht sind Theorien geistige Konstruktionen mit dem Ziel, kausale Erklärungen von empirischen Gesetzen zu geben, während diese Gesetze auf der Grundlage von Messungen entdeckt werden und die Tatsachen der Wissenschaft konstituieren. Erkenntnistheoretisch sind daher Theorien und Gesetze auf verschiedenen (...) Ebenen anzusiedeln: während Gesetze Wissen liefern, liefern Theorien Verstehen. Der Kern der Theorien besteht aus Modellen, die idealisierte Abstraktionen aus Zustandsarten darstellen. Theoretische Modelle konstituieren Ontologien, die kausale Mechanismen aufzeigen. Solche Ontologien betreffen den Bedeutungsaspekt des Gegenstandsbereichs, auf den die Theorie angewendet wird, während empirische Gesetze den Ausdrucksaspekt des gleichen Gegenstandsbereichs betreffen. Theorien erklären Gesetze, indem sie zeigen, wie der Ausdrucksaspekt lediglich der natürliche Ausdruck der Auswirkungen der Kausalmechanismen in Bedeutungsaspekt sind. (shrink)

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)

Evan Fales has recently argued that, although I provide the most promising approach for those concerned to defend belief in divine intervention, I nevertheless fail to show that such belief can be rational. I argue that Fales’ objections are unsuccessful.