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 paper starts with the distinction between conjunction-of-parts accounts and disjunction-of-possibilities accounts to truthlikeness. In Sect. 3, three distinctions between kinds of truthlikeness measures are introduced: comparative versus numeric t-measures, t-measures for qualitative versus quantitative theories, and t-measures for deterministic versus probabilistic truth. These three kinds of truthlikeness are explicated and developed within a version of conjunctive part accounts based on content elements. The focus lies on measures of probabilistic truthlikeness, that are divided into t-measures for statistical probabilities and single (...) case probabilities. The logical notion of probabilistic truthlikeness can be treated as a subcase of deterministic truthlikeness for quantitative theories. In contrast, the epistemic notion of probabilistic truthlikeness creates genuinely new problems, especially for hypotheses about single case probabilities that are evaluated not by comparison to observed frequencies, but by comparison to the truth values of single event statements. By the method of meta-induction, competing theories about single case probabilities can be aggregated into a combined theory with optimal predictive success and epistemic truthlikeness. (shrink)

Eric Scerri has proposed an account of how reduction might be understood in chemistry. He claims to build on a general aspect of Popper's views which survives his otherwise heavy criticism, namely adherence to actual scientific practice. This is contrasted with Nagel's conception, which Scerri takes to be the philosopher's standard notion. I argue that his proposal, interesting though it is, is not so foreign to ideas in the tradition within which Nagel wrote as Scerri would have us believe. Moreover, (...) actual scientific practice can be commandeered in support of a holistic conception which Popper contrasted with what he saw as the admirable strivings towards reduction in science. (shrink)

The concept of "fitness" is a notion of central importance to evolutionary theory. Yet the interpretation of this concept and its role in explanations of evolutionary phenomena have remained obscure. We provide a propensity interpretation of fitness, which we argue captures the intended reference of this term as it is used by evolutionary theorists. Using the propensity interpretation of fitness, we provide a Hempelian reconstruction of explanations of evolutionary phenomena, and we show why charges of circularity which have been levelled (...) against explanations in evolutionary theory are mistaken. Finally, we provide a definition of natural selection which follows from the propensity interpretation of fitness, and which handles all the types of selection discussed by biologists, thus improving on extant definitions. (shrink)

It is argued that it is not sufficient to consider only the sentences included in the explanans and explanandum when determining whether they constitute an explanation, but these sentences must always be evaluated relative to a knowledge situation. The central criterion on an explanation is that the explanans in a non-trivial way increases the belief value of the explanandum, where the belief value of a sentence is determined from the given knowledge situation. The outlined theory of explanations is applied to (...) some well-known examples and is also compared to other theories of explanation. (shrink)

My purpose here is to elaborate the reasons I maintain that Salmon has not been completely successful in reporting the history of work on explanation. The most important limitation of his account is that it does not emphasize the critical necessity to embrace a suitable conception of probability in the development of the theory of probabilistic explanation.

Brute facts are facts that have no explanation. If we come to know that a fact is brute, we obviously don’t get an explanation of that fact. Nevertheless, we do make some sort of epistemic gain. In this essay, I give an account of that epistemic gain, and suggest that the idea of brute facts allows us to distinguish between the notion of explanation and the notion of understanding. I also discuss Eric Barnes’ (1994) attack on Friedman’s (1974) version of (...) the uni-fication theory of explanation. The nification theory asserts that scientific understanding results from minimizing the number of brute facts that we have to accept in our view of he world. Barnes claims that the unification theory cannot do justice to he notion of being a brute fact, because it implies that brute facts are gaps in our understanding of the world. I defend Friedman’s theory against Barnes’ critique. (shrink)

In a recent issue of The Monist, Alisa Bokulich argues that those who embrace an ontic conception of scientific explanation are committed to rejecting an explanatory role for idealized, i.e., deliberately false, models. Her argument is based on an inaccurate characterization of the ontic view. Indeed, her positive view of idealization embraces rather than opposes the ontic conception. Because Bokulich is not alone in this misunderstanding, an effort to diagnose and correct it might prevent scholars from talking past one another (...) and, hopefully, nudge future discussions down more inviting paths. (shrink)

Completeness is an important but misunderstood norm of explanation. It has recently been argued that mechanistic accounts of scientific explanation are committed to the thesis that models are complete only if they describe everything about a mechanism and, as a corollary, that incomplete models are always improved by adding more details. If so, mechanistic accounts are at odds with the obvious and important role of abstraction in scientific modelling. We respond to this characterization of the mechanist’s views about abstraction and (...) articulate norms of completeness for mechanistic explanations that have no such unwanted implications. _1_ Introduction _2_ A Balancing Act: When Do Details Matter? _3_ The Norms of Causal Explanation _4_ The Norms of Constitutive Explanation _5_ Salmon-Completeness _6_ From More Details to More Relevant Details _7_ Non-explanatory Virtues of Abstraction _8_ From Explanatory Models to Explanatory Knowledge _9_ Mechanistic Completeness Reconsidered _10_ Conclusion. (shrink)

Hempel's high probability requirement asserts that any rationally acceptable answer to the question 'Why did event X occur?' must offer information which shows that X was to be expected at least with reasonable probability. Salmon rejected this requirement in his S-R model. This led to a series of paradoxical consequences, such as the assertion that an explanation of an event can both lower its probability and make it arbitrarily low, and the assertion that the explanation of an outcome would have (...) qualified as an explanation of its non-occurrence as well. We argue that if inductive explanations are to be seen as generalizations of the causal-deterministic model, or if they are to be seen as satisfying the requirement--fulfilled by the D-N model--that explanations ought to identify certain features of the universe that are nomically responsible for the explanadum event, then the high probability requirement seems to be unacceptable. If this is so, a realistically inspired theory of inductive explanation will be committed to the paradoxes that follow from Salmon's model. (shrink)

Convincing disputes about explanatory reductionism in the philosophy of biology require a clear and precise understanding of what a reductive explanation in biology is. The central aim of this book is to provide such an account by revealing the features that determine the reductive character of a biological explanation. Chapters I-IV provide the ground, on which I can then, in Chapter V, develop my own account of explanatory reduction in biology: Chapter I reveals the meta-philosophical assumptions that underlie my analysis (...) of reduction, Chapter II introduces the previous debate about reduction in the philosophy of biology by pointing out the most crucial lessons one should learn from this debate, Chapter III critically discusses the two perspectives on explanatory reduction that have been proposed in the philosophy of biology so far, and Chapter IV clarifies in what ways the issue of reduction is entangled with the issue of explanation. In Chapter V I present my own account of explanatory reduction. My main claim is that reductive explanations in the biological sciences possess three main characteristics: they explain the behavior of a biological system S, first, exclusively in terms of factors that are located on a lower level of organization than S, second, by focusing on factors that are internal to S , and third, by describing the genuine parts of S only as “parts in isolation”. This account is ontic because it traces the reductivity of an explanation back to certain relations that exist in the world. (shrink)

Philosophical work on explanation has focused on the following two topics: theories of explanation, intended to enumerate necessary and sufficient conditions for explanation, and inference to the best explanation as the strongest form of justification for ontological or metaphysical claims. I critically examine the most important philosophical work in both of these areas and defend my own conclusions about the connections between explanation and ontology. I argue that all of our inferences about the nature of the world, in ontology or (...) metaphysics, presuppose criteria for acceptable explanation. I first examine the metaphysics of Plato, Aristotle, and Leibniz, arguing that their metaphysical reasoning was guided by assumptions about the nature of explanation. I also survey some recent work in ontology and find inference to the best explanation offered as the strongest available method of defending existence claims. To conclude this discussion, I offer an original argument for the thesis that all reasoning about existence claims, being nondeductive, presupposes some criteria for acceptable explanations, or a theory of explanation. Carl Hempel and Wesley Salmon have been two of the most influential philosophers offering theories of explanation, and I examine their work with the intention of discovering ontological or metaphysical assumptions shaping their theories of explanation. I argue that Hempel's theory rests on deterministic assumptions, and I argue the Salmon's theory of causality, which he admits supports his theory of explanation, is subject to empiricist criticisms. Theories of explanation typically rest on assumptions about the nature of the world, I argue, and I explain why this is so with an original theory of explanation connecting it to the psychological phenomenon of understanding. We rely on understanding in the identification of explanations, and understanding requires establishing a metaphysical context for an explanandum-event. I end by indicating the possibility of generating vicious justificatory circles through the reciprocal relationship of support between explanation and ontology, and I argue that vicious circles can be avoided only by grounding all ontological reasoning in a fundamental explanatory task of giving order to experience through a system of categories. (shrink)

According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects of scientific (...) explanation. In C. G. Hempel (Ed.), Aspects of scientific explanation (pp. 331–496). New York: Free Press; Kitcher (1989); Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25). My concern is with the minimal suggestion that an adequate philosophical theory of scientific explanation can limit its attention to the format or structure with which theories are represented. The representational subsumption view is a plausible hypothesis about the psychology of understanding. It is also a plausible claim about how scientists present their knowledge to the world. However, one cannot address the central questions for a philosophical theory of scientific explanation without turning one’s attention from the structure of representations to the basic commitments about the worldly structures that plausibly count as explanatory. A philosophical theory of scientific explanation should achieve two goals. The first is explanatory demarcation. It should show how explanation relates with other scientific achievements, such as control, description, measurement, prediction, and taxonomy. The second is explanatory normativity. It should say when putative explanations succeed and fail. One cannot achieve these goals without undertaking commitments about the kinds of ontic structures that plausibly count as explanatory. Representations convey explanatory information about a phenomenon when and only when they describe the ontic explanations for those phenomena. (shrink)

The three cardinal aims of science are prediction, control, and explanation; but the greatest of these is explanation. Also the most inscrutable: prediction aims at truth, and control at happiness, and insofar as we have some independent grasp of these notions, we can evaluate science’s strategies of prediction and control from the outside. Explanation, by contrast, aims at scientific understanding, a good intrinsic to science and therefore something that it seems we can only look to science itself to explicate.