Erik Weber

When scientist investigate why things happen, they aim at giving an explanation. But what does a scientific explanation look like? In the first chapter (Theories of Scientific Explanation) of this book, the milestones in the debate on how to characterize scientific explanations are exposed. The second chapter (How to Study Scientific Explanation?) scrutinizes the working-method of three important philosophers of explanation, Carl Hempel, Philip Kitcher and Wesley Salmon and shows what went wrong. Next, it is the responsibility of current philosophers of explanation to go on where Hempel, Kitcher and Salmon failed. However, we should go on in a clever way. We call this clever way the pragmatic approach to scientific explanation and clarify briefly what this approach consists in. The third chapter (A Toolbox for Describing and Evaluating Explanatory Practices) elaborates the pragmatic approach by presenting a toolbox for analysing scientific explanation. In the last chapter (Examples of Descriptions and Evaluations of Explanatory Practices) the approach is illustrated with real-life examples of scientists aiming at explaining

In the literature on scientific explanation two types of pluralism are very common. The first concerns the distinction between explanations of singular facts and explanations of laws: there is a consensus that they have a different structure. The second concerns the distinction between causal explanations and uni.cation explanations: most people agree that both are useful and that their structure is different. In this article we argue for pluralism within the area of causal explanations: we claim that the structure of a causal explanation depends on the causal structure of the relevant fragment of the world and on the interests of the explainer.

Explanatory pluralism is the view that the best form and level of explanation depends on the kind of question one seeks to answer by the explanation, and that in order to answer all questions in the best way possible, we need more than one form and level of explanation. In the first part of this article, we argue that explanatory pluralism holds for the medical sciences, at least in theory. However, in the second part of the article we show that medical research and practice is actually not fully and truly explanatory pluralist yet. Although the literature demonstrates a slowly growing interest in non-reductive explanations in medicine, the dominant approach in medicine is still methodologically reductionist. This implies that non-reductive explanations often do not get the attention they deserve. We argue that the field of medicine could benefit greatly by reconsidering its reductive tendencies and becoming fully and truly explanatory pluralist. Nonetheless, trying to achieve the right balance in the search for and application of reductive and non-reductive explanations will in any case be a difficult exercise

In this article we criticize two recent articles that examinethe relation between explanation and unification. Halonen and Hintikka (1999), on the one hand,claim that no unification is explanation. Schurz (1999), on the other hand, claims that all explanationis unification. We give counterexamples to both claims. We propose a pluralistic approach to the problem:explanation sometimes consists in unification, but in other cases different kinds of explanation(e.g., causal explanation) are required; and none of these kinds is more fundamental.

In the literature on scientific explanation, there is a classical distinction between explanations of facts and explanations of laws. This paper is about explanations of laws, more specifically mechanistic explanations of laws. We investigate whether providing unificatory information in mechanistic explanations of laws has a surplus value. Unificatory information is information about how the mechanism that explains the law which is our target relates to other mechanisms. We argue that providing unificatory information can lead to explanations with more explanatory power and that it may lead to more strongly supported explanations.

Explanatory pluralism has been defended by several philosophers of history and social science, recently, for example, by Tor Egil Førland in this journal. In this article, we provide a better argument for explanatory pluralism, based on the pragmatist idea of epistemic interests. Second, we show that there are three quite different senses in which one can be an explanatory pluralist: one can be a pluralist about questions, a pluralist about answers to questions, and a pluralist about both. We defend the last position. Finally, our third aim is to argue that pluralism should not be equated with “anything goes”: we will argue for non-relativistic explanatory pluralism. This pluralism will be illustrated by examples from history and social science in which different forms of explanation (for example, structural, functional, and intentional explanations) are discussed, and the fruitfulness of our framework for understanding explanatory pluralism is shown.

Although interest in them is clearly growing, most professional historians do not accept thought experiments as appropriate tools. Advocates of the deliberate use of thought experiments in history argue that without counterfactuals, causal attributions in history do not make sense. Whereas such arguments play upon the meaning of causation in history, this article focuses on the reasoning processes by which historians arrive at causal explanations. First, we discuss the roles thought experiments play in arriving at explanations of both facts and contrasts. Then, we pinpoint the functions thought experiments fulfill in arriving at weighted explanations of contrasts

In a 2008 paper, Walmsley argued that the explanations employed in the dynamical approach to cognitive science, as exemplified by the Haken, Kelso and Bunz model of rhythmic finger movement, and the model of infant preservative reaching developed by Esther Thelen and her colleagues, conform to Carl Hempel and Paul Oppenheim’s deductive-nomological model of explanation (also known as the covering law model). Although we think Walmsley’s approach is methodologically sound in that it starts with an analysis of scientific practice rather than a general philosophical framework, we nevertheless feel that there are two problems with his paper. First, he focuses only on the deductivenomological model and so neglects the important fact that explanations are causal. Second, the explanations offered by the dynamical approach do not take the deductive-nomological format, because they do not deduce the explananda from exceptionless laws. Because of these two points, Walmsley makes the dynamical explanations in cognitive science appear problematic, while in fact they are not

The paper has two aims. First, to show that we need social mechanisms to establish the policy relevance of causal claims, even if it is possible to build a good argument for those claims without knowledge of mechanisms. Second, to show that although social scientists can, in principle, do without social mechanisms when they argue for causal claims, in reality scientific practice contexts where they do not need mechanisms are very rare. Key Words: social mechanisms • causal inference • social policy.

This article has three aims. The first is to give a partial explication of the concept of unification. My explication will be partial because I confine myself to unification of particular events, because I do not consider events of a quantitative nature, and discuss only deductive cases. The second aim is to analyze how unification can be reached. My third aim is to show that unification is an intellectual benefit. Instead of being an intellectual benefit unification could be an intellectual harm, i.e., a state of mind we should try to avoid by all means. By calling unification an intellectual benefit, we claim that this form of understanding has an intrinsic value for us. I argue that unification really has this alleged intrinsic value.

Although there is a consensus among philosophers of mathematics and mathematicians that mathematical explanations exist, only a few authors have proposed accounts of explanation in mathematics. These accounts fit into the unificationist or top-down approach to explanation. We argue that these models can be complemented by a bottom-up approach to explanation in mathematics. We introduce the mechanistic model of explanation in science and discuss the possibility of using this model in mathematics, arguing that using it does not presuppose a Platonist view of mathematics and allows one to gain insight into why a theorem is true by answering what-if-things-had-been-different questions

Instances of explanatory reduction are often advocated on metaphysical grounds; given that the only real things in the world are subatomic particles and their interaction, we have to try to explain everything in terms of the laws of physics. In this paper, we show that explanatory reduction cannot be defended on metaphysical grounds. Nevertheless, indispensability arguments for reductive explanations can be developed, taking into account actual scientific practice and the role of epistemic interests. Reductive explanations might be indispensable to address some epistemic interest answering a specific explanation-seeking question in the most accurate, adequate and efficient way. Just like explanatory pluralists often advocate the indispensability of higher levels of explanation pointing at the pragmatic value of the explanatory information obtained on these higher levels, we argue that explanatory reduction—traditionally understood as the contender of pluralism—can be defended in a similar way. The pragmatic value reductionist, lower level explanations might have in the biomedical sciences and the social sciences is illustrated by some case studies.

In a recent paper on realism and pragmatism published in this journal, Osmo Kivinen and Tero Piiroinen have been pleading for more methodological work in the philosophy of the social sciences—refining the conceptual tools of social scientists—and less philosophically ontological theories. Following this de-ontologizing approach, we scrutinize the debates on social explanation and contribute to the development of a pragmatic social science methodology. Analyzing four classic debates concerning explanation in the social sciences, we propose to shift the debate away from (a) the ontologizing defenses of forms of social explanation, and (b) a winner-takes-all-approach. Instead, we advocate (c) a pragmatic approach towards social explanation, elaborating a rigorous framework for explanatory pluralism detached from the debates on social ontology.

In this essay the authors explore the nature of efficient causal explanation in Newton’s "Principia and The Opticks". It is argued that: (1) In the dynamical explanations of the Principia, Newton treats the phenomena under study as cases of Hall’s second kind of atypical causation. The underlying concept of causation is therefore a purely interventionist one. (2) In the descriptions of his optical experiments, Newton treats the phenomena under study as cases of Hall’s typical causation. The underlying concept of causation is therefore a mixed interventionist/mechanicist one

In a recent article in this journal, Federica Russo and Jon Williamson argue that an analysis of causality in terms of probabilistic relationships does not do justice to the use of mechanistic evidence to support causal claims. I will present Ronald Giere's theory of probabilistic causation, and show that it can account for the use of mechanistic evidence (both in the health sciences—on which Russo and Williamson focus—and elsewhere). I also review some other probabilistic theories of causation (of Suppes, Eells, and Humphreys) and show that they cannot account for the use of mechanistic evidence. I argue that these theories are also inferior to Giere's theory in other respects

Some social scientists and philosophers (e.g., James Coleman and Jon Elster) claim that all social facts are best explained by means of a micro-explanation. They defend a micro-reductionism in the social sciences: to explain is to provide a mechanism on the individual level. The first aim of this paper is to challenge this view and defend the view that it has to be substituted for an explanatory pluralism with two components: (1) structural explanations of P-, O- and T-contrasts between social facts are more efficient than the competing micro-explanations; and (2) whether a plain social fact (as opposed to a contrast) is best explained in a micro-explanation or a structural explanation depends on the explanatory interest. The second aim of the paper is to show how this explanatory pluralism is compatible with ontological individualism. This paper is motivated by our conviction that explanatory pluralism as defended by Frank Jackson and Philip Pettit is on the right track, but must be further elaborated. We want to supplement their contribution, by (1) introducing the difference between explanations of facts and explanations of contrasts; (2) giving examples from the social sciences, instead of mainly from the natural sciences or common sense knowledge; and (3) emphasizing the pragmatic relevance of explanations on different levels –social, psychological, biological, etc. – which is insufficiently done by Jackson and Pettit.

In the past 25 years, many philosophers have endorsed the view that the practical value of causal knowledge lies in the fact that manipulation of causes is a good way to bring about a desired change in the effect. This view is intuitively very plausible. For instance, we can predict a storm on the basis of a barometer reading, but we cannot avoid the storm by manipulating the state of the barometer (barometer status and storm are effects of a common cause, viz. atmospheric conditions). In Section 1 we present textual evidence which shows that this view is very popular. In Section 2 we show that this standard view is too restrictive: the practical value of causal knowledge is wider. In Section 3 we introduce the distinction between ‘manipulative policy’ and ‘selective policy’ as a theoretical framework to account for this wider practical value.

In the current literature on scientific explanation unification became unfashionable in favour of causal approaches. We want to bring unification back into the picture. In this paper we demonstrate that resemblance questions do occur in scientific practice and that they cannot be properly answered without unification. Our examples show that resemblance questions about particular facts demand what we call causal network unification, while resemblance questions about regularities require what we call mechanism unification. We clarify how these types of unification relate to Philip Kitcher’s account, but also to causal and mechanistic accounts of explanation

In this paper we discuss three interrelated questions. First: is explanation in mathematics a topic that philosophers of mathematics can legitimately investigate? Second: are the specific aims that philosophers of mathematical explanation set themselves legitimate? Finally: are the models of explanation developed by philosophers of science useful tools for philosophers of mathematical explanation? We argue that the answer to all these questions is positive. Our views are completely opposite to the views that Mark Zelcer has put forward recently. Throughout this paper, we show why Zelcer’s arguments fail.

One of the functions of scientific knowledge is to provide the theories and laws we need in order to understand the world. My article deals with the epistemic aspect of understanding, i.e., with understanding as unification. The aim is to explicate what we have to do in order to make our scientific knowledge contribute to an increase of the degree to which the particular events we have observed, fit into our world-picture. The analysis contains two parts. First I define the concept of scientific epistemic explanation. Explanations of these type are the appropriate instruments for increasing the degree of unification of the particular events we have observed. In the second, largest part of the article I analyze the construction process of scientific epistemic explanations, focusing on the application of scientific theories.