Erik Weber

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.

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

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.

This book contains a selection of the papers presented at the Logic, Reasoning and Rationality 2010 conference in Ghent. The conference aimed at stimulating the use of formal frameworks to explicate concrete cases of human reasoning, and conversely, to challenge scholars in formal studies by presenting them with interesting new cases of actual reasoning. According to the members of the Wiener Kreis, there was a strong connection between logic, reasoning, and rationality and that human reasoning is rational in so far as it is based on logic. Later, this belief came under attack and logic was deemed inadequate to explicate actual cases of human reasoning. Today, there is a growing interest in reconnecting logic, reasoning and rationality. A central motor for this change was the development of non-classical logics and non-classical formal frameworks. The book contains contributions in various non-classical formal frameworks, case studies that enhance our apprehension of concrete reasoning patterns, and studies of the philosophical implications for our understanding of the notions of rationality.

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 paper we elaborate Ned Hall's theoretical utility perspective for causation in everyday contexts. We do this by presenting some instances of it, thereby adding some flesh to the skeleton that Hall has provided. Our elaboration of the theoretical utility perspective also provides arguments for it: the instances we present show the fruitfulness of the approach. A question raised by Hall's proposal is: should we give up descriptive analysis of causation (and descriptive analysis in general) completely? We argue that, at least for causation, traditional descriptive conceptual analysis must be given up. However, we also argue that a more modest variant of descriptive conceptual analysis can be useful

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

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 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.

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

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 this paper we chart epistemological similarities between shared function talk in biology and the engineering sciences, focusing on the notions of biological advantage function and technical advantage function. We start by showing that biological advantage function ascriptions are common in biology and that technical advantage function ascriptions are common in engineering science. We then proceed to show that these ascriptions have a very similar structure and that their epistemic value also is similar: both biological advantage function and technical advantage function ascriptions provide the means to answer what-would-happen-if questions. We develop and illustrate our claims with four case studies: two from biology, and two from engineering design research. Our results offer new insights into a relatively neglected issue in both philosophy of biology and technology, viz. assessing the explanatory and predictive utility of function ascriptions.

Among philosophers of science, there is now a widespread agreement that the DN model of explanation is poorly equipped to account for explanations in biology. Rather than identifying laws, so the consensus goes, researchers explain biological capacities by constructing a model of the underlying mechanism.We think that the dichotomy between DN explanations and mechanistic explanations is misleading. In this article, we argue that there are cases in which biological capacities are explained without constructing a model of the underlying mechanism. Although these explanations do not conform to Hempel’s DN model, they do invoke more or less stable generalisations. Because they invoke generalisations and have the form of an argument, we call them inferential explanations. We support this claim by considering two examples of explanations of biological capacities: pigeon navigation and photoperiodism. Next, we will argue that these non-mechanistic explanations are crucial to biology in three ways: sometimes, they are the only thing we have, they are heuristically useful, and they provide genuine understanding and so are interesting in their own right.In the last sections we discuss the relation between types of explanations and types of experiments and situate our views within some relevant debates on explanatory power and explanatory virtues.

In this paper we argue that in recent literature on mechanistic explanations, authors tend to conflate two distinct features that mechanistic models can have or fail to have: plausibility and richness. By plausibility, we mean the probability that a model is correct in the assertions it makes regarding the parts and operations of the mechanism, i.e., that the model is correct as a description of the actual mechanism. By richness, we mean the amount of detail the model gives about the actual mechanism. First, we argue that there is at least a conceptual reason to keep these two features distinct, since they can vary independently from each other: models can be highly plausible while providing almost no details, while they can also be highly detailed but plainly wrong. Next, focusing on Craver's continuum of ?how-possibly,? to ?how-plausibly,? to ?how-actually? models, we argue that the conflation of plausibility and richness is harmful to the discussion because it leads to the view that both are necessary for a model to have explanatory power, while in fact, richness is only so with respect to a mechanism's activities, not its entities. This point is illustrated with two examples of functional models

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 §1 we present textual evidence which shows that this view is very popular. In §2 we show that this standard view is too restrictive: the practical value of causal knowledge is wider. In §3 we introduce the distinction between ‘manipulative policy’ and ‘selective policy’ as a theoretical framework to account for this wider practical value.