It has been the dominant view that probabilistic explanations of particular facts must be inductive in character. I argue here that this view is mistaken, and that the aim of probabilistic explanation is not to demonstrate that the explanandum fact was nomically expectable, but to give an account of the chance mechanism(s) responsible for it. To this end, a deductive-nomological model of probabilistic explanation is developed and defended. Such a model has application only when the probabilities occurring in (...) covering laws can be interpreted as measures of objective chance, expressing the strength of physical propensities. Unlike inductive models of probabilistic explanation, this deductive model stands in no need of troublesome requirements of maximal specificity or epistemic relativization. (shrink)

The paper explores a deductive-nomological account of metaphysical explanation: some truths metaphysically explain, or ground, another truth just in case the laws of metaphysics determine the latter truth on the basis of the former. I develop and motivate a specific conception of metaphysical laws, on which they are general rules that regulate the existence and features of derivative entities. I propose an analysis of the notion of ‘determination via the laws’, based on a restricted form of logical entailment. (...) I argue that the DN-account of ground can be defended against the well-known objections to the DN-approach to scientific explanation. The goal of the paper is to show that the DN-account of metaphysical explanation is a well-motivated and defensible theory. (shrink)

The discussion on mathematical explanation has inherited the same sense of dissatisfaction that philosophers of science expressed, in the context of scientific explanation, towards the deductive-nomological model. This model is regarded as unable to cover cases of bona fide mathematical explanations and, furthermore, it is largely ignored in the relevant literature. Surprisingly, the reasons for this ostracism are not sufficiently manifest. In this paper I explore a possible extension of the model to the case of mathematical explanations and (...) I claim that there are at least two reasons to judge the deductive-nomological picture of explanation as inadequate in that context. (shrink)

I propose a deductive-nomological model for mathematical scientific explanation. In this regard, I modify Hempel’s deductive-nomological model and test it against some of the following recent paradigmatic examples of the mathematical explanation of empirical facts: the seven bridges of Königsberg, the North American synchronized cicadas, and Hénon-Heiles Hamiltonian systems. I argue that mathematical scientific explanations that invoke laws of nature are qualitative explanations, and ordinary scientific explanations that employ mathematics are quantitative explanations. I analyse the repercussions (...) of this deductivenomological model on causal explanations. (shrink)

In recent years philosophy of science has seen a resurgence of interest in metaphysical issues, especially those concerning laws, causation,and explanation. Although this book takes only the latter two words for its title, it is also about laws of nature. It is divided into three sections: the first is on causation, the second is on laws, and the third is on explanation: this is entirely appropriate because the debates about them are closely related. Ever since Hume argued that causation is (...) nothing more than regularities, laws have been more respectable than causes in philosophy. Perhaps this is also because science is replete with specially named laws which seem to play a central role in theories and explanations. Yet, as many philosophers have recently pointed out, contrary to Russell’s famous pronouncement that causation is a relic of a bygone age (quoted p. 3 by Psillos), the contemporary special sciences are very much concerned with the identification and investigation of all manner of causal structures. This raises the question of whether the apparent causal powers attributed to kinds in the special sciences are anything over and above a way of talking about the result of the operations of physical laws governing their microconstituents. Hence the logical empiricist’s project of showing how the laws of the special sciences reduce to those of physics. On their view, explanation, and in particular causal explanation, is nothing more than argument using the laws of nature as premises. However, this coveringlaw model of explanation has been subjected to intense criticism, and there have been attempts to construct alternatives that rely on the idea that to explain an event is to cite its real cause, where this cause need not be subsumed under any law. Since the demise of logical empiricism, or at least the waning of its influence, there has been a proliferation of theories about laws, causation and explanation, many of which differ radically from one another. (shrink)

We revive the idea that a deductive-nomological explanation of a scientific theory by its successor may be defensible, even in those common and troublesome cases where the theories concerned are mutually incompatible; and limiting, approximating and counterfactual assumptions may be required in order to define a logical relation between them. Our solution is based on a general characterization of limiting relations between physical theories using the method of nonstandard analysis.

This paper discusses the important paper by Paul Thagard on the pathway version of mechanistic explanation that is currently used in chemical explanation. The author claims that this method of explanation has a respectable pedigree and can be traced back to the Chemical Revolution in the arguments used by the Lavoisier School in their theoretical duels with Richard Kirwan, the proponent of a revised phlogistonian theory. Kirwan believed that complex chemical reactions could be explained by recourse to affinity tables that (...) catalogued the attraction that various simple bodies possessed towards each other. To explain was in effect to make a delayed prediction, it is not enough just to show how a phenomenon fits into the discernible patterns of the world. Lavoisier, Fourcroy and their colleagues used pathway reasoning, although disguising this fact by suggesting that affinities varied when subjected to n-body situations. (shrink)

I provide a general introduction to the notion at issue in this volume, i.e. deduction, and to some akin notions like inference and reasoning. I also argue that logic is, or should be concerned with three attitudes: an archaeological attitude, a nomological attitude, and an aetiological attitude. Then, I sum up the content of the contributions in this volume.

According to Hempel, all scientific explanations and predictions which are produced exclusively with deterministic laws must be deductive, in the sense that the explanandum or the prediction must be a logical consequence of the laws and the initial conditions in the explanans. This deducibility thesis has been attacked from several quarters. Some time ago Canfield and Lehrer presented a “refutation” of DT as applied to predictions, in which they tried to prove that “if the deductive reconstruction [DT for (...) predictions] were an adequate reconstruction, then scientific prediction would be impossible”. Their argument seems to have been uncontested except for an inconclusive rejoinder by Beard. Moreover, Stegmüller has recently argued that “it may turn out that all or at least most of the so-called deductive-nomological explanations are in truth inductive and not deductive arguments, in view of the difficulty which has been pointed out by Canfield and Lehrer”. It seems it would be worth investigating whether Canfield and Lehrer's argument is, indeed, correct. (shrink)

According to the backing law account of causation a singular causal claim is to be analyzed (or “justified”) by reference to a suitable nomic theory which, together with the given singular statement describing a cause, deductively supports or explains the statement describing the effect. This backing law (or deductive-nomological) account of singular causation has recently become the target of several kinds of criticism. First, the possibility of giving a detailed and elaborate account of the required nomic or explanatory (...) backing has been doubted. Secondly, it has been claimed that the deductive-nomological account is bound to lead to unacceptable ontological difficulties (see Kim [1969] and [1973a]). Thirdly, it has been argued that the backing law account fails to give a satisfactory analysis of certain conditional or counterfactual aspects of causation (see Lewis [1973b]). Fourthly, it has been claimed that this approach does not work at least within the social and historical sciences as there are no laws and theories of the required sort to be found there. (shrink)

The purpose of this essay is to defend the deductive-nomological model of explanation against a number of criticisms that have been made of it. It has traditionally been thought that scientific explanations were causal and that scientific explanations involved deduction from laws. In recent years, however, this three-fold identity has been challenged: there are, it is argued, causal explanations that are not scientific, scientific explanations that are not deductive, deductions from laws that are neither causal explanations nor (...) scientific explanations, and causal explanations that involve no deductions from laws. The aim of the present essay is to defend the traditional identities, and to show that the more recent attempts at invalidating them fail in their object. More specifically, this essay argues that a Humean version of the deductive-nomological model of explanation can be defended as the correct account of scientific explanation of individual facts and processes, and as the correct account of causal explanations of individual facts and processes. The deductive-nomological model holds that to explain an event E, say that a is G, one must find some initial conditions C, say that a is F, and a law or theory T such that T and C jointly entail E, and both are essential to the deduction. (shrink)

It is usually supposed that the ‘fundamental’ laws of nature must be general, i.e. must essentially begin with a universal quantifier. Other ‘derivative’ laws may not satisfy this requirement but only because they are the logical or mathematical consequences of a set of fundamental laws. As it stands, this requirement is either too strong or trivial. Consider the Newtonian gravitational law: where FG is the impressed gravitational force on x due to y at t, ms is the mass of x, (...) d is the displacement of the center of mass of x from the center of mass of y at t, and G is the universal gravitational constant.. What is the role of the constant “G”? Reflection shows that it is actually a disguised existential quantifier, or, more precisely, a Skolem function replacement for one. Rendering Newtonian mechanics in a parsimonious language, we could drop the “G” and rewrite as The constant “G” could then be defined, if desired, as the unique r satisfying ). Such a theory would have exactly the same deductive consequences as. But has an initial existential quantifier essentially. (shrink)

Now that the excitement created by the historicist turn in philosophy of science has cooled down, the particular questions raised by this turn do not command anymore the almost exclusive attention of practitioners in the field. Philosophers of science feel freer to embark on such different projects as, for example, an effort to reexamine—on the basis of the lessons taught and of the experience gained—the issues which dominated the scene before the historicist movement erupted. Fred Wilson’s book on the (...) class='Hi'>deductive-nomological model of explanation is a case in point. (shrink)

Call an explanation in which a non-mathematical fact is explained—in part or in whole—by mathematical facts: an extra-mathematical explanation. Such explanations have attracted a great deal of interest recently in arguments over mathematical realism. In this article, a theory of extra-mathematical explanation is developed. The theory is modelled on a deductive-nomological theory of scientific explanation. A basic DN account of extra-mathematical explanation is proposed and then redeveloped in the light of two difficulties that the basic theory faces. The (...) final view appeals to relevance logic and uses resources in information theory to understand the explanatory relationship between mathematical and physical facts. 1Introduction2Anchoring3The Basic Deductive-Mathematical Account4The Genuineness Problem5Irrelevance6Relevance and Information7Objections and Replies 7.1Against relevance logic7.2Too epistemic7.3Informational containment8Conclusion. (shrink)

Opinionated state of the art paper on scientific explanation. Analysis and discussion of the most relevant models and theories in the contemporary literature, namely, the deductive-nomological model, the models of inductive-statistical and statistical relevance, the pragmatic theory of why questions, the unifying theory of standard arguments, and the causal/non-causal counterfactual theory.

According to the deductive-nomological account of ground, a fact A grounds another fact B in case the laws of metaphysics determine the existence of B on the basis of the existence of A. Accounts of grounding of this particular variety have already been developed in the literature. My aim in this paper is to sketch a new version of this account. My preferred account offers two main improvements over existing accounts. First, the present account is able to deal (...) with necessitarian as well as non-necessitarian cases of grounding by acknowledging the existence of two types of metaphysical laws. I will argue that we should assume that metaphysical laws come in the necessitarian as well as in the non-necessitarian varietyclosely paralleling the distinction between strict and non-strict laws in the philosophy of science. The second main improvement of the present account is that this account is able to provide an explanation of why the laws of metaphysics have a direction built into them. I will argue that we should characterize metaphysical laws with the help of Theodore Sider’s (2011) notion of structure, which is a descendent of David Lewis’s (1983) notion of naturalness. According to the account of metaphysical laws developed in this paper, metaphysical laws express in their antecedents either perfectly structural truths or more structural truths than in their consequents. Since on Sider’s account structural features of reality are fundamental features of reality, the account is able to explain as to why the laws of metaphysics take us from the fundamental to the derivative. (shrink)

In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity of the term “explanation” in explainable AI (XAI) can be solved by adopting any of four different extant accounts of explanation in the philosophy of science: the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New Mechanist models. In this chapter, I show that the authors’ claim that these accounts can be applied to deep neural networks as they would to any natural phenomenon is mistaken. (...) I also provide a more general argument as to why the notion of explainability as it is currently used in the XAI literature bears little resemblance to the traditional concept of scientific explanation. It would be more fruitful to use the label “understandable AI” to avoid the confusion that surrounds the goal and purposes of XAI. In the second half of the chapter, I argue for a pragmatic conception of understanding that is better suited to play the central role attributed to explanation in XAI. Following De Regt (2017) and Kuorikoski and Ylikoski (2015), the conditions of satisfaction for understanding an ML system are fleshed out in terms of an agent’s success in using the system. (shrink)

SummaryHempel's Deductive‐Nomological model of explanation is compared to Sellars' brand of essentialism. The source of their differences is shown to lie in their views on the explanatory role of inductively based generalizations. An adequate explanation requires a reasoned account of why an empirical generalization fails. On Sellars' view this entails concentrating on the nature of the things whose behavior is in question. We thereby remove ourselves from the misleading positivist methodology in which one counterinstance renders a generalization uninteresting. (...) It is suggested instead that “disconfirmed” generalizations are of crucial positive methodological importance for purposes of theory development.RésuméLe modèle déductif‐nomologique de Hempel est comparéà l'essentialisme dans la version qu'en propose Sellars. L'auteur montre que leurs différences proviennent de ce qu'ils jugent différemment le rôle explicatif des généralisations inductives et les raisons de l'échec d'une généralisation empirique. D'après Sellars, il faut par conséquent se concentrer sur la nature des choses dont on observe le comportement; on évite ainsi les problèmes méthodologiques soulevés par les théories positivistes standard de la confirmation. L'auteur conclut en montrant que des généralisations infirmées ont une importance positive capitale pour le développement des théories. (shrink)

In this book, I will discuss three main topics: the roots and aims of scientific knowledge, scientific knowledge in society, and science and values I understand scientific knowledge as being a planned and continuous production of the general and common knowledge of scientific communities. I begin my discussion with a brief analysis of the main differences between sciences, on the one hand, and everyday experience, philosophies, religions, and ideologies, on the other. I define the concept of science as a set (...) of family resemblances (in Wittgenstein's sense). The sciences can be distinguished along three lines, in terms of the difference between empirical and non-empirical sciences, differences in degrees of theoretical and methodological structure among individual sciences and scientific disciplines. Scientific knowledge is based upon some grounds, paradigmatic knowledge bases. Scientific knowledge is founded on a unified paradigmatic knowledge base. The latter includes fundamental theories, basic facts, generally accepted empirical regularities (laws), scientific research methods, scientific explanations, and established relations to other sciences. Scientific knowledge in general is defined well in classical terms by the definition that knowledge is a true and rationally-justified belief. In this connection, scientific truth is relative, depending on the given paradigmatic knowledge base and on the precision and accessibility of the measurements and observations involved. In the book's first part, I consider the production and testing of scientific hypotheses, scientific explanations, the structure of scientific theories, and the problem of scientific realism. I consider explanation an integral part of a wider nexus of heuristic and theoretical activities in science like giving reasons, understanding, clarification, and founding. Continuous production of scientific explanations is a necessary condition for the systematic reproduction of scientific knowledge. Accordingly, it does not suffice to describe them in terms of the logical structure of the arguments on which they are based, but rather, scientific explanations ought to be given complete with an epistemic and pragmatic context of the explanation. I consider especially deductive-nomological, inductive-statistical, dispositional, teleological-functional, and genetic explanations. I emphasize the ever-increasing importance of explanations by analogy and by model. Orthogonal to these distinctions lies the distinction between causal and epistemic explanation. Epistemic explanations relate scientific discoveries to epistemic progress, and causal explanations are these epistemic explanations that relate scientific discoveries to reality too. Causal explanations inform us that certain changes occur in the world as agents of other changes, or rather, that certain changes are real consequences of other changes. Thus, by having explanations of causes, we can intervene in the world to induce by design certain changes in some course of phenomena. I consider scientific theories as logically and epistemically ordered wholeness of scientific knowledge in a realm of research that enables us to construct useful models of reality. After the discussion of different 'statement' views on theories and different interpretations of the theory-experience relation, I introduce the 'model' theoretical (non-statement) views on theories, especially the structural theory of science. The model views on theories transcend the realism-antirealism dualism by introducing models between theory and reality. Therefore, the question of scientific realism is not merely whether and, if so, which element(s), of scientific theories represent reality, but rather which conditions suffice to infer from a theory that one of its models truly expresses reality. In other words, under which conditions do models of a theory represent models of reality? Generally, models are just as much images of reality in a theory as they are images of a theory in reality. The actual problem of scientific realism doesn't originate in the observable-unobservable distinction (even if this distinction is otherwise theoretically loaded), but rather in ontological quandaries, of the sort especially encountered in modern physical theories such as quantum mechanics, relativity theory, and cosmological theories, although they are known to arise in other sciences as well. Scientific theories may be real, even if they describe nothing about the world. However, such theories can be given this status only if their formal, mathematical postulates exhibit some formal homology between reality and mathematical forms. The very fact that it is possible to describe reality with the help of mathematics even in cases, where every (representational, conceptual) language and thought fall short, would indicate that even mathematics itself is, in some sense, a part of reality, although 'outside' of the empirical world. It follows we have to distinguish the non-empirical reality and the empirical world. Formal objectivity and non-arbitrariness of theories do not emerge from either our language or thoughts, or from the world of facts, causes, and effects, but rather from non-empirical reality, which frames the world as some kind of objective transcendental condition for the possibility of the existence of the world, thoughts, and language. In the second part of the book, I consider the incorporation of scientific work and scientific knowledge into modern society and its relations to values and ethics. Production of scientific knowledge goes on as a parallel distributed process of a special kind of social (shared) cognition. The central part of this cognition is the presentation of parts of the world by scientific (theoretical) models which are warranted by the shared and public practice of justification, and in reflective acting in the world as it would. I consider various formal concepts of collective propositional knowledge (distributed, mutual, common knowledge). Scientific knowledge is an achievement to be ascribed to a scientific community or, indirectly, to all those who apply or expand it, rather than to individuals per se. It presents the continuous and partially planned production of non-trivial common knowledge of the research communities in modern societies. However, besides common knowledge, shared dispositional knowledge (knowledge how of scientific teams) and shared knowledge by acquiring (knowledge of fundamental similarities and differences in scientific teams) are parts of the real body of scientific knowledge. Scientific common knowledge implies the projection into idealized universal knowledge of all real or potentially rational and well-informed individuals. The respect of scientific knowledge thus presents the norm of rationality. Science as the intensively shared and socially distributed kind of cognition enters into modern society as a special kind of work (general work), as a productive force (social invention drive), and as a form of capital (human, social, cultural, informational, etc.). However any of these categories can adequately represent the socio-economical role of science in modern societies, and we are still waiting for a more adequate general concept that would theoretically cover the 'scientification' of modern societies. My thesis is that the most important general effect of scientification is (and will be) the growth of social processes that structurally resemble parallel distributed shared social cognition of science, and not so much the rise of the use of scientific knowledge and scientifically based information in all kinds of production. Scientification of modern societies inevitably includes a much stronger need for moral and social responsibility of scientists for their work and its effects in their local society and the global world. This need comes in contradiction with the quite large accepted idea (and even the 'norm') on value-free research. I discuss to some degree different arguments for the absence of non-epistemic values and ethical reasoning from science and find them non-convincing. The sound idea of value-free research lies in the need for the absence of categorical value statements from scientific explanations and theoretical deductions. Values and ethical judgment have, and sometimes they even have to play an important role in another part of research and its application in science or outside it. Not the value-loudness of research is a danger for science but its blind, non-reflective use. It leads to the ideologization and politicization of science. A similar danger lies in the purely value-neutrality of research. It leads to the instrumentalization of science to the technical or bureaucratic means for 'any' aims. (shrink)

As Aristotle stated, scientific explanation is based on deductive argument--yet, Wesley C. Salmon points out, not all deductive arguments are qualified explanations. The validity of the explanation must itself be examined. _Four Decades of Scientific Explanation_ provides a comprehensive account of the developments in scientific explanation that transpired in the last four decades of the twentieth century. It continues to stand as the most comprehensive treatment of the writings on the subject during these years. Building on the historic (...) 1948 essay by Carl G. Hempel and Paul Oppenheim, "Studies in the Logic of Explanation,” which introduced the deductive-nomological model on which most work on scientific explanation was based for the following four decades, Salmon goes beyond this model's inherent basis of describing empirical knowledge to tells us “not only _what,_ but also _why_.” Salmon examines the predominant models in chronological order and describes their development, refinement, and criticism or rejection. _Four Decades of Scientific Explanation_ underscores the need for a consensus of approach and ongoing evaluations of methodology in scientific explanation, with the goal of providing a better understanding of natural phenomena. (shrink)

An account of distinctively mathematical explanation (DME) should satisfy three desiderata: it should account for the modal import of some DMEs; it should distinguish uses of mathematics in explanation that are distinctively mathematical from those that are not (Baron [2016]); and it should also account for the directionality of DMEs (Craver and Povich [2017]). Baron’s (forthcoming) deductive-mathematical account, because it is modelled on the deductive-nomological account, is unlikely to satisfy these desiderata. I provide a counterfactual account of (...) DME, the Narrow Ontic Counterfactual Account (NOCA), that can satisfy all three desiderata. NOCA appeals to ontic considerations to account for explanatory asymmetry and ground the relevant counterfactuals. NOCA provides a unification of the causal and the non-causal, the ontic and the modal, by identifying a common core that all explanations share and in virtue of which they are explanatory. (shrink)

In the beginning, there was the DN (Deductive Nomological) model of explanation, articulated by Hempel and Oppenheim (1948). According to DN, scientific explanation is subsumption under natural law. Individual events are explained by deducing them from laws together with initial conditions (or boundary conditions), and laws are explained by deriving them from other more fundamental laws, as, for example, the simple pendulum law is derived from Newton's laws of motion.

What is the nature of causation? How is causation linked with explanation? And can there be an adequate theory of explanation? These questions and many others are addressed in this unified and rigorous examination of the philosophical problems surrounding causation, laws and explanation. Part 1 of this book explores Hume's views on causation, theories of singular causation, and counterfactual and mechanistic approaches. Part 2 considers the regularity view of laws and laws as relations among universals, as well as recent alternative (...) approaches to laws. Part 3 examines the issues arising from deductive-nomological explanation, statistical explanation, the explanation of laws and the metaphysics of explanation. Accessible to readers of all levels, this book provides an excellent introduction to one of the most enduring problems of philosophy. (shrink)

Contents Preface General Introduction 1 | Science and Pseudoscience Introduction Karl Popper, Science: Conjectures and Refutations Thomas S. Kuhn, Logic of Discovery or Psychology of Research? Imre Lakatos, Science and Pseudoscience Paul R. Thagard, Why Astrology Is a Pseudoscience Michael Ruse, Creation-Science Is Not Science Larry Laudan, Commentary: Science at the Bar---Causes for Concern Commentary 2 | Rationality, Objectivity, and Values in Science Introduction Thomas S. Kuhn, The Nature and Necessity of Scientific Revolutions Thomas S. Kuhn, Objectivity, Value Judgment, and (...) Theory Choice Ernan McMullin, Rationality and Paradigm Change in Science Larry Laudan, Kuhn’s Critique of Methodology Helen E. Longino, Values and Objectivity Kathleen Okruhlik, Gender and the Biological Sciences Commentary 3 | The Duhem-Quine Thesis and Underdetermination Introduction Pierre Duhem, Physical Theory and Experiment W. V. Quine, Two Dogmas of Empiricism Donald Gillies, The Duhem Thesis and the Quine Thesis Larry Laudan, Demystifying Underdetermination *Colin Howson and Peter Urbach, The Duhem Problem Commentary 4 | Induction, Prediction, and Evidence Introduction Peter Lipton, Induction Karl Popper, The Problem of Induction Wesley C. Salmon, Rational Prediction Carl G. Hempel, Criteria of Confirmation and Acceptability Peter Achinstein, Explanation v. Prediction: Which Carries More Weight? *Nelson Goodman, The New Riddle of Induction Commentary 5 | Confirmation and Relevance: Bayesian Approaches Introduction Wesley C. Salmon, Rationality and Objectivity in Science *Deborah G. Mayo, A Critique of Salmon’s Bayesian Way *Alan Chalmers, The Bayesian Approach Paul Horwich, Therapeutic Bayesianism Commentary 6 | Models of Explanation Introduction Rudolf Carnap, The Value of Laws: Explanation and Prediction Carl G. Hempel, Two Basic Types of Scientific Explanation Carl G. Hempel, The Thesis of Structural Identity Carl G. Hempel, Inductive-Statistical Explanation Peter Railton, A Deductive-Nomological Model of Probabilistic Explanation *Philip Kitcher, Explanatory Unification *James Woodward, The Manipulability Conception of Causal Explanation Commentary 7 | Laws of Nature Introduction A. J. Ayer, What Is a Law of Nature? Fred I. Dretske, Laws of Nature D. H. Mellor, Necessities and Universals in Natural Laws Nancy Cartwright, Do the Laws of Physics State the Facts? Commentary 8 | Intertheoretic Reduction Introduction Ernest Nagel, Issues in the Logic of Reductive Explanations Paul K. Feyerabend, How to Be a Good Empiricist *Jerry A. Fodor, Special Sciences Philip Kitcher, 1953 and All That: A Tale of Two Sciences Commentary 9 | Empiricism and Scientific Realism Introduction Grover Maxwell, The Ontological Status of Theoretical Entities Bas C. van Fraassen, Arguments Concerning Scientific Realism Alan Musgrave, Realism versus Constructive Empiricism Larry Laudan, A Confutation of Convergent Realism *Juha T. Saatsi, On the Pessimistic Induction and Two Fallacies Ian Hacking, Experimentation and Scientific Realism David B. Resnik, Hacking’s Experimental Realism *Martin Carrier, What Is Right with the Miracle Argument Arthur Fine, The Natural Ontological Attitude Alan Musgrave, NOA’s Ark---Fine for Realism Commentary Glossary Bibliography Name Index Subject Index. (shrink)

According to a causal model of explanation, we explain phenomena by giving their causes or, where the phenomena are themselves causal regularities, we explain them by giving a mechanism linking cause and effect. If we explain why smoking causes cancer, we do not give the cause of this causal connection, but we do give the causal mechanism that makes it. The claim that to explain is to give a cause is not only natural and plausible, but it also avoids many (...) of the objections to other accounts of explanation, such as the views that to explain is to give a reason to believe the phenomenon occurred, to somehow make the phenomenon familiar, or to give a Deductive-Nomological argument. Unlike the reason for belief account, a causal model makes a clear distinction between understanding why a phenomenon occurs and merely knowing that it does, and the model does so in a way that makes understanding unmysterious and objective. Understanding is not some sort of super-knowledge, but simply more knowledge: knowledge of the phenomenon and knowledge of its causal history. A causal model makes it clear how something can explain without itself being explained, and so avoids the regress of whys, since we can know a phenomenon's cause without knowing the cause of the cause. It also accounts for legitimate self-evidencing explanations, explanations where the phenomenon is an essential part of the evidence that the explanation is correct, so the explanation can not supply a non-circular reason for believing the phenomenon occurred. There is no barrier to knowing a cause through its effects and also knowing that it is their cause. The speed of recession of a star explains its observed red-shift, even though the shift is an essential part of the evidence for its speed of recession. The model also avoids the most serious objection to the familiarity view, which is that some phenomena are familiar yet not understood, since a phenomenon can be perfectly familiar, such as the blueness of the sky or the fact that the same side of the moon always faces the earth, even if we do not know its cause. Finally, a causal model avoids many of the objections to the Deductive-Nomological model. Ordinary explanations do not have to meet the requirements of the Deductive-Nomological model, because one does not need to give a law to give a cause, and one does not need to know a law to have good reason to believe that a cause is a cause. As for the notorious over-permissiveness of the Deductive-Nomological model, the reason recession explains red-shift but not conversely, is simply that causes explain effects but not conversely, and the reason a conjunction of laws does not explain its conjuncts is that conjunctions do not cause their conjuncts. (shrink)

This essay analyzes and develops recent views about explanation in biology. Philosophers of biology have parted with the received deductive-nomological model of scientific explanation primarily by attempting to capture actual biological theorizing and practice. This includes an endorsement of different kinds of explanation (e.g., mathematical and causal-mechanistic), a joint study of discovery and explanation, and an abandonment of models of theory reduction in favor of accounts of explanatory reduction. Of particular current interest are philosophical accounts of complex explanations (...) that appeal to different levels of organismal organization and use contributions from different biological disciplines. The essay lays out one model that views explanatory integration across different disciplines as being structured by scientific problems. I emphasize the philosophical need to take the explanatory aims pursued by different groups of scientists into account, as explanatory aims determine whether different explanations are competing or complementary and govern the dynamics of scientific practice, including interdisciplinary research. I distinguish different kinds of pluralism that philosophers have endorsed in the context of explanation in biology, and draw several implications for science education, especially the need to teach science as an interdisciplinary and dynamic practice guided by scientific problems and explanatory aims. (shrink)

Issues concerning scientific explanation have been a focus of philosophical attention from Pre- Socratic times through the modern period. However, recent discussion really begins with the development of the Deductive-Nomological (DN) model. This model has had many advocates (including Popper 1935, 1959, Braithwaite 1953, Gardiner, 1959, Nagel 1961) but unquestionably the most detailed and influential statement is due to Carl Hempel (Hempel 1942, 1965, and Hempel & Oppenheim 1948). These papers and the reaction to them have structured subsequent (...) discussion concerning scientific explanation to an extraordinary degree. After some general remarks by way of background and orientation (Section 1), this entry describes the DN model and its extensions, and then turns to some well-known objections (Section 2). It next describes a variety of subsequent attempts to develop alternative models of explanation, including Wesley Salmon's Statistical Relevance (Section 3) and Causal Mechanical (Section 4) models and the Unificationist models due to Michael Friedman and Philip Kitcher (Section 5). Section 6 provides a summary and discusses directions for future work. (shrink)

Today, mechanisms and mechanistic explanation are very popular in philosophy of science and are deemed a welcome alternative to laws of nature and deductive‐nomological explanation. Starting from Mitchell's pragmatic notion of laws, I cast doubt on their status as a genuine alternative. I argue that (1) all complex‐systems mechanisms ontologically must rely on stable regularities, while (2) the reverse need not hold. Analogously, (3) models of mechanisms must incorporate pragmatic laws, while (4) such laws themselves need not always (...) refer to underlying mechanisms. Finally, I show that Mitchell's account is more encompassing than the mechanistic account *Received August 2008; revised January 2010. †To contact the author, please write to: Centre for Logic and Philosophy of Science, Ghent University, Blandijnberg 2, B‐9000 Belgium; e‐mail: [email protected]. (shrink)

In "Studies In The Logic Of Explanation" (Philosophy of Science, XV, 1948) Hempel and Oppenheim analyze the basic pattern of scientific explanation. One of the difficult problems which they acknowledge is "whether and how the analysis of explanation can be extended from the case where all general ex- planatory principles invoked are of a strictly universal or 'deterministic' form to the case where explanatory reference is made to statistical hypotheses." It is hoped that the remarks which follow may contribute a (...) little toward clarifying the issue. (shrink)

Husserl’s early picture of explanation in the sciences has never been completely provided. This lack represents an oversight, which we here redress. In contrast to currently accepted interpretations, we demonstrate that Husserl does not adhere to the much maligned deductive-nomological (DN) model of scientific explanation. Instead, via a close reading of early Husserlian texts, we reveal that he presents a unificationist account of scientific explanation. By doing so, we disclose that Husserl’s philosophy of scientific explanation is no mere (...) anachronism. It is, instead, tenable and relevant. We discuss how Husserl and other contemporary thinkers draw theoretical inspiration from the same source—namely, Bernard Bolzano. Husserl’s theory of scientific explanation shares a common language and discusses the same themes as, for example, Phillip Kitcher and Kit Fine. To advance our novel reading, we discuss Husserl’s investigations of grounding, inter-lawful explanation, intra-mathematical explanation, and scientific unification. (shrink)

This paper attempts a critical reappraisal of Nagel's (1961, 1970) model of reduction taking into account both traditional criticisms and recent defenses. This model treats reduction as a type of explanation in which a reduced theory is explained by a reducing theory after their relevant representational items have been suitably connected. In accordance with the deductive-nomological model, the explanation is supposed to consist of a logical deduction. Nagel was a pluralist about both the logical form of the connections (...) between the reduced and reducing theories (which could be conditionals or biconditionals) and their epistemological status (as analytic connections, conventions, or synthetic claims). This paper defends Nagel's pluralism on both counts and, in the process, argues that the multiple realizability objection to reductionism is misplaced. It also argues that the Nagel model correctly characterizes reduction as a type of explanation. However, it notes that logical deduction must be replaced by a broader class of inferential techniques that allow for different types of approximation. Whereas Nagel (1970), in contrast to his earlier position (1961), recognized the relevance of approximation, he did not realize its full import for the model. Throughout the paper two case studies are used to illustrate the arguments: the putative reduction of classical thermodynamics to the kinetic theory of matter and that of classical genetics to molecular biology. (shrink)

The purpose of this paper is to provide a systematic defense of the single-case propensity account of probabilistic explanation from the criticisms advanced by Hanna and by Humphreys and to offer a critical appraisal of the aleatory conception advanced by Humphreys and of the deductive-nomological-probabilistic approach Railton has proposed. The principal conclusion supported by this analysis is that the Requirements of Maximal Specificity and of Strict Maximal Specificity afford the foundation for completely objective explanations of probabilistic explananda, so (...) long as they are employed on the basis of propensity criteria of explanatory relevance. (shrink)

This paper addresses a fundamental line of research in neuroscience: the identification of a putative neural processing core of the cerebral cortex, often claimed to be “canonical”. This “canonical” core would be shared by the entire cortex, and would explain why it is so powerful and diversified in tasks and functions, yet so uniform in architecture. The purpose of this paper is to analyze the search for canonical explanations over the past 40 years, discussing the theoretical frameworks informing this research. (...) It will highlight a bias that, in my opinion, has limited the success of this research project, that of overlooking the dimension of cortical development. The earliest explanation of the cerebral cortex as canonical was attempted by David Marr, deriving putative cortical circuits from general mathematical laws, loosely following a deductive-nomological account. Although Marr’s theory turned out to be incorrect, one of its merits was to have put the issue of cortical circuit development at the top of his agenda. This aspect has been largely neglected in much of the research on canonical models that has followed. Models proposed in the 1980s were conceived as mechanistic. They identified a small number of components that interacted as a basic circuit, with each component defined as a function. More recent models have been presented as idealized canonical computations, distinct from mechanistic explanations, due to the lack of identifiable cortical components. Currently, the entire enterprise of coming up with a single canonical explanation has been criticized as being misguided, and the premise of the uniformity of the cortex has been strongly challenged. This debate is analyzed here. The legacy of the canonical circuit concept is reflected in both positive and negative ways in recent large-scale brain projects, such as the Human Brain Project. One positive aspect is that these projects might achieve the aim of producing detailed simulations of cortical electrical activity, a negative one regards whether they will be able to find ways of simulating how circuits actually develop. (shrink)

This paper makes a phenomenological contribution to the distinction between personal and subpersonal types of explanation. I expound the little-known fact that Husserl gives an account of personal level explanation via his exposition of our capacity to express the understanding of another’s motivational nexus when we are in the personalistic attitude. I show that Husserl’s unique exposition of the motivational nexus conveys its concrete, internally coherent, and intentional nature, involving relationships amongst the sense contents of acts of consciousness. Moreover, the (...) motivational nexus is a generative space of possibility and choice. I show that, for these reasons, motivational explanation is not causal, nor deductive nomological, nor does it reduce to subpersonal explanation. I finish with the comment that the uniqueness of personal level explanation points towards the possibility that the human sciences ought also employ types of explanations not found in natural sciences. (shrink)

In a recent article, Erasmus, Brunet, and Fisher (2021) argue that Artificial Neural Networks (ANNs) are explainable. They survey four influential accounts of explanation: the Deductive-Nomological model, the Inductive-Statistical model, the Causal-Mechanical model, and the New-Mechanist model. They argue that, on each of these accounts, the features that make something an explanation is invariant with regard to the complexity of the explanans and the explanandum. Therefore, they conclude, the complexity of ANNs (and other Machine Learning models) does not (...) make them less explainable. In this reply, it is argued that Erasmus et al. left out one influential account of explanation from their discussion: the Unificationist model. It is argued that, on the Unificationist model, the features that makes something an explanation is sensitive to complexity. Therefore, on the Unificationist model, ANNs (and other Machine Learning models) are not explainable. It is emphasized that Erasmus et al.’s general strategy is correct. The literature on explainable Artificial Intelligence can benefit by drawing from philosophical accounts of explanation. However, philosophical accounts of explanation do not settle the problem of whether ANNs are explainable because they do not unanimously declare that explanation is invariant with regard to complexity. (shrink)

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. (shrink)

In this paper I reconstruct and critically examine the reasoning leading to the famous prediction of the ‘omega minus’ particle by M. Gell-Mann and Y. Ne’eman (in 1962) on the basis of a symmetry classification scheme. While the peculiarity of this prediction has occasionally been noticed in the literature, a detailed treatment of the methodological problems it poses has not been offered yet. By spelling out the characteristics of this type of prediction, I aim to underscore the challenges raised by (...) this episode to standard scientific methodology, especially to the traditional deductive-nomological account of prediction. (shrink)

The use of idealizations and approximations in scientific explanations poses a problem for traditional philosophical theories of confirmation since, strictly speaking, these sorts of statements are false. Furthermore, in several central cases in the history of science, theoretical predictions seen as confirmatory are not, in any usual sense, even approximately true. As a means of eliminating the puzzling nature of these cases, two theses are proposed. First, explanations consist of idealized deductive-nomological sketches plus what are called modal auxiliaries, (...) i.e., arguments showing that if the idealizations used in the initial conditions are improved, then there will be an improvement in the prediction. Second, a theory is confirmed if it can be shown that its idealized sketches can be improved; similarly, a theory is disconfirmed if its idealized sketches cannot be improved. Several examples are given to illustrate both confirmation and disconfirmation achieved by means of the modal auxiliary. These cases are compared with Scriven's bridge example. (shrink)

Taking a cue from remarks Thomas Kuhn makes in 1990 about the historical turn in philosophy of science, I examine the history of history and philosophy of science within parts of the British philosophical context in the 1950s and early 1960s. During this time, ordinary language philosophy's influence was at its peak. I argue that the ordinary language philosophers' methodological recommendation to analyze actual linguistic practice influences several prominent criticisms of the deductive-nomological model of scientific explanation and that (...) these criticisms relate to the historical turn in philosophy of science. To show these connections, I primarily examine the work of Stephen Toulmin, who taught at Oxford from 1949 to 1954, and Michael Scriven, who completed a dissertation on explanation under Gilbert Ryle and R.B. Braithwaite in 1956. I also consider Mary Hesse's appeal to an ordinary language-influenced account of meaning in her account of the role of models and analogies in scientific reasoning, and W.H. Watson's Wittgensteinian philosophy of science, an early influence on Toulmin. I think there are two upshots to my historical sketch. First, it fills out details of the move away from logical positivism to more historical- and practice-focused philosophies of science. Second, questions about linguistic meaning and the proper targets and aims of philosophical analysis are part and parcel of the historical turn, as well as its reception. Looking at the philosophical background during which so-called linguistic philosophers also had a hand in bringing these questions to prominence helps us understand why. (shrink)

The current antireductionist consensus rests in part on the indefensibility of the deductive-nomological model of explanation, on which classical reductionism depends. I argue that the DN model is inessential to the reductionist program and that mechanism provides a better framework for thinking about reductionism. This runs counter to the contemporary mechanists’ claim that mechanism is an alternative to reductionism. I demonstrate that mechanists are committed to reductionism, as evidenced by the historical roots of the contemporary mechanist program. This (...) view shares certain core commitments with reductionism. It is these shared commitments that constitute the essential elements of the reductionist program. (shrink)