By a valid inference is here understood an inference that succeeds in its aim to justify its conclusion given that its premisses are already justified. For an inference to be valid it is thus not enough that the sentence asserted in the conclusion is a logical consequence of the sentences asserted in the premisses. A proof is understood as a succession of valid inferences that is closed (i.e. all its assumptions are discharged and all its free variables are bound by (...) some of the inferences). On the other hand, in mathematics the justification of an assertion has traditionally been taken to consist in a proof of it, which gives rise to an explanatory circle.In an attempt to avoid this circle, I have in previous works tried to explain the notion of ground for an assertion without referring to valid inferences. However, it turns out that the resulting notion of ground has unwanted consequences like the notion of proof in the so-called BHK-interpretation of intuitionistic logic. For instance, one is guaranteed a ground or a proof of an implication A → B as soon as there is in fact no ground or proof of A, regardless of whether one really knows this fact, and hence without having a real justification of the assertion of the implication.In the absence of a non-circular explanation of the notions of valid inference and proof, I suggest that we should try to formulate a number of principles of how these two epistemic notions depend on each other. A more careful explanation of the validity of an inference results in what I call a heuristic idea about the validity of inference, which is used as a guide to formulate precise principles. One such principle generalizes Gentzen’s idea that his introduction rules define the meaning of the logical constants. Another principle generalizes a previously noted property of Gentzen’s elimination rules that I have called that of satisfying the inversion principle. It results in a notion that I call non-creativity, which implies validity. (shrink)

How far should our realism extend? For many years philosophers of mathematics and philosophers of ethics have worked independently to address the question of how best to understand the entities apparently referred to by mathematical and ethical talk. But the similarities between their endeavours are not often emphasised. This book provides that emphasis. In particular, it focuses on two types of argumentative strategies that have been deployed in both areas. The first—debunking arguments—aims to put pressure on realism by emphasising the (...) seeming redundancy of mathematical or moral entities when it comes to explaining our judgements. In the moral realm this challenge has been made by Gilbert Harman and Sharon Street; in the mathematical realm it is known as the 'Benacerraf-Field' problem. The second strategy—indispensability arguments—aims to provide support for realism by emphasising the seeming intellectual indispensability of mathematical or moral entities, for example when constructing good explanatory theories. This strategy is associated with Quine and Putnam in mathematics and with Nicholas Sturgeon and David Enoch in ethics. Explanation in Ethics and Mathematics addresses these issues through an explicitly comparative methodology which we call the 'companions in illumination' approach. By considering how argumentative strategies in the philosophy of mathematics might apply to the philosophy of ethics, and vice versa, the papers collected here break new ground in both areas. For good measure, two further companions for illumination are also broached: the philosophy of chance and the philosophy of religion. Collectively, these comparisons light up new questions, arguments, and problems of interest to scholars interested in realism in any area. (shrink)

Teleological explanations (TEs) account for the existence or properties of an entity in terms of a function: we have hearts because they pump blood, and telephones for communication. While many teleological explanations seem appropriate, others are clearly not warranted-for example, that rain exists for plants to grow. Five experiments explore the theoretical commitments that underlie teleological explanations. With the analysis of [Wright, L. (1976). Teleological Explanations. Berkeley, CA: University of California Press] from philosophy as a point of departure, we examine (...) in Experiment 1 whether teleological explanations are interpreted causally, and confirm that TEs are only accepted when the function invoked in the explanation played a causal role in bringing about what is being explained. However, we also find that playing a causal role is not sufficient for all participants to accept TEs. Experiment 2 shows that this is not because participants fail to appreciate the causal structure of the scenarios used as stimuli. In Experiments 3-5 we show that the additional requirement for TE acceptance is that the process by which the function played a causal role must be general in the sense of conforming to a predictable pattern. These findings motivate a proposal, Explanation for Export, which suggests that a psychological function of explanation is to highlight information likely to subserve future prediction and intervention. We relate our proposal to normative accounts of explanation from philosophy of science, as well as to claims from psychology and artificial intelligence. (shrink)

Can there be mathematical explanations of physical phenomena? In this paper, I suggest an affirmative answer to this question. I outline a strategy to reconstruct several typical examples of such explanations, and I show that they fit a common model. The model reveals that the role of mathematics is explicatory. Isolating this role may help to re-focus the current debate on the more specific question as to whether this explicatory role is, as proposed here, also an explanatory one.

This paper defends a counterfactual account of explanation, according to which successful explanation requires tracing patterns of counterfactual dependence of a special sort, involving what I call active counterfactuals. Explanations having this feature must appeal to generalizations that are invariant--stable under certain sorts of changes. These ideas are illustrated by examples drawn from physics and econometrics.

Explanations in genetics have intriguing aspects to both biologists and philosophers, and there is no account that satisfactorily elucidates such explanations. The aim of this article is to analyze the kind of explanations usually given in Classical (Transmission) Genetics (CG) and to present in detail the application of an account of explanation as ampliative, specialized nomological embedding to elucidate the such explanations. First, we present explanations in CG in the classical format of inferences with the explanans as the premises (...) and the explanandum as the conclusion and compare them with explanations in other paradigmatic explanatory fields such as Classical Mechanics. Second, we summarize the main aspects discussed in the literature with regard the peculiarities of genetic explanations. Third, we introduce the account of scientific explanation as ampliative, specialized nomological embedding making use of Sneedian structuralism, in particular the notions of theory-net, fundamental law or guiding principle, specialization, and special laws. Finally, we apply this account to the case of CG and show that this analysis sheds light to the intriguing aspects of genetic explanations and remove most of their alleged oddities. (shrink)

Bradford Skow examines important philosophical questions about causation and explanation. His answers rely on a pair of connected distinctions: the distinction between acting and not acting, and that between situations in which an event happens and when something is in some state.

This volume, the second in the Springer series Philosophy of Science in a European Perspective, contains selected papers from the workshops organised by the ESF Research Networking Programme PSE (The Philosophy of Science in a European Perspective) in 2009. Five general topics are addressed: 1. Formal Methods in the Philosophy of Science; 2. Philosophy of the Natural and Life Sciences; 3. Philosophy of the Cultural and Social Sciences; 4. Philosophy of the Physical Sciences; 5. History of the Philosophy of Science. (...) This volume is accordingly divided in five sections, each section containing papers coming from the meetings focussing on one of these five themes. However, these sections are not completely independent and detached from each other. For example, an important connecting thread running through a substantial number of papers in this volume is the concept of probability: probability plays a central role in present-day discussions in formal epistemology, in the philosophy of the physical sciences, and in general methodological debates---it is central in discussions concerning explanation, prediction and confirmation. The volume thus also attempts to represent the intellectual exchange between the various fields in the philosophy of science that was central in the ESF workshops. (shrink)

In this paper I apply the mechanistic account of explanation to engineering science. I discuss two ways in which this extension offers further development of the mechanistic view. First, functional individuation of mechanisms in engineering science proceeds by means of two distinct sub types of role function, behavior function and effect function, rather than role function simpliciter. Second, it offers refined assessment of the explanatory power of mechanistic explanations. It is argued that in the context of malfunction explanations of (...) technical systems, two key desiderata for mechanistic explanations, ‘completeness and specificity’ and ‘abstraction’, pull in opposite directions. I elaborate a novel explanatory desideratum to accommodate this explanatory context, dubbed ‘local specificity and global abstraction’, and further argue that it also holds for mechanistic explanations of malfunctions in the biological domain. The overall result is empirically-informed understanding of mechanistic explanation in engineering science, thus contributing to the ongoing project of understanding mechanistic explanation in novel or relatively unexplored domains. I illustrate these claims in terms of reverse engineering and malfunction explanations in engineering science. (shrink)

I offer an account of the relation between explanations of behaviour in terms of psychological states and explanations in terms of neural states that: makes it transparent how they can be true together; explains why explanations in terms of psychological states are characteristically of behaviour described in general and relational terms, and explains why certain sorts of neurological investigations undermine psychological explanations of behaviour, while others leave them intact. In the course of the argument, I offer an account of implicit (...) theories. (shrink)

In this paper, I outline two strands of evidence for the conclusion that the dynamical approach to cognitive science both seeks and provides covering law explanations. Two of the most successful dynamical models—Kelso’s model of rhythmic finger movement and Thelen et al.’s model of infant perseverative reaching—can be seen to provide explanations which conform to the famous explanatory scheme first put forward by Hempel and Oppenheim. In addition, many prominent advocates of the dynamical approach also express the provision of this (...) kind of explanation as a goal of dynamical cognitive science. I conclude by briefly outlining two consequences. First, dynamical cognitive science’s explanatory style may strengthen its links to the so-called “situated” approach to cognition, but, secondly, it may also undermine the widespread intuition that dynamics is related to emergentism in the philosophy of mind. (shrink)

Explanation and evidence are related in one way that is uncontroversial: evidence can confirm or disconfirm explanations. One explanation of Sally’s cold is that she has a virus; another is that she has a bacterial infection. The available evidence confirms the virus explanation because the evidence supports that colds are caused by viruses, not bacteria. A more interesting question concerns whether explanatory facts themselves provide evidence. That is to say, do we get evidence for p simply by (...) realizing that p, if true, would explain some data? Explanationists answer this question affirmatively, maintaining that explanatoriness is evidence of truth. (shrink)

Although standard Machine Learning models are optimized for making predictions about observations, more and more they are used for making predictions about the results of actions. An important goal of Explainable Artificial Intelligence (XAI) is to compensate for this mismatch by offering explanations about the predictions of an ML-model which ensure that they are reliably action-guiding. As action-guiding explanations are causal explanations, the literature on this topic is starting to embrace insights from the literature on causal models. Here I take (...) a step further down this path by formally defining the causal notions of sufficient explanations and counterfactual explanations. I show how these notions relate to (and improve upon) existing work, and motivate their adequacy by illustrating how different explanations are action-guiding under different circum- stances. Moreover, this work is the first to offer a formal definition of actual causation that is founded entirely in action-guiding explanations. Although the definitions are motivated by a focus on XAI, the analysis of causal explanation and actual causation applies in general. I also touch upon the significance of this work for fairness in AI by showing how actual causation can be used to improve the idea of path-specific counterfactual fairness. (shrink)

We present different constructions for nonprioritized belief revision, that is, belief changes in which the input sentences are not always accepted. First, we present the concept of explanation in a deductive way. Second, we define multiple revision operators with respect to sets of sentences (representing explanations), giving representation theorems. Finally, we relate the formulated operators with argumentative systems and default reasoning frameworks.

The idea at the core of the New Mechanical account of explanation can be summarized in the claim that explaining means showing ‘how things work’. This simple motto hints at three basic features of Mechanistic Explanation (ME): ME is an explanation-how, that implies the description of the processes underlying the phenomenon to be explained and of the entities that engage in such processes. These three elements trace a fundamental contrast with the view inherited from Hume and later (...) from strict logical empiricism (see Creath 2017), focused on epistemic and formal features of science and according to which issues concerning the kind of entities and processes that lie within a theory’s domain are extraneous to science and belong instead to ontology or metaphysics. Philosophers belonging to the new mechanical philosophy believe that the received view of scientific explanation (Hempel 2001), pivoting on the notion of law of nature, overshadows this insight. Since its origin in the 17th century, mechanical philosophy aimed to explain natural phenomena by reducing them to mechanisms. Traditional attempts to define the concept of mechanism involved the identification of a limited set of fundamental elements as, for instance, contact action, action at a distance, inertial motion (see e.g. Hesse 2005), and, more recently, transmission of a mark, or of a conserved quantity (see Frisch, this volume). The new mechanical philosophy rejects this austere characterization of mechanisms and mechanistic explanation and aim at providing a novel, philosophically rigorous explication of the concept of mechanism and of its role in scientific explanation and practice. ME has been adopted with profit in philosophy of special sciences (for instance in biomedical sciences, e.g. in the explanation of chemical transmission at synapses ((Machamer, Darden and Craver 2000), MDC henceforth); but also in social sciences, e.g. the three kinds of social mechanisms in Coleman’s analysis of Max Weber’s account of the role of the Protestant ethic in the growth of capitalism (Hedström and Swedberg 1998)), where exceptionless regularities are rarely ever found. In physics, it is generally possible to formulate explanations in law-based form, with the result that the plurality of explanatory forms might be overlooked. This should not come as a surprise, given that physics was the main inspiration for logical empiricists, and, in particular, Newtonian physics was a template for Hempel’s formulation of the covering law model. However, this situation is unfortunate, since, we will argue, knowing how things work is often part of the explanation of physical phenomena. In this chapter, we provide an introduction to the basic features of ME, with specific focus on its application to physics (section 1). The main part of the chapter is devoted to the defence of two theses: on the one hand, some domains of physics are not compatible with mechanistic reasoning and explanation (section 2); on the other hand, a comprehensive account of explanation in physics can’t dispense with ME (section 3). (shrink)

There are three main approaches to scientific explanation in the philosophical literature. The unification approach claims that science explains by fitting the particular facts and events within a general theoretical framework. The mechanistic approach claims that science explains by identifying mechanisms. According to the manipulationist approach an explanation ought to be such that it can be used to answer a “what-if-things-had-been-different question.” The article examines whether these three approaches are compatible or not in the case of the social (...) sciences, and it concludes by defending explanatory pluralism. (shrink)

The following is an essay on the notion of scientific explanation as unification. In it a new notion of content is used to explicate Michael Friedman's notion of "k-atomicity," and to explicate the notion of the surplus content of hypothesis h relative to evidence e. From this basis an analysis of unification as theoretical reduction is advanced. A second notion of unification, unification as reconciling prima facie incompatible statements, is introduced again with the aid of this new notion of (...) content. More generally, it is argued that rather than seek the essence of scientific explanation we should carefully catalog the various distinct explanatory virtues. Finally it is argue that in particular philosophical explanations put a high premium on the quality of unification through showing how to make compatible seemingly irreconcilable claims. (shrink)

Explanations implicitly end with something that makes sense, and begin with something that does not make sense. A statistical relationship, for example, a numerical fact, does not make sense; an explanation of this relationship adds something, such as causal information, which does make sense, and provides an endpoint for the sense-making process. Does social science differ from natural science in this respect? One difference is that in the natural sciences, models are what need ‘‘understanding.’’ In the social sciences, matters (...) are more complex. There are models, such as causal models, which need to be understood, but also depend on background knowledge that goes beyond the model and the correlations that make it up, which produces a regress. The background knowledge is knowledge of in-filling mechanisms, which are normally made up of elements that involve the direct understanding of the acting and believing subjects themselves. These models, and social science explanations generally, are satisfactory only when they end the regress in this kind of understanding or use direct understanding evidence to decide between alternative mechanism explanations. (shrink)

Recent theoretical and empirical work suggests that explanation and categorization are intimately related. This paper explores the hypothesis that explanations can help structure conceptual representations, and thereby influence the relative importance of features in categorization decisions. In particular, features may be differentially important depending on the role they play in explaining other features or aspects of category membership. Two experiments manipulate whether a feature is explained mechanistically, by appeal to proximate causes, or functionally, by appeal to a function or (...) goal. Explanation type has a significant impact on the relative importance of features in subsequent categorization judgments, with functional explanations reversing previously documented effects of [”]causal status’. The findings suggest that a feature’s explanatory importance can impact categorization, and that explanatory relationships, in addition to causal relationships, are critical to understanding conceptual representation. (shrink)

This chapter explores the epistemic roles played by connectionist models of cognition, and offers a formal analysis of how connectionist models explain. It looks at how other types of computational models explain. Classical artificial intelligence (AI) programs explain using abductive reasoning, or inference to the best explanation; they begin with the phenomena to be explained, and devise rules that can produce the right outcome. The chapter also looks at several examples of connectionist models of cognition, observing what sorts of (...) constraints are used in their design, and how their results are evaluated. It argues that the point of implementing networks roughly analogous to neural structures is to discover and explores the generic mechanisms at work in the brain, not to deduce the precise activities of specific structures. The chapter explores a formal analysis of the explanations offered, which interprets connectionist models and the cognitive theories they represent as sharing membership in a type of mechanism. (shrink)

The recent literature in philosophy of biology has drawn attention to the different sorts of explanations proffered in the biological sciences—we have molecular, biomedical, and evolutionary explanations. Do these explanations all have a common structure or relation that they seek to capture? This paper will answer in the negative. I defend a pluralistic and pragmatic approach to explanation. Using examples from classical population genetics, I argue that formal demonstrations, and even strictly “mathematical truths,” may serve as explanatory in different (...) historical contexts. (shrink)

Many philosophers argue that explanatoriness plays no special role in confirmation – that “inference to the best explanation” (IBE) incorrectly demands giving hypotheses extra credit for their potential explanatory qualities beyond the credit they already deserve for their predictive successes. This paper argues against one common strategy for responding to this thought – that is, for trying to fit IBE within a Bayesian framework. That strategy argues that a hypothesis’ explanatory quality (its “loveliness”) contributes either to its prior probability (...) or to its likelihood. This paper argues that this strategy fails because it must give different treatments to two hypotheses that are unlovely for the very same reason. The strategy therefore loses the insight into scientific reasoning that its reconstruction in terms of IBE is supposed to provide. The paper then provides a Bayesian account of the confirmatory role of explanatoriness that represents explanatory quality as having an impact in the same place for two hypotheses that are unlovely for the very same reason. This approach works by “putting explanation back into IBE” – that is, by invoking hypotheses that refer explicitly to scientific explanation and by invoking the agent's background opinions regarding the kind of explanation that the evidence is liable to have. On this approach, there is no list of “explanatory virtues” the possession of which always helps to make an explanation better. Rather, for different facts, there are different characteristics that our background knowledge of other explanations gives us some reason to expect the given fact's explanation to possess. (shrink)

The likelihood of a statement is often derived by generating an explanation for it and evaluating the plausibility of the explanation. The explanation discounting principle states that people tend to focus on a single explanation; alternative explanations compete with the effect of reducing one another’s credibility. Two experiments tested the hypothesis that this principle applies to inductive inferences concerning the properties of everyday categories. In both experiments, subjects estimated the probability of a series of statements and (...) the conditional probabilities of those conclusions given other related facts. For example, given that most lawyers make good sales people, what is the probability that most psychologists make good sales people? The results showed that when the fact and the conclusion had the same explanation the fact increased people’s willingness to believe the conclusion, but when they had different explanations the fact decreased the conclusion’s credibility. This decrease is attributed to explanation discounting; the explanation for the fact had the effect of reducing the plausibility of the explanation for the conclusion. (shrink)

Our lives, states of health, relationships, behavior, experiences of the natural world, and the technologies that shape our contemporary existence are subject to a superfluity of competing, multi-faceted and sometimes incompatible explanations. Widespread confusion about the nature of "explanation" and its scope and limits pervades popular exposition of the natural sciences, popular history and philosophy of science. This fascinating book explores the way explanations work, why they vary between disciplines, periods, and cultures, and whether they have any necessary boundaries. (...) In other words, Explanations aims to achieve a better understanding of explanation, both within the sciences and the humanities. It features contributions from expert writers from a wide range of disciplines, including science, philosophy, mathematics, and social anthropology. (shrink)

This volume offers a broad, philosophical discussion on mechanical explanations. Coverage ranges from historical approaches and general questions to physics and higher-level sciences . The contributors also consider the topics of complexity, emergence, and reduction. Mechanistic explanations detail how certain properties of a whole stem from the causal activities of its parts. This kind of explanation is in particular employed in explanatory models of the behavior of complex systems. Often used in biology and neuroscience, mechanistic explanation models have (...) been often overlooked in the philosophy of physics. The authors correct this surprising neglect. They trace these models back to their origins in physics. The papers present a comprehensive historical, methodological, and problem-oriented investigation. The contributors also investigate the conditions for using models of mechanistic explanations in physics. The last papers make the bridge from physics to economics, the theory of complex systems and computer science . This book will appeal to graduate students and researchers with an interest in the philosophy of science, scientific explanation, complex systems, models of explanation in physics higher level sciences, and causal mechanisms in science. (shrink)

There is a recent interest within both philosophy of science as well as within epistemology to provide a defensible account of understanding. In the present article I build on insights from previous work in attempt to provide an account of two related forms of understanding in terms of the ability to form rational intentions when using specific types of mental representations. I propose first that “understanding that X” requires that one form a representation of X and, further, that one must (...) be capable of forming rational intentions using this representation across a range of conceivable conditions. I then propose that “understanding why X” requires that one possess a representation of a successful explanation for why X, and that one must be similarly capable of forming rational intentions using this representation across a range of conceivable conditions. I conclude the manuscript by reviewing objections and considering the way this account relates to other literature on explanation and understanding. (shrink)

This collection of new essays explores the nature of explanation and causality.

There is a long tradition in philosophy and the social sciences that emphasizes the meaningfulness of human action. This tradition doubts or even negates the possibility of causal explanations of human action precisely on the basis that human actions have meaning. This article provides an argument in favor of methodological naturalism in the social sciences. It grants the main argument of the Interpretivists, that is, that human actions are meaningful, but it shows how a transformation of a "nexus of meaning" (...) into a "causal nexus" can take place, proposing the "successful transformation argument.". (shrink)

Studdert-Kennedy, Gerald, Evidence and Explanation in Social Science. ... Kauffman, Stuart, "Articulation of Parts Explanation in Biology and the Rational ...

The aim of this paper is threefold. First, I defend the view that for Aristotle ethical inquiry, like all philosophical inquiry, is in the business of seeking scientific explanations. This defense will require (in section II) first describing the basic structure of such explanations and then showing how those explanations can either be found in or endorsed by Aristotle’s ethics. My description of scientific explanation should be relatively uncontroversial, and my subsequent discussion of scientific explanations in Aristotle’s ethics is (...) intended to help us see more clearly just what sorts of things are to count as “the thats” and “the whys” in ethical inquiry. The second aim of my paper is (in section III) to interpret anew the two thorny passages in which Aristotle discusses the role of scientific explanation in ethics. My interpretations further support the view that scientific explanations play a central role in Aristotle’s ethics, but they do so in a way that corrects textual misunderstandings made by scholars who share that view. Lastly (in section IV), I explain how Aristotle thinks scientific explanations in ethics can help us live better lives. Aristotle insists that the ultimate goal of ethics is to become good (EN I.3, 1095a5-6; II.2, 1103b26-29; X.9, 1179a35-b4), and so if scientific explanations are, indeed, necessary for ethics, then they must somehow help achieve that goal. (shrink)

Explanations are very important to us in many contexts: in science, mathematics, philosophy, and also in everyday and juridical contexts. But what is an explanation? In the philosophical study of explanation, there is long-standing, influential tradition that links explanation intimately to causation: we often explain by providing accurate information about the causes of the phenomenon to be explained. Such causal accounts have been the received view of the nature of explanation, particularly in philosophy of science, since (...) the 1980s. However, philosophers have recently begun to break with this causal tradition by shifting their focus to kinds of explanation that do not turn on causal information. The increasing recognition of the importance of such non-causal explanations in the sciences and elsewhere raises pressing questions for philosophers of explanation. What is the nature of non-causal explanations – and which theory best captures it? How do non-causal explanations relate to causal ones? How are non-causal explanations in the sciences related to those in mathematics and metaphysics? This volume of new essays explores answers to these and other questions at the heart of contemporary philosophy of explanation. The essays address these questions from a variety of perspectives, including general accounts of non-causal and causal explanations, as well as a wide range of detailed case studies of non-causal explanations from the sciences, mathematics and metaphysics. (shrink)

I shall endeavor to show that every physical theory since Newton explains without drawing attention to causes–that, in other words, physical theories as physical theories aspire to explain under an ideal quite distinct from that of causal explanation. If I am right, then even if sometimes the explanations achieved by a physical theory are not in violation of the standard of causal explanation, this is purely an accident. For physical theories, as I will show, do not, as such, (...) aim at accommodating the goals or aspirations of causal explanation. This will serve as the founding insight for a new theory of explanation, which will itself serve as the cornerstone of a new theory of scientific method. (shrink)

The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes (...) a robust argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the "reviewed view" of scientific explanation to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts. (shrink)

Sally Haslanger undertakes groundbreaking work in developing an account of structural explanations and the social structures that figure in them. A chief virtue of the account is that it can show the importance of structural explanations while also respecting the role of individual autonomy in explaining many decisions, by demonstrating the way in which social structures may set up a ‘choice architecture’ in which these choices are made. This paper gives an overview of this achievement, and goes on to consider (...) why there may be need to broaden the role of social structural explanations beyond those that involve explicit choice within a choice architecture. It develops the idea, familiar from work by Heidegger and Ingarden, that social artifacts, roles, and nodes in social structures may be constitutively defined by norms. It closes by suggesting that attention to the role of norms in social structures may enable us to broaden the account to include structural explanations of other kinds. (shrink)

The ontic conception of explanation is predicated on the proposition that “explanation is a relation between real objects in the world” and hence, according to this approach, scientific explanation cannot take place absent such a premise. Despite the fact that critics have emphasized several drawbacks of the ontic conception, as for example its inability to address the so-called “abstract explanations”, the debate is not settled and the ontic view can claim to capture cases of explanation that (...) are non-abstract, such as causal relations between events. However, by eliminating the distinction between abstract and non-abstract explanations, it follows that ontic and epistemic proposals can no longer contend to capture different cases of explanation and either all are captured by the ontic view or all are captured by the epistemic view. On closer inspection, it turns out that the ontic view deals with events that fall outside the scientists’ scope of observation and that it does not accommodate common instances of explanation such as explanations from false propositions and hence it cannot establish itself as the dominant philosophical stance with respect to explanation. On the contrary, the epistemic conception does account for almost all episodes of explanation and can be described as a relation between representations, whereby the explanans transmit information to the explanandum and that this information can come, dependent on context, in the form of any of the available theories of explanation (law-like, unificatory, causal and non-causal). The range of application of the ontic view thus is severely restricted to trivial cases of explanation that come through direct observation of the events involved in an explanation and explanation is to be mostly conceived epistemically. (shrink)

_Explanation and Power _ was first published in 1988. Minnesota Archive Editions uses digital technology to make long-unavailable books once again accessible, and are published unaltered from the original University of Minnesota Press editions. The meaning of any utterance or any sign is the response to that utterance or sign: this is the fundamental proposition behind Morse Peckham's _Explanation and Power. Published_ in 1979 and now available in paperback for the first time, _Explanation and Power _grew out of Peckham's efforts, (...) as a scholar of Victorian literature, to understand the nature of Romanticism. His search ultimately led back to—and built upon—the tradition of signs developed by the American Pragmatists. Since, in Peckham's view, meaning is not inherent in word or sign, only in response, human behavior itself must depend upon interaction, which in turn relies upon the stability of verbal and nonverbal signs. In the end, meaning can be stabilized only by explanation, and when explanation fails, by force. Peckham's semiotic account of human behavior, radical in its time, contends with the same issues that animate today's debates in critical theory — how culture is produced, how meaning is arrived at, the relation of knowledge to power and of society to its institutions. Readers across a wide range of disciplines, in the humanities and social sciences, will welcome its reappearance. (shrink)

The Problem of Old Evidence is a perennial issue for Bayesian confirmation theory. Garber (Test Sci Theor 10:99–131, 1983) famously argues that the problem can be solved by conditionalizing on the proposition that a hypothesis deductively implies the existence of the old evidence. In recent work, Hartmann and Fitelson (Philos Sci 82(4):712–717, 2015) and Sprenger (Philos Sci 82(3):383–401, 2015) aim for similar, but more general, solutions to the Problem of Old Evidence. These solutions are more general because they allow the (...) explanatory relationship between a new hypothesis and old evidence to be inductive, rather than deductive. In this paper, I argue that these solutions are either unsound or under-motivated, depending on the case of inductive explanation that we have in mind. This lends support to the broader claim that Garber–Style Bayesian confirmation cannot capture the sense in which new hypotheses that do not deductively imply old evidence nevertheless seem to be confirmed via old evidence. (shrink)

This paper provides a new approach to inference to the best explanation based on a new coherence measure for comparing how well hypotheses explain the evidence. It addresses a number of criticisms of the use of probabilistic measures in this context by Clark Glymour, including limitations of earlier work on IBE. Computer experiments are used to show that the new approach finds the truth with a high degree of accuracy in hypothesis selection tasks and that in some cases its (...) accuracy is greater than hypothesis selection based on maximizing posterior probability. Hence, by overcoming some of the problems with the previous approach, this work provides a more adequate defence of IBE and suggests that IBE not only tracks truth but also has practical advantages over the previous approach. Applications of the new approach to parameter estimation and model selection are also explored. (shrink)