This paper describes the development of theories of scientific explanation since Hempel's earliest models in the 1940ies. It focuses on deductive and probabilistic whyexplanations and their main problems: lawlikeness, explanation-prediction asymmetries, causality, deductive and probabilistic relevance, maximal specifity and homogenity, the height of the probability value. For all of these topic the paper explains the most important approaches as well as their criticism, including the author's own accounts. Three main theses of this paper are: (1) Both deductive and probabilistic explanations (...) are important in science, not reducible to each other. (2) One must distinguish between (cause giving) explanations and (reason giving) justifications and predictions. (3) The adequacy of deductive as well as probabilistic explanations is relative to a pragmatically given background knowledge-which does not exclude, however, the possibility of purely semantic models. (shrink)

The basic theory of scientific understanding presented in Sections 1–2 exploits three main ideas.First, that to understand a phenomenonP (for a given agent) is to be able to fitP into the cognitive background corpusC (of the agent).Second, that to fitP intoC is to connectP with parts ofC (via arguments in a very broad sense) such that the unification ofC increases.Third, that the cognitive changes involved in unification can be treated as sequences of shifts of phenomena inC. How the theory fits (...) typical examples of understanding and how it excludes spurious unifications is explained in detail. Section 3 gives a formal description of the structure of cognitive corpuses which contain descriptive as well as inferential components. The theory of unification is then refined in the light of so called puzzling phenomena, to enable important distinctions, such as that between consonant and dissonant understanding. In Section 4, the refined theory is applied to several examples, among them a case study of the development of the atomic model. The final part contains a classification of kinds of understanding and a discussion of the relation between understanding and explanation. (shrink)

Normic Laws and the Significance of Nonmonotonic Reasoning for Philosophy of Science. Normic laws have the form ‘if A then normally B’. They have been discovered in the explanation debate, but were considered as empirically vacuous (§1). I argue that the prototypical (or ideal) normality of normic laws implies statistical normality (§2), whence normic laws have empirical content. In §3–4 I explain why reasoning from normic laws is nonmonotonic, and why the understanding of the individual case is so important here. (...) After sketching some foundations of nonmonotonic reasoning as developed by AI-researchers (§5), Iargue that normic laws are also the best way to understand ceteris paribus laws (§6). §7 deals with the difference between physical and non-physical disciplines and §9 with the difference between normicity and approximation. In §8 it is shown how nonmonotonic reasoning provides a new understanding of the protection of theories against falsification by auxiliary hypotheses. §10, finally, gives a system- and evolution-theoretical explanation of the deeper reason for the omnipresence of normic laws in practice and science, and forthe connection between ideal and statistical normality. (shrink)

I explore a type of computational social simulation known as artificial societies. Artificial society simulations are dynamic models of real-world social phenomena. I explore the role that these simulations play in social explanation, by situating these simulations within contemporary philosophical work on explanation and on models. Many contemporary philosophers have argued that models provide causal explanations in science, and that models are necessary mediators between theory and data. I argue that artificial society simulations provide causal mechanistic explanations. I conclude that (...) in their current form, these simulations are based on methodologically individualist assumptions that could limit their potential scope of social explanation. (shrink)

A classification of models of reduction into three categories — theory reductionism, explanatory reductionism, and constitutive reductionism — is presented. It is shown that this classification helps clarify the relations between various explications of reduction that have been offered in the past, especially if a distinction is maintained between the various epistemological and ontological issues that arise. A relatively new model of explanatory reduction, one that emphasizes that reduction is the explanation of a whole in terms of its parts is (...) also presented in detail. Finally, the classification is used to clarify the debate over reductionism in molecular biology. It is argued there that while no model from the category of theory reduction might be applicable in that case, models of explanatory reduction might yet capture the structure of the relevant explanations. (shrink)

Causal assessment is the problem of establishing whether a relation between (variable) X and (variable) Y is causal. This problem, to be sure, is widespread across the sciences. According to accredited positions in the philosophy of causality and in social science methodology, invariance under intervention provides the most reliable test to decide whether X causes Y. This account of invariance (under intervention) has been criticised, among other reasons, because it makes manipulations on the putative causal factor fundamental for the causal (...) methodology; consequently, the argument goes, the account is ill-suited to those contexts where manipulations are not performed, for instance, the social sciences. The article aims to extend the account of invariance (under intervention), in a way that manipulations on the putative causal factors are not methodologically fundamental, and yet invariance remains key for causal assessment both in experimental and non-experimental contexts. (shrink)

The issue of causal comparability in the social sciences underlies matters of both generalization and extrapolation. After critiquing two existing interpretations of comparability, due to Hitchcock and Hausman, I propose a distinction between ontological and epistemic comparability. While the former refers to whether two cases are actually comparable, the latter respects that in cases of incomplete information, we need to rely on whatever evidence we have of comparability. I argue, using a political science case study, that in those cases of (...) imperfect information, an epistemic homogeneity criterion can be an adequate justification for generalization. (shrink)

We argue elsewhere that explanatoriness is evidentially irrelevant . Let H be some hypothesis, O some observation, and E the proposition that H would explain O if H and O were true. Then O screens-off E from H: Pr = Pr. This thesis, hereafter “SOT” , is defended by appeal to a representative case. The case concerns smoking and lung cancer. McCain and Poston grant that SOT holds in cases, like our case concerning smoking and lung cancer, that involve frequency (...) data. However, McCain and Poston contend that there is a wider sense of evidential relevance—wider than the sense at play in SOT—on which explanatoriness is evidentially relevant even in cases involving frequency data. This is their main point, but they also contend that SOT does not hold in certain cases not involving frequency data. We reply to each of these points and conclude with some general remarks on screening-off as a test of evidential relevance. (shrink)

The explanatory role of natural selection is one of the long-term debates in evolutionary biology. Nevertheless, the consensus has been slippery because conceptual confusions and the absence of a unified, formal causal model that integrates different explanatory scopes of natural selection. In this study we attempt to examine two questions: (i) What can the theory of natural selection explain? and (ii) Is there a causal or explanatory model that integrates all natural selection explananda? For the first question, we argue that (...) five explananda have been assigned to the theory of natural selection and that four of them may be actually considered explananda of natural selection. For the second question, we claim that a probabilistic conception of causality and the statistical relevance concept of explanation are both good models for understanding the explanatory role of natural selection. We review the biological and philosophical disputes about the explanatory role of natural selection and formalize some explananda in probabilistic terms using classical results from population genetics. Most of these explananda have been discussed in philosophical terms but some of them have been mixed up and confused. We analyze and set the limits of these problems. (shrink)

There are two important philosophical research programmes on explanation in the twentieth century—the search for an account or model of scientific explanation, and the defence of inference to the best explanation as a rational form of inference. These two research programmes have largely developed independently from one another. This paper argues that bringing the two research programmes in contact promises to yield fruitful discussion. I consider and reject two arguments for keeping the two research programmes separate. I outline several issues (...) related to both research programmes that would benefit from inter(sub)disciplinary dialogue. (shrink)

A number of authors have pointed to “convergent evolution” as evidence for the central role of natural selection in shaping predictable trajectories of macroevolution. However, there are numerous conceptual and empirical difficulties that arise in broadly appealing to the frequency of homoplasy as evidence for a non-contingently constrained adaptational design space. Most important is the need to distinguish between convergent (externally constrained) and parallel (internally constrained) evolution, and to consider how the respective frequencies of these significantly different sources of homoplasy (...) affect a strong adaptationist view of life. In this paper, I critically evaluate Simon Conway Morris’s use of the homoplasy literature to support his argument for a non-contingent, counterfactually stable account of macroevolutionary pattern. In so doing, I offer a conception of parallelism which avoids the charge that it differs from convergence merely in degree and not in kind. I argue that although organisms sharing a homoplastic trait will also share varying degrees of homology, it is the underlying developmental homology with respect to the generators directly causally responsible for the homoplastic event that defines parallel evolution and non-arbitrarily distinguishes it from convergence. The notion of “screening-off” is used to distinguish the proximal generators of a homoplastic trait from its more distal genetic causes (such as a master control gene). (shrink)

In a brief passage, David Lewis derives from quantum-theory a dilemma regarding the explanation of chance events which he tries to solve by first distinguishing plain from contrastive why-questions have answers. His brevity warrants elaboration and critique. I endorse his derivation, but I make a structural objection to his solution. Once a further distinction is drawn between different kinds of contrastive why-question, his solution can be modified and refined so as to go some way to meeting this objection. However, it (...) cannot resist a deeper objection to the effect that chance events can be given contrastive explanations after all. The alternative solution to the dilemma that I recommend holds Lewis's conception of the 'essence of chance' to be illusory. (shrink)

Van Fraassen explains rejections and asymmetries in science in terms of his contextual theory of explanation in the same way that scientists explain observable phenomena in the world in terms of scientific theories. I object that van Fraassen’s skeptical view regarding inference to the best explanation together with the English view of rationality jointly imply that the contextual theory is not rationally compelling, so van Fraassen and his epistemic colleagues can rationally disbelieve it. Prasetya replies that the truth of the (...) contextual theory coincides with its empirical adequacy, so the contextual theory is compelling. He also replies that the contextual theory is compelling, provided that van Fraassen’s argument for it instantiates a compelling subset of IBE. I argue that Prasetya’s replies either fail or help scientific realism. (shrink)

Van Fraassen (2007, 2017) consistently uses the English view of rationality to parry criticisms from scientific realists. I assume for the sake of argument that the English view of rationality is tenable, and then argue that it has disastrous implications for van Fraassen’s (1980) contextual theory of explanation, for the empiricist position that T is empirically adequate, and for scientific progress. If you invoke the English view of rationality to rationally disbelieve that your epistemic colleagues’ theories are true, they might, (...) in turn, invoke it to rationally disbelieve that your positive theories are true. (shrink)

The philosophical literature on scientific explanation contains a striking diversity of accounts. I use novel empirical methods to address this fragmentation and assess the importance and generality of explanation in science. My evidence base is a set of 781 articles from one year of the journal Science, and I begin by applying text mining techniques to discover patterns in the usage of “explain” and other words of philosophical interest. I then use random sampling from the data set to develop and (...) test a classification scheme for scientific explanation. My results show that explanation and inference to the best explanation are ubiquitous in science, that they occur across a wide range of scientific disciplines, and that they are a goal of scientific practise. These explanations and inferences to the best explanation come in a diversity forms, which at least partially justifies the fragmentation of philosophical accounts. I draw two methodological lessons: first that text mining can enhance traditional conceptual analysis by establishing facts about word usage; and second that random sampling of cases can increase our confidence that a philosophical account applies in general. These empirical techniques supplement traditional philosophical methods. (shrink)

This chapter contains sections titled: Highlights of Past Literature Current Work Future Work.

argue that natural selection does not explain the genotypic arid phenotypic properties of individuals. On this view, natural selection explains the adaptedness of individuals, not by explaining why the individuals that exist have the adaptations they do, but rather by explaining why the individuals that exist are the ones with those adaptations. This paper argues that this ‘Negative’ view of natural selection ignores the fact that natural selection is a cumulative selection process. So understood, it explains how the genetic sequences (...) that individuals inherit and that are responsible for their complex (and co-adapted) adaptations first arose in the gene-pool. (shrink)

In this paper, I argue that there is neither valid deductive support nor strong inductive support for Kuhn’s incommensurability thesis. There is no valid deductive support for Kuhn’s incommensurability thesis because, from the fact that the reference of the same kind terms changes or discontinues from one theoretical framework to another, it does not necessarily follow that these two theoretical frameworks are taxonomically incommensurable. There is no strong inductive support for Kuhn’s incommensurability thesis, since there are rebutting defeaters against it (...) in the form of episodes from the history of science that do not exhibit discontinuity and replacement, as Kuhn’s incommensurability thesis predicts, but rather continuity and supplementation. If this is correct, then there are no compelling epistemic reasons to believe that Kuhn’s incommensurability thesis is true or probable. (shrink)

Efforts to formalize qualitative accounts of inference to the best explanation (IBE) confront two obstacles: the imprecise nature of such accounts and the unusual logical properties that explanations exhibit, such as contradiction-intolerance and irreflexivity. This paper aims to surmount these challenges by utilising a new, more precise theory that treats explanations as expressions that codify defeasible inferences. To formalise this account, we provide a sequent calculus in which IBE serves as an elimination rule for a connective that exhibits many of (...) the properties associated with the behaviour of the English expression ‘That... best explains why ... ’. We first construct a calculus that encodes these properties at the level of the turnstile, i.e. as a metalinguistic expression for classes of defeasible consequence relations. We then show how this calculus can be conservatively extended over a language that contains a best-explains-why operator. (shrink)

Causal analysis in the social sciences takes advantage of a variety of methods and of a multi-fold source of information and evidence. This pluralistic methodology and source of information raises the question of whether we should accordingly have a pluralistic metaphysics and epistemology. This paper focuses on epistemology and argues that a pluralistic methodology and evidence don’t entail a pluralistic epistemology. It will be shown that causal models employ a single rationale of testing, based on the notion of variation. Further, (...) I shall argue that this monistic epistemology is also involved in alternative philosophical theories of causation. (shrink)

The principle of the common cause demands that every pair of causally independent but statistically correlated events should be the effect of a common cause. This demand is often supplemented with the requirement that said cause should screen-off the two events from each other. This paper introduces a new probabilistic model for common causes, which generalises this requirement to include sets of distinct but non-disjoint causes. It is demonstrated that the model hereby proposed satisfies the explanatory function generally attributed to (...) common causes and an existential proof is offered. Finally, the model is compared to other known structures intended to perform a similar function, such as Reichenbachian Common Cause Systems and Bayesian Networks. (shrink)

The principle of the common cause claims that if an improbable coincidence has occurred, there must exist a common cause. This is generally taken to mean that positive correlations between non-causally related events should disappear when conditioning on the action of some underlying common cause. The extended interpretation of the principle, by contrast, urges that common causes should be called for in order to explain positive deviations between the estimated correlation of two events and the expected value of their correlation. (...) The aim of this paper is to provide the extended reading of the principle with a general probabilistic model, capturing the simultaneous action of a system of multiple common causes. To this end, two distinct models are elaborated, and the necessary and sufficient conditions for their existence are determined. (shrink)

Moral philosophers are, among other things, in the business of constructing moral theories. And moral theories are, among other things, supposed to explain moral phenomena. Consequently, one’s views about the nature of moral explanation will influence the kinds of moral theories one is willing to countenance. Many moral philosophers are (explicitly or implicitly) committed to a deductive model of explanation. As I see it, this commitment lies at the heart of the current debate between moral particularists and moral generalists. In (...) this paper I argue that we have good reasons to give up this commitment. In fact, I show that an examination of the literature on scientific explanation reveals that we are used to, and comfortable with, non-deductive explanations in almost all areas of inquiry. As a result, I argue that we have reason to believe that moral explanations need not be grounded in exceptionless moral principles. (shrink)

Proponents of Evidence-based medicine (EBM) do not provide a clear role for basic science in therapeutic decision making. Of what they do say about basic science, most of it is negative. Basic science resides on the lower tiers of EBM's hierarchy of evidence. Therapeutic decisions, according to proponents of EBM, should be informed by evidence from randomised studies (and systematic reviews of randomised studies) rather than basic science. A framework of models explicates the links between the mechanisms of basic science, (...) experimental inquiry, and observed data. Relying on the framework of models I show that basic science often plays a role not only in specifying experiments, but also analysing and interpreting the data that is provided. Further, and contradicting what is implied in EBM's hierarchy of evidence, appeals to basic science are often required to apply clinical research to therapeutic questions. © 2010 Springer Science+Business Media B.V. (shrink)

Proponents of ontic conceptions of explanation require all explanations to be backed by causal, constitutive, or similar relations. Among their justifications is that only ontic conceptions can do justice to the ‘directionality’ of explanation, i.e., the requirement that if X explains Y , then not-Y does not explain not-X . Using topological explanations as an illustration, we argue that non-ontic conceptions of explanation have ample resources for securing the directionality of explanations. The different ways in which neuroscientists rely on multiplexes (...) involving both functional and anatomical connectivity in their topological explanations vividly illustrate why ontic considerations are frequently (if not always) irrelevant to explanatory directionality. Therefore, directionality poses no problem to non-ontic conceptions of explanation. (shrink)

I analyze the frame problem and its relation to other epistemological problems for artificial intelligence, such as the problem of induction, the qualification problem and the "general" AI problem. I dispute the claim that extensions to logic (default logic and circumscriptive logic) will ever offer a viable way out of the problem. In the discussion it will become clear that the original frame problem is really a fairy tale: as originally presented, and as tools for its solution are circumscribed by (...) Pat Hayes, the problem is entertaining, but incapable of resolution. The solution to the frame problem becomes available, and even apparent, when we remove artificial restrictions on its treatment and understand the interrelation between the frame problem and the many other problems for artificial epistemology. I present the solution to the frame problem: an adequate theory and method for the machine induction of causal structure. Whereas this solution is clearly satisfactory in principle, and in practice real progress has been made in recent years in its application, its ultimate implementation is in prospect only for future generations of AI researchers. (shrink)

Popper has provided a model for the scientific explanation of human actions and a metaphysical theory of man which can guide scientific research. In this paper I discuss the problems of the empirical content and nomicity of the Rationality Principle and extend the method of situational analysis to the problem of explaining beliefs. The domain of applicability of the Rationality Principle is bounded on one side by cases in which behavior is determined by processes which can not be influenced by (...) criticism and on the other side by the phenomenon of substantive creativity. However, a large part of human activity lies within its scope. (shrink)

The Problem of Old Evidence is a perennial issue for Bayesian confirmation theory. Garber 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 :712–717, 2015) and Sprenger :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)