In earlier publications of the first author it was shown that intentional explanation of actions, functional explanation of biological traits and causal explanation of abnormal events share a common structure. They are called explanation by specification (of a goal, a biological function, an abnormal causal factor, respectively) as opposed to explanation by subsumption under a law. Explanation by specification is guided by a schematic train of thought, of which the argumentative steps not (...) concerning questions were already shown to be logically valid (elementary) arguments.Independently, the second author developed a new, inferential approach to erotetic logic, the logic of questions. In this approach arguments resulting in questions, with declarative sentences and/or other questions as premises, are analyzed, and validity of such arguments is defined. (shrink)

Andrés Rivadulla argues for an instrumentalist approach to the use of theoretical models in the physical sciences, which, on his view, have not to be conceived of as intended representations of the phenomena, but just as useful tools for explaining and predicting those phenomena. He analyses two examples of theoretical models employed for those aims. The first one is the supernova model, intended mainly as explanatory. The second one is the atomic central field shell model, where the postulated internal structure (...) for atoms—their electronic configuration- made the prediction and discovery of new noble gases possible. He concludes that theoretical models can be regarded as artifacts designed with the specific purpose of facilitating calculations. (shrink)

This paper evaluates Kuipers' account of functional explanation in biology in view of an example of such an explanation taken from real biology. The example is the explanation of why electric fishes swim backwards (Lannoo and Lannoo 1993). Kuipers' account depicts the answer to a request for functional explanation as consisting only of statements that articulate a certain kind of consequence. It is argued that such an account fails to do justice to the main insight provided (...) by the example explanation, namely the insight into why backwards swimming is needed by fishes that locate their food by means of an electric radar. The paper sketches an improved account that does justice to this kind of insight. It is argued that this account is consistent with and complementary to Kuipers' insight that function attributions are established by means of a process of hypothetico-deductive reasoning guided by a heuristic principle. (shrink)

The article is a reappraisal of the requirement of maximal specificity (RMS) proposed by the author as a means of avoiding "ambiguity" in probabilistic explanation. The author argues that RMS is not, as he had held in one earlier publication, a rough substitute for the requirement of total evidence, but is independent of it and has quite a different rationale. A group of recent objections to RMS is answered by stressing that the statistical generalizations invoked in probabilistic explanations must (...) be lawlike, and by arguing that predicates fit for occurrence in lawlike statistical probability statements must meet two conditions, at least one of which is violated in each of the counterexamples adduced in the objections. These considerations suggest the conception that probabilistic-statistical laws concern the long-run frequency of some characteristic within a reference class as characterized by some particular "description" or predicate expression, and that replacement of such a description by a coextensive one may turn a statement that is lawlike into another that is not. Finally, to repair a defect noted by Grandy, the author's earlier formulation of RMS is replaced by a modified version. (shrink)

We provide a novel Bayesian justification of inference to the best explanation. More specifically, we present conditions under which explanatory considerations can provide a significant confirmatory boost for hypotheses that provide the best explanation of the relevant evidence. Furthermore, we show that the proposed Bayesian model of IBE is able to deal naturally with the best known criticisms of IBE such as van Fraassen?s?bad lot? argument.

This paper builds a dialectical system of explanation with speech act rules that define the kinds of moves allowed, like requesting and offering an explanation. Pre and post-condition rules for the speech acts determine when a particular speech act can be put forward as a move in the dialogue, and what type of move or moves must follow it. A successful explanation has been achieved when there has been a transfer of understanding from the party giving the (...) explanation to the party asking for it. The dialogue has an opening stage, an explanation stage and a closing stage. Whether a transfer of understanding has taken place is tested by a dialectical shift to an examination dialogue. (shrink)

I distinguish two versions of kin selection theory—a purely genetic version and a version that also appeals to cultural forms of cooperation —and present an argument in favor of using the former when it comes to accounting for the evolution of cooperation in non-human organisms. Specifically, I first show that both GKST and WKST are equally mathematically coherent—they can both be derived from the Price equation—but not necessarily equally empirically plausible, as they are based on different assumptions about the inheritance (...) system underlying the cooperative phenotype. Given this, I then, second, present a model selection theoretic argument in favor of GKST over WKST. This argument is based on the fact that, in non-human cases, the former theory is likely to be as empirically successful as WKST, while containing fewer degrees of freedom. I end by defending both the intrinsic importance of this argument and its relevance to the discussion surrounding the “gene’s eye view of evolution.”. (shrink)

Explanation in biology has long been characterized as being very different from explanation in other scientific disciplines, very much so from explanation in physics. One of the reasons was the existence in biology of explanation types that were unheard of in the physical sciences: teleological explanations (e.g. Hull 1974), evolutionary explanations (e.g. Mayr 1988), or even functional explanations (e.g. Neander 1991). More recently, and owing much to the rise of molecular biology, biological explanations have been depicted (...) as mechanisms (e.g; Machamer, Darden and Craver 2000). The aim of this volume is to shed some new light on the diversity of explanation types in biology. What are the different types of explanation that occur in biology? Are these types of explanation specific to particular sub-disciplines of biology, or to particular types of problems across biology? How do they relate to each another? Do they compete with one another for answering the same questions? Or do they complement each other, providing insights to different questions? What are the reasons for such diversity? Can this diversity be overcome by a broader unifying model of explanation or is it more profound and irreducible? Why? This volume aims at making sense of this diversity of types of explanations that are found in biology, of their relationship with one another. After all, explanation in biology may prove not only different from explanation in the physical sciences, but also much more diverse than originally anticipated. (shrink)

Agent causation presupposes that actions are behaviors under the causal control of the agent’s mental states, its beliefs and desires. Here the idea of conscious causation in causal explanations of actions is examined, specifically, actions said to be the result of conscious efforts. Causal–functionalist theories of consciousness purport to be naturalistic accounts of the causal efficacy of consciousness. Flanagan argues that his causal–functionalist theory of consciousness satisfies naturalistic constraints on causation and that his causal efficacy thesis is compatible with results (...) of Libet’s experiments on conscious causation. First, the notions of conscious effort and conscious causation are analyzed with respect to the project of naturalizing the mind, that is, the attempt to assimilate folk-psychological explanation to the causal model of explanation in the natural sciences. It is argued that a serious obstacle for any naturalist program is that mental states are individuated by their semantic content, not the mechanistic, physical properties of their neural state instantiations. In particular, it is argued that explanation by reference to mental state content yields not a causal but an interpretive or rationalizing account of action in which the question of causal efficacy is irrelevant. Then, Flanagan’s causal–functionalist theory of consciousness is critically assessed; specifically his interpretations of Libet’s negative experimental results on the causal efficacy of consciousness are diagnosed and disputed. It is contended that Flanagan misinterprets the results of Libet’s consciousness experiments and that his functionalist concept of consciousness fails to yield an adequate explanation of the alleged causal efficacy of consciousness. Finally, his thesis is countered with other experimental results that appear to favor an epiphenomenalist view over the causal efficacy account of consciousness. (shrink)

Abstract.Evolutionary psychology and behavioural genomics are both approaches to explain human behaviour from a genetic point of view. Nonetheless, thus far the development of these disciplines is anything but interdependent. This paper examines the question whether evolutionary psychology can contribute to behavioural genomics. Firstly, a possible inconsistency between the two approaches is reviewed, viz. that evolutionary psychology focuses on the universal human nature and disregards the genetic variation studied by behavioural genomics. Secondly, we will discuss the structure of biological explanations. (...) Some philosophers rightly acknowledge that explanations do not involve laws which are exceptionless and universal. Instead, generalisations that are invariant suffice for successful explanation as long as two other stipulations are recognised: the domain within which the generalisation has no exceptions as well as the distribution of the mechanism described by the generalisation should both be specified. It is argued that evolutionary psychology can contribute to behavioural genomic explanations by accounting for these two specifications. (shrink)

Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...) considerably richer than those suggested by a nomological framework. Second, the fact that mechanisms involve organized systems of component parts and operations provides direction to both the discovery and testing of mechanistic explanations. Finally, models of mechanisms are developed for specific exemplars and are not represented in terms of universally quantified statements. Generalization involves investigating both the similarity of new exemplars to those already studied and the variations between them. (shrink)

Explanatory contrastivists hold that we often explain phenomena of the form p rather than q. In this paper, I present a new, social‐epistemological model of contrastive explanation—accountabilism. Specifically, my view is inspired by social‐scientific research that treats explanations fundamentally as accounts; that is, communicative actions that restore one's social status when charged with questionable behaviour. After developing this model, I show how accountabilism provides a more comprehensive model of contrastive explanation than the causal models of contrastive explanation (...) that are currently en vogue. (shrink)

Since approval of the EU General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that the GDPR will legally mandate a ‘right to explanation’ of all decisions made by automated or artificially intelligent algorithmic systems. This right to explanation is viewed as an ideal mechanism to enhance the accountability and transparency of automated decision-making. However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In contrast (...) to the right to explanation of specific automated decisions claimed elsewhere, the GDPR only mandates that data subjects receive meaningful, but properly limited, information (Articles 13-15) about the logic involved, as well as the significance and the envisaged consequences of automated decision-making systems, what we term a ‘right to be informed’. Further, the ambiguity and limited scope of the ‘right not to be subject to automated decision-making’ contained in Article 22 (from which the alleged ‘right to explanation’ stems) raises questions over the protection actually afforded to data subjects. These problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless. We propose a number of legislative and policy steps that, if taken, may improve the transparency and accountability of automated decision-making when the GDPR comes into force in 2018. (shrink)

Existing definitions of relevance relations are essentially ambiguous outside the binary case. Hence definitions of probabilistic causality based on relevance relations, as well as probability values based on maximal specificity conditions and homogeneous reference classes are also not uniquely specified. A 'neutral state' account of explanations is provided to avoid the problem, based on an earlier account of aleatory explanations by the author. Further reasons in support of this model are given, focusing on the dynamics of explanation. It is (...) shown that truth in explanation need not entail maximal specificity and that probabilistic explanations should not contain a specification of probability values. (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)

Aristotle normally begins a teleological explanation of a living thing with an identification of its goods. The existence of these goods implies certain requirements or “hypothetical necessity”. For example, if a fish is to survive and reproduce, it must be able to acquire food, which requires that it move, and so it must have fins, which in turn require tissues, and these must be composed of a certain combination of the elements. Some features of living things are not necessary (...) for its survival, but only as a concomitant to some other necessity, or because it is better for the creature that way. The case of animal behavior shows that Aristotle does not indulge in anthropomorphism: apparently purposeful behavior is not caused by deliberation. In fact, far from assimilating animal to human behavior, Aristotle goes the other direction and shows how many purposeful human activities happen without deliberation. (shrink)

A naturalistic theory of rationalization is defended against a fundamental objection. The theory claims that: The rationalizing relation can be fully analysed in causal explanatory terms. However, is rendered problematic by the fact that: Rationalizations exhibit a higher degree of intensionality than ordinary physical causal explanations. To show that can be maintained in the face of , I develop an account of on which and may be reconciled. ;The opening chapter gives an account of the intensionality of ordinary physical causal (...) explanations by developing a counterfactual analysis of causal relevance. Chapter II examines the nature and intensionality of rationalizations. It is argued that despite , a rationalization partly involves a causal relevance relation between its explanans and explanandum. ;In Chapter III, I consider an argument which implies that is false: Davidson's argument that there can be no psychophysical laws. An attempt is made to give a clear and compelling representation of this argument. As I interpret it, the argument depends on the alleged insolubility of the problem of causal deviance in action. ;Chapter IV develops a general theory of deviance according to which any functional system may produce output in a deviant way, and applies this theory to agents. A negative consequence of the theory is that an action may be performed in a deviant way even if the agent's intention causally explains the action . A positive consequence of the theory is that an action is performed in a non-deviant way just in case there is an appropriate causal explanation of why the agent's intention causes him to act. My specific suggestion is that an agent performs an action in a non-deviant way when and only when his intention causes him to act because he "knows how" to act in that way. ;In the last chapter, it is argued that our solution to the problem of deviance can be adapted to account for in causal explanatory terms. In this way and are reconciled. (shrink)

The articulation of an overarching account of scientific explanation has long been a central preoccupation for the philosophers of science. Although a while ago the literature was dominated by two approaches—a causal account and a unificationist account—today the consensus seems to be that the causal account has won. In this paper, I challenge this consensus and attempt to revive unificationism. More specifically, I aim to accomplish three goals. First, I add new criticisms to the standard anti-unificationist arguments, in order (...) to motivate the need for a revision of the doctrine. Second, and most importantly, I sketch such a revised version. Then I argue that, contrary to widespread belief, the causal account and this revised unificationist account of explanation are compatible. Moreover, I also maintain that the unificationist account has priority, since a most satisfactory theory of explanation can be obtained by incorporating the causal account, as a sub-component of the unificationist account. The driving force behind this reevaluation of the received view in the philosophy of explanation is a reconsideration of the role of scientific understanding. (shrink)

This article reveals that explanatory practice in software engineering is in accordance with pragmatic explanatory pluralism, which states that explanations should at least partially be evaluated by their practical use. More specifically, I offer a defense of the idea that several explanation-types are legitimate in software engineering, and that the appropriateness of an explanation-type depends on (a) the engineer’s interests, and (b) the format of the explanation-seeking question he asks, with this format depending on his interests. This (...) idea is defended by considering examples that are representative for explanatory practice in software engineering. Different kinds of technological explanation are spelled out, and the dependence of their appropriateness on interests and question-formats is extensively illustrated. (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)

Computational models have an increasing impact in social and historical sciences. In this paper, I will focus on a specific type of modelling developed in computational social sciences, an agent-based model. My inquiry will aim to identify the sort of explanatory virtues that such a model could have. I will discuss the suggested possibility of causal explanations but also the recent proposal advanced by Grüne-Yanoff that sees them as potential functional explanations. In the last part I shall make some suggestions (...) on how to advance our inquiry. These will point to the need of a more consistent explanatory context drawing on the way social scientists make use of such models. The other will point to the need of considering a cognitive process that is closely related to the explanatory one - understanding. (shrink)

Fifty years ago, Carl Gustav Hempel published his famous book Aspects of Scientific Explanation. Since then the number of publications on this subject has grown exponentially. An occasion like this deserves to be commemorated. In this article I offer a modest tribute to this great methodologist of science. This paper tackles the uses of explanation in theoretical sciences. In particular it is concerned with the possibility of causal explanations in physics. What I intend to do is to focus (...) on the issue of whether the explanation of Kepler’s empirical laws of the planetary motions can be a causal explanation. More specifically my point is: can the deductive subsumption of Kepler’s 3rd Law under theoretical principles provide a causal explanation for the planetary motions? My answer is a definitive no. As a matter of fact, on occasion subsumptions occur under differing theoretical principles that are incompatible with one another. In such cases we would have incompatible scientific explanations. This is precisely the situation facing the scientific explanation of Kepler’s laws, in particular the third law. Since there exist incompatible gravitational theories, it is impossible for the scientific account of Kepler’s law to be a causal explanation of the planetary motions. This is just one example of the difficulties faced by causal explanations in sciences such as theoretical physics. (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)

Besides mechanistic explanations of phenomena, which have been seriously investigated in the last decade, biology and ecology also include explanations that pinpoint specific mathematical properties as explanatory of the explanandum under focus. Among these structural explanations, one finds topological explanations, and recent science pervasively relies on them. This reliance is especially due to the necessity to model large sets of data with no practical possibility to track the proper activities of all the numerous entities. The paper first defines topological explanations (...) and then explains why topological explanations and mechanisms are different in principle. Then it shows that they are pervasive both in the study of networks—whose importance has been increasingly acknowledged at each level of the biological hierarchy—and in contexts where the notion of selective neutrality is crucial; this allows me to capture the difference between mechanisms and topological explanations in terms of practical modelling practices. The rest of the paper investigates how in practice mechanisms and topologies are combined. They may be articulated in theoretical structures and explanatory strategies, first through a relation of constraint, second in interlevel theories, or they may condition each other. Finally, I explore how a particular model can integrate mechanistic informations, by focusing on the recent practice of merging networks in ecology and its consequences upon multiscale modelling. (shrink)

Philippe Huneman has recently questioned the widespread application of mechanistic models of scientific explanation based on the existence of structural explanations, i.e. explanations that account for the phenomenon to be explained in virtue of the mathematical properties of the system where the phenomenon obtains, rather than in terms of the mechanisms that causally produce the phenomenon. Structural explanations are very diverse, including cases like explanations in terms of bowtie structures, in terms of the topological properties of the system, or (...) in terms of equilibrium. The role of mathematics in bowtie structured systems and in topologically constrained systems has recently been examined in different papers. However, the specific role that mathematical properties play in equilibrium explanations requires further examination, as different authors defend different interpretations, some of them closer to the new-mechanistic approach than to the structural model advocated by Huneman. In this paper, we cover this gap by investigating the explanatory role that mathematics play in Blaser and Kirschner’s nested equilibrium model of the stability of persistent long-term human-microbe associations. We argue that their model is explanatory because: i) it provides a mathematical structure in the form of a set of differential equations that together satisfy an ESS; ii) that the nested nature of the ESSs makes the explanation of host-microbe persistent associations robust to any perturbation; iii) that this is so because the properties of the ESS directly mirror the properties of the biological system in a non-causal way. The combination of these three theses make equilibrium explanations look more similar to structural explanations than to causal-mechanistic explanation. (shrink)

It is shown that three common conditions for scientific explanations are violated by a widely used class of domain-independent explanations. These explanations can accommodate both complex and noncomplex systems and do not require the use of detailed models of system-specific processes for their effectiveness, although they are compatible with such model-based explanations. The approach also shows how a clean separation can be maintained between mathematical representations and empirical content.

Michael Strevens offers an account of causal explanation according to which explanatory practice is shaped by counterbalanced commitments to representing causal influence and abstracting away from overly specific details. In this paper, I challenge a key feature of that account. I argue that what Strevens calls explanatory frameworks figure prominently in explanatory practice because they actually improve explanations. This suggestion is simple but has far-reaching implications. It affects the status of explanations that cite multiply realizable properties; changes the explanatory (...) role of causal factors with small effect; and undermines Strevens’ titular explanatory virtue, depth. This results in greater coherence with explanatory practice and accords with the emphasis that Strevens places on explanatory patterns. Ultimately, my suggestion preserves a tight connection between explanation and the creation of understanding by taking into account explanations’ role in communication. (shrink)

Scientific explanations are widely recognized to have instrumental value by helping scientists make predictions and control their environment. In this paper I raise, and provide a first analysis of, the question whether explanatory proofs in mathematics have analogous instrumental value. I first identify an important goal in mathematical practice: reusing resources from existing proofs to solve new problems. I then consider the more specific question: do explanatory proofs have instrumental value by promoting reuse of the resources they contain? In general, (...) I argue that the answer to this question is “no” and demonstrate this in detail for the theory of mathematical explanation developed by Marc Lange. (shrink)

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)

In this article we analyze the methodological commitments of a radical embodied cognition (REC) approach to social interaction and social cognition, specifically with respect to the explanatory framework it adopts. According to many representatives of REC, such as enactivists and the proponents of dynamical and ecological psychology, sociality is to be explained by (1) focusing on the social unit rather than the individuals that comprise it and (2) establishing the regularities that hold on this level rather than modeling the sub-personal (...) mechanisms that could be said to underlie social phenomena. We point out that, despite explicit commitment, such a view implies an implicit rejection of the mechanistic explanation framework widely adopted in traditional cognitive science (TCS), which, in our view, hinders comparability between REC and these approaches. We further argue that such a position is unnecessary and that enactive mechanistic explanation of sociality is both possible and desirable. We examine three distinct objections from REC against mechanistic explanation, which we dub the decomposability, causality and extended cognition worries. In each case we show that these complaints can be alleviated by either appreciation of the full scope of the mechanistic account or adjustments on both mechanistic and REC sides of the debate. (shrink)

Predictive risk modelling is a computational method used to generate probabilities correlating events. The output of such systems is typically represented by a statistical score derived from various related and often arbitrary datasets. In many cases, the information generated by such systems is treated as a form of evidence to justify further action. This paper examines the nature of the information generated by such systems and compares it with more orthodox notions of evidence found in epistemology. The paper focuses on (...) a specific example to illustrate the issues: The New Zealand Government has proposed implementing a predictive risk modelling system which purportedly identifies children at risk of a maltreatment event before the age of five. Timothy Williamson’s conception of epistemology places a requirement on knowledge that it be explanatory. Furthermore, Williamson argues that knowledge is equivalent to evidence. This approach is compared to the claim that the output of such computational systems constitutes evidence. While there may be some utility in using predictive risk modelling systems, I argue, since an explanatory account of the output of such algorithms that meets Williamson’s requirements cannot be given, doubt is cast upon the resulting statistical scores as constituting evidence on generally accepted epistemic grounds. The algorithms employed in such systems are geared towards identifying patterns which turn out to be good correlations. However, rather than providing information about specific individuals and their exposure to risk, a more valid explanation of a high probability score is that the particular variables related to incidents of maltreatment are just higher amongst certain subgroups in a population than they are amongst others. The paper concludes that any justification of the information generated by such systems is generalised and pragmatic at best and the application of this information to individual cases raises various ethical issues. (shrink)

We defend a noetic account of intramathematical explanation. On this view, a piece of mathematics is explanatory just in case it produces understanding of an appropriate type. We motivate the view by presenting some appealing features of noeticism. We then discuss and criticize the most prominent extant version of noeticism, due to Inglis and Mejía Ramos, which identifies explanatory understanding with the possession of well-organized cognitive schemas. Finally, we present a novel noetic account. On our view, explanatory understanding arises (...) from meeting specific explanatory objectives. We defend a cluster-concept account of explanatory objectives and identify four important subfamilies within the relevant network of resemblance relations. The resulting view is objectivist (in the sense that it takes explanatory success to be a matter of observer-independent fact), broader in scope than why-question-based accounts, compatible with empirical findings on experts’ explanatory judgments, and capable of generalizing (with appropriate provisos) to scientific explanation as a whole. It thus fulfills Friedman’s half-century-old demand for a general and objectivist theory which accounts for the link between explanation and understanding. (shrink)

Panpsychism claims that the vast majority of conscious subjects in our world are inanimate and physical. Ensemble explanations account for striking phenomena by placing them within an ensemble of outcomes, most of which are not striking. This paper develops an explanatory problem for panpsychism: panpsychism renders two appealing ensemble explanations unsatisfactory. Specifically, we argue that panpsychism renders unsatisfactory the multiverse explanation of why a universe supports life and the many-planets explanation of why a planet supports life.

Two of the most influential theories about scientific inference are inference to the best explanation and Bayesianism. How are they related? Bas van Fraassen has claimed that IBE and Bayesianism are incompatible rival theories, as any probabilistic version of IBE would violate Bayesian conditionalization. In response, several authors have defended the view that IBE is compatible with Bayesian updating. They claim that the explanatory considerations in IBE are taken into account by the Bayesian because the Bayesian either does or (...) should make use of them in assigning probabilities to hypotheses. I argue that van Fraassen has not succeeded in establishing that IBE and Bayesianism are incompatible, but that the existing compatibilist response is also not satisfactory. I suggest that a more promising approach to the problem is to investigate whether explanatory considerations are taken into account by a Bayesian who assigns priors and likelihoods on his or her own terms. In this case, IBE would emerge from the Bayesian account, rather than being used to constrain priors and likelihoods. I provide a detailed discussion of the case of how the Copernican and Ptolemaic theories explain retrograde motion, and suggest that one of the key explanatory considerations is the extent to which the explanation a theory provides depends on its core elements rather than on auxiliary hypotheses. I then suggest that this type of consideration is reflected in the Bayesian likelihood, given priors that a Bayesian might be inclined to adopt even without explicit guidance by IBE. The aim is to show that IBE and Bayesianism may be compatible, not because they can be amalgamated, but rather because they capture substantially similar epistemic considerations. 1 Introduction2 Preliminaries3 Inference to the Best Explanation4 Bayesianism5 The Incompatibilist View : Inference to the Best Explanation Contradicts Bayesianism5. 1 Criticism of the incompatibilist view6 Constraint - Based Compatibilism6. 1 Criticism of constraint - based compatibilism7 Emergent Compatibilism7. 1 Analysis of inference to the best explanation7. 1. 1 Inference to the best explanation on specific hypotheses7. 1. 2 Inference to the best explanation on general theories7. 1. 3 Copernicus versus Ptolemy7. 1. 4 Explanatory virtues7. 1. 5 Summary7. 2 Bayesian account8 Conclusion. (shrink)

Philosophers have discussed the relationship of cause and effect from ancient times through our own.Prior to the work of Niels Bohr, these discussions presupposed that successful causal attribution implies explanation.The success of quantum theory challenged this presupposition.Bohr introduced a principle of complementarity that provides a new way of looking at causality and explanation. In this succinct review of the history of these discussions, John Losee presents the philosophical background of debates over the cause-effect relation.He reviews the positions of (...) Aristotle, Rene Descartes, Isaac Newton, David Hume, Immanuel Kant, and John Stuart Mill.He shows how nineteenth-century theories in physics and chemistry were informed by a dominant theory of causality and how specific developments in physics provided the background for the emergence of quantum theory. Problems created for the "causality implies explanation" thesis by the emergence of quantum theory are reviewed in detail.Losee evaluates Bohr's proposals to apply a principle of complementarity within physics, biology, and psychology.He also discusses the feasibility of using complementarity as a principle of interpretation within Christian theology.This volume, which includes an in-depth index, is an essential addition to the libraries of advanced undergraduate and graduate students, philosophers, and those interested in causality and explanation. (shrink)

It’s commonly held that particular moral facts are explained by ‘natural’ or ‘descriptive’ facts, though there’s disagreement over how such explanations work. We defend the view that general moral principles also play a role in explaining particular moral facts. More specifically, we argue that this view best makes sense of some intuitive data points, including the supervenience of the moral upon the natural. We consider two alternative accounts of the nature and structure of moral principles—’the nomic view’ and ‘moral platonism’—before (...) considering in what sense such principles obtain of necessity. (shrink)

The goal of this dissertation is to show that cognitive science, while dealing with the special subject of the mind, can nonetheless fit into the same general model that Bas van Fraassen provides for the natural sciences. The dissertation focuses specifically on the nature of explanation in the cognitive sciences. ;Chapter One makes clear what I take cognitive science to be in the work. The two central cases here are Noam Chomsky's theory of generative syntax, and David Marr's computational (...) theory of vision. The bulk of this chapter is devoted to setting out the structure of the two theories, in order to provide concrete case studies in the sorts of explanations they give. ;Chapter Two sets out the model of explanation employed by considering the relative virtues of several recent approaches--Hempel's classic D-N model, Salmon's statistical relevance model, and Stalnaker and Lewis' counterfactual approach--and concluding with van Fraassen's pragmatics-oriented model. Like many accounts, van Fraassen's takes explanations to be answers to "Why?" questions, and emphasizes the pragmatic machinery at work in question-asking. ;Chapter Three considers two influential views of explanation in cognitive science. Such explanations are often said to be functional or dispositional. I look at proposals of each sort and argue that none of the explanatory strategies singled out can count as the central one in cognitive scientific explanation, because cognitive science has no central explanatory strategy. ;In Chapter Four I hold instead that cognitive science uses all the relevance relations found in natural science. The odd features of cognitive scientific explanation turn instead around the sorts of objects and processes that get posited in its theories--abstract, multiply-instantiable objects that can act as representations, and abstract processes of transformation on these objects. I do not, then, deny that we get new sorts of explanation in cognitive science--only that the source of this novelty has been properly located. (shrink)

My primary focus in this paper is on an objection to Humean account of laws and specifically to David Lewis’ “best systems analysis” (BSA). The objection is that the laws according to the BSA (which I call L-laws) fail to account for the ability of laws to explain. In contrast governing laws (which I will call G-laws) are alleged to account for the role of laws in scientific explanations by virtue of their governing role. If governing is required for laws (...) to be explanatory then Humean accounts like Lewis’ are dead in the water since explanation is central to the role of laws in the sciences. However, I will argue that there are effective rebuttals to arguments that Humean laws don’t explain and that actually it is governing accounts that have difficulty with explanation. (shrink)

In this paper I shall adopt a possible reading of the notions of ‘explanatory indispensability’ and ‘genuine mathematical explanation in science’ on which the Enhanced Indispensability Argument proposed by Alan Baker is based. Furthermore, I shall propose two examples of mathematical explanation in science and I shall show that, whether the EIA-partisans accept the reading I suggest, they are easily caught in a dilemma. To escape this dilemma they need to adopt some account of explanation and offer (...) a plausible answer to the following ‘question of evidence’: What is a genuine mathematical explanation in empirical science and on what basis do we consider it as such? Finally, I shall suggest how a possible answer to the question of evidence might be given through a specific account of mathematical explanation in science. Nevertheless, the price of adopting this standpoint is that the genuineness of mathematical explanations of scientific facts turns out to be dependent on pragmatic constraints and therefore cannot be plugged in EIA and used to establish existential claims about mathematical objects. (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)

In this 1970 introduction to philosophy Mr Taylor concentrates on two central topics - explanation and meaning. He takes the argument far enough to acquaint the reader first-hand with the methods and approach of analytical philosophy, and yet because of the scope of these two topics he is able to introduce many of the traditional philosophical problems in epistemology, metaphysics, ethics, aesthetics, and logic. By this approach he avoids the dangers both of superficiality and of undue technicality. Philosophers are (...) concerned to analyse and describe certain concepts and modes of argument, not to establish facts or conclusions of any sort that can be tested by formal demonstration or controlled observation; their findings cannot be conveniently categorized or graded into a comprehensive and progressive course of studies. Mr Taylor meets this difficulty with his extended discussions of specific topics and questions which have implications over the whole subject. (shrink)

Recent research suggests that processing of irrelevant information can be modulated in a rapid online fashion by contextual information in the task environment depending on the usefulness of that information in different contexts. Congruency effects evoked by irrelevant stimulus attributes are smaller in contexts with high proportions of incongruent trials and larger in contexts with high proportions of congruent trials . The present study investigates these context-adaptation effects in a masked-priming paradigm. Context-specific adaptation effects transfer to stimulus identities that are (...) equiprobale in all contexts – an observation that renders explanations in terms of event-learning processes unlikely. Yet, context-specific effects vanished when the irrelevant information remained unconscious. The results suggest that context-specific adaptation of congruency effects reflect cognitive control operations that alter the processing of irrelevant information depending on the experienced utility of that information for action control. (shrink)

In this 1970 introduction to philosophy Mr Taylor concentrates on two central topics - explanation and meaning. He takes the argument far enough to acquaint the reader first-hand with the methods and approach of analytical philosophy, and yet because of the scope of these two topics he is able to introduce many of the traditional philosophical problems in epistemology, metaphysics, ethics, aesthetics, and logic. By this approach he avoids the dangers both of superficiality and of undue technicality. Philosophers are (...) concerned to analyse and describe certain concepts and modes of argument, not to establish facts or conclusions of any sort that can be tested by formal demonstration or controlled observation; their findings cannot be conveniently categorized or graded into a comprehensive and progressive course of studies. Mr Taylor meets this difficulty with his extended discussions of specific topics and questions which have implications over the whole subject. (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)

The purpose of this paper is to provide a systematic appraisal of the covering law and statistical relevance theories of statistical explanation advanced by Carl G. Hempel and by Wesley C. Salmon, respectively. The analysis is intended to show that the difference between these accounts is inprinciple analogous to the distinction between truth and confirmation, where Hempel's analysis applies to what is taken to be the case and Salmon's analysis applies to what is the case. Specifically, it is argued (...) (a) that statistical explanations exhibit the nomic expectability of their explanandum events, which in some cases may be strong but in other cases will not be; (b) that the statistical relevance criterion is more fundamental than the requirement of maximal specificity and should therefore displace it; and, (c) that if statistical explanations are to be envisioned as inductive arguments at all, then only in a qualified sense since, in particular, the requirement of high inductive probability between explanans and explanandum must be abandoned. (shrink)

Much discussion was inspired by the publication of Harvey Brown's book \textit{Physical Relativity} and the so-called dynamical approach to Special Relativity there advocated. At the center of the debate there is the question about the nature of the relation between spacetime and laws or, more specifically, between spacetime symmetries and the symmetries of laws. Originally, the relation was mainly assumed to be explanatory and the dispute expressed in terms of the arrow of explanation – whether it goes from spacetime (...) (symmetries) to (symmetries of) laws or vice-versa. Not everybody agreed with a setting that involves leaving ontology out. In a recent turn, the relation has been claimed to be analytical or definitional. In this paper I intend to examine critically this claim and propose a way to generally understand the relation between spacetime symmetries and symmetries of laws as deriving from constitutive principles. (shrink)

This paper brings together results from the philosophy and the psychology of explanation to argue that there are multiple concepts of explanation in human psychology. Specifically, it is shown that pluralism about explanation coheres with the multiplicity of models of explanation available in the philosophy of science, and it is supported by evidence from the psychology of explanatory judgment. Focusing on the case of a norm of explanatory power, the paper concludes by responding to the worry (...) that if there is a plurality of concepts of explanation, one will not be able to normatively evaluate what counts as good explanation. (shrink)

This paper examines the status of explanation in the natural sciences and ethics by focusing on the important role of empirical evidence and theoretical properties. As a means of exploring these issues, the debate between Nicholas Sturgeon and Gilbert Harman will serve as a central point in the discussion, since Sturgeon has provided several arguments against Harman's attempt to draw a distinction between scientific and moral explanation. Specifically, Sturgeon holds that the special function of observation and testing, which (...) we commonly believe to be relevant only in science, can be applied to moral cases with equal justification. Overall, Sturgeon's arguments will be exposed as deficient in several respects, which is an aspect of this debate often overlooked. (shrink)