There has been a growing trend to include non-causal models in accounts of scientific explanation. A worry addressed in this paper is that without a higher threshold for explanation there are no tools for distinguishing between models that provide genuine explanations and those that provide merely potential explanations. To remedy this, a condition is introduced that extends a veridicality requirement to models that are empirically underdetermined, highly-idealised, or otherwise non-causal. This condition is applied to models of electroweak symmetry breaking beyond (...) the Standard Model. (shrink)

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

Although scientific realism is the default position in the life sciences, philosophical accounts of realism are geared towards physics and run into trouble when applied to fields such as biology or neuroscience. In this paper, I formulate a new robustness-based version of entity realism, and show that it provides a plausible account of realism for the life sciences that is also continuous with scientific practice. It is based on the idea that if there are several independent ways of measuring, detecting (...) or deriving something, then we are justified in believing that it is real. I also consider several possible objections to robustness-based entity realism, discuss its relationship to ontic structural realism, and show how it has the potential to provide a novel response to the pessimistic induction argument. (shrink)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...) lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature. (shrink)

Explaining the behaviour of ecosystems is one of the key challenges for the biological sciences. Since 2000, new-mechanicism has been the main model to account for the nature of scientific explanation in biology. The universality of the new-mechanist view in biology has been however put into question due to the existence of explanations that account for some biological phenomena in terms of their mathematical properties (mathematical explanations). Supporters of mathematical explanation have argued that the explanation of the behaviour of ecosystems (...) is usually provided in terms of their mathematical properties, and not in mechanistic terms. They have intensively studied the explanation of the properties of ecosystems that behave following the rules of a non-random network. However, no attention has been devoted to the study of the nature of the explanation in those that form a random network. In this paper, we cover that gap by analysing the explanation of the stability behaviour of the microbiome recently elaborated by Coyte and colleagues, to determine whether it fits with the model of explanation suggested by the new-mechanist or by the defenders of mathematical explanation. Our analysis of this case study supports three theses: (1) that the explanation is not given solely in terms of mechanisms, as the new-mechanists understand the concept; (2) that the mathematical properties that describe the system play an essential explanatory role, but they do not exhaust the explanation; (3) that a non-previously identified appeal to the type of interactions that the entities in the network can exhibit, as well as their abundance, is also necessary for Coyte and colleagues’ account to be fully explanatory. From the combination of these three theses we argue for the necessity of an integrative pluralist view of the nature of behaviour explanation when this is given by appealing to the existence of a random network. (shrink)

In the modern debate in metaethics and moral psychology, moral rationalism is often presented as a view that cannot account for the intimate relation between moral behaviour on one hand and feelings, emotions, or desires on the other. Although there is no lack of references to the classic rationalists of the 18th century in the relevant discussions, the works of these writers are rarely ever examined detail. Yet, as the debate in Kant scholarship between “intellectualists” and “affectivists” impressively shows, a (...) more thorough analysis of what the classic rationalists actually have to say about moral motivation is suited to cast serious doubts on the idea that moral rationalism must crucially neglect the affective–conative side of human psychology. The aim of this paper is to analyse the conceptions of moral motivation that were embraced by Kant's rationalist predecessors—Clarke, Wolff, Burnet, Balguy, and Price—which have not attracted a similar amount of attention by specialists so far. The claim I will defend is that none of those early rationalists actually embraces the motivational thesis that is often taken to be characteristic of moral rationalism, a thesis I shall refer to as strong rationalism about (moral) motivation. (shrink)

This paper sketches a causal account of scientific explanation designed to sustain the judgment that high-level, detail-sparse explanations—particularly those offered in biology—can be at least as explanatorily valuable as lower-level counterparts. The motivating idea is that complete explanations maximize causal economy: they cite those aspects of an event’s causal run-up that offer the biggest-bang-for-your-buck, by costing less (in virtue of being abstract) and delivering more (in virtue making the event stable or robust).

Economists use various models to explain why it is that firms are capable of pricing above marginal cost. In this paper, we will examine two of them: the Cournot and Bertrand duopoly models. Economists generally accept both models as good explanations of the phenomenon, but the two models contradict each other in various important ways. The puzzle is that two inconsistent explanations are both regarded as good explanations for the same phenomenon. This becomes especially worrisome when the two models are (...) offering divergent policy recommendations. This report presents that puzzle by laying out how the two models contradict each other in a myriad of ways and then offers five possible solutions to that puzzle from various economists, philosophers of science, and philosophers of economics. (shrink)

I consider recent strategies proposed by econometricians for extrapolating causal effects from experimental to target populations. I argue that these strategies fall prey to the extrapolator’s circle: they require so much knowledge about the target population that the causal effects to be extrapolated can be identified from information about the target alone. I then consider comparative process tracing as a potential remedy. Although specifically designed to evade the extrapolator’s circle, I argue that CPT is unlikely to facilitate extrapolation in typical (...) econometrics and evidence-based policy applications. To argue this, I offer a distinction between two kinds of extrapolation, attributive and predictive, the latter being prevalent in econometrics and evidence-based policy. I argue that CPT is not helpful for predictive extrapolation when using the kinds of evidence that econometricians and evidence-based policy researchers prefer. I suggest that econometricians may need to consider qualitative evidence to overcome this problem. (shrink)

Stemming from the realization of the importance of the role of explanation in the science classroom, the Next Generation Science Standards call for appropriately supporting students to learn science, argue from evidence, and provide explanations. Despite the ongoing emphasis on explanations in the science classroom, there seems to be no well-articulated framework that supports students in constructing adequate scientific explanations, or that helps teachers assess student explanations. Our motivation for this article is twofold: First, we think that the ways in (...) which researchers in science education have studied scientific explanation are, at best, leaves much to be desired and, at worst, simply incomplete. Second, we believe that research about the teaching and learning of explanation in science classrooms must be guided by explicit models or frameworks that specify elements involved in constructing explanations particularly applicable to science. More importantly, we think that the development of such models or guidelines should be based on theoretical and philosophical foundations. In order to develop these frameworks or guidelines, we first outline and clarify models of scientific explanation developed by philosophers of science over the last few decades. In the second section of this article, we present a more recent philosophical work on scientific explanation, the pragmatic approach to studying scientific explanations. This approach suggests a toolbox for analyzing scientists’ scientific explanations, which provides a useful instrument to science education. In Section 3, we discuss the ways by which the previous two sections are useful in developing a K-12 scientific explanation schema. Implications for future research on students’ explanations are discussed. (shrink)

The primary aim of this chapter is to explain the nature of evidential reasoning, the characteristic difficulties encountered, and the tools to address these difficulties. Our focus is on evidential reasoning in criminal cases. There is an extensive scholarly literature on these topics, and it is a secondary aim of the chapter to provide readers the means to find their way in historical and ongoing debates.

Lange’s collection of expanded, mostly previously published essays, packed with numerous, beautiful examples of putatively non-causal explanations from biology, physics, and mathematics, challenges the increasingly ossified causal consensus about scientific explanation, and, in so doing, launches a new field of philosophic investigation. However, those who embraced causal monism about explanation have done so because appeal to causal factors sorts good from bad scientific explanations and because the explanatory force of good explanations seems to derive from revealing the relevant causal (or (...) ontic) structures. The taxonomic project of collecting examples and sorting their types is an essential starting place for a theory of non-causal explanation. But the title of Lange’s book requires something further: showing that the putative explanations are, in fact, explanatory and revealing the non-causal source of their explanatory power. This project is incomplete if there are examples of putative non-causal explanations that fit the form but that nobody would accept as explanatory (absent a radical revision of intuitions). Here we provide some reasons for thinking that there are such examples. (shrink)

Can mathematics contribute to our understanding of physical phenomena? One way to try to answer this question is by getting involved in the recent philosophical dispute about the existence of mathematical explanations of physical phenomena. If there is such a thing, given the relation between explanation and understanding, we can say that there is an affirmative answer to our question. But what if we do not agree that mathematics can play an explanatory role in science? Can we still consider that (...) the above question can have an affirmative answer? My main aim here is to give an account that takes mathematics, in some of the cases discussed in the literature, as contributing to our understanding of physical phenomena despite not being explanatory. (shrink)

Theories of explanation need to account for a puzzling feature of our explanatory practices: the fact that we prefer explanations that are relatively abstract but only moderately so. Contra Franklin-Hall ([2016]), I argue that the interventionist account of explanation provides a natural and elegant explanation of this fact. By striking the right balance between specificity and generality, moderately abstract explanations optimally subserve what interventionists regard as the goal of explanation, namely identifying possible interventions that would have changed the explanandum.

I provide a theory of causation within the causal modeling framework. In contrast to most of its predecessors, this theory is model-invariant in the following sense: if the theory says that C caused (didn't cause) E in a causal model, M, then it will continue to say that C caused (didn't cause) E once we've removed an inessential variable from M. I suggest that, if this theory is true, then we should understand a cause as something which transmits deviant or (...) non-inertial behavior to its effect. (shrink)

Recent literature on non-causal explanation raises the question as to whether explanatory monism, the thesis that all explanations submit to the same analysis, is true. The leading monist proposal holds that all explanations support change-relating counterfactuals. We provide several objections to this monist position. 1Introduction2Change-Relating Monism's Three Problems3Dependency and Monism: Unhappy Together4Another Challenge: Counterfactual Incidentalism4.1High-grade necessity4.2Unity in diversity5Conclusion.

Der Artikel führt den Begriff der epistemischen Konkurrenz ein. Im Gegensatz zu „wissenschaftliche Kontroverse“ beschreibt er eine Situation, in der sich zwei Forschungsfelder gegenseitig als mit demselben Bereich von Phänomen befasst wahrnehmen, wobei ihre methodischen Ansätze und theoretischen Erklärungen jedoch so unterschiedlich sind, dass ein offener Konflikt über die Wahrheit oder Falschheit bestimmter Aussagen oder die Genauigkeit in der Anwendung einer Methode nicht stattfindet. Nichtsdestotrotz streben beide Parteien danach, die maßgebliche Erklärung der entsprechenden Phänomene anzubieten. Indem die erweiterte Gemeinschaft der (...) Forschenden oder die Öffentlichkeit einen Ansatz als maßgeblich anerkennt, handeln sie als eine dritte, gewissermaßen neutrale Partei, die einen Preis vergibt, um den die anderen Parteien konkurrieren. Der Artikel beschreibt das Verhältnis von Genetik und Entwicklungsbiologie um 1900 als epistemische Konkurrenz. Diese Forschungsfelder geben unterschiedliche Erklärungen für die Phänomene der organischen Reproduktion. Die Erklärungen unterscheiden sich bezüglich der Formbegriffe und entsprechend hinsichtlich der Begriffe der Vererbung von Aspekten der Form eines Organismus. Zudem basieren die Erklärungen der beiden Felder auf unterschiedlichen Arten kausalen Schließens, die in unterschiedliche experimentelle Ansätze eingebettet sind. Diese Unterschiede können als Instanzen eines allgemeineren Unterschieds zwischen der Tradition der Naturgeschichte, die sich mit Unterschieden zwischen Organismen auseinandersetzt, und der Tradition der Anatomie, die sich mit den Teilen der Organismen beschäftigt, angesehen werden. Dieses Bild der Genetik und Entwicklungsbiologie als Zweige unterschiedlicher fest verankerter Traditionen widerspricht dem Narrativ der Trennung des Begriffs der Vererbung von dem der Entwicklung im Zuge einer Trennung der Disziplinen der Genetik und der Embryologie, das in der Historiographie der Biologie nach wie vor weit verbreitet ist. (shrink)

According to manipulationist accounts of causal explanation, to explain an event is to show how it could be changed by intervening on its cause. The relevant change must be a ‘serious possibility’ claims Woodward 2003, distinct from mere logical or physical possibility—approximating something I call ‘scientific possibility’. This idea creates significant difficulties: background knowledge is necessary for judgments of possibility. Yet the primary vehicles of explanation in manipulationism are ‘invariant’ generalisations, and these are not well adapted to encoding such knowledge, (...) especially in the social sciences, as some of it is non-causal. Ceteris paribus (CP) laws or generalisations labour under no such difficulty. A survey of research methods such as case and comparative studies, randomised control trials, ethnography, and structural equation modeling, suggests that it would be more difficult and in some instances impossible to try to represent the output of each method in invariant generalisations; and that this is because in each method causal and non-causal background knowledge mesh in a way that cannot easily be accounted for in manipulationist terms. Ceteris paribus-generalisations being superior in this regard, a theory of explanation based on the latter is a better fit for social science. (shrink)

Many psychologists studying lay belief attribution and behavior explanation cite Donald Davidson in support of their assumption that people construe beliefs as inner causes. But Davidson’s influential argument is unsound; there are no objective grounds for the intuition that the folk construe beliefs as inner causes that produce behavior. Indeed, recent experimental work by Ian Apperly, Bertram Malle, Henry Wellman, and Tania Lombrozo provides an empirical framework that accords well with Gilbert Ryle’s alternative thesis that the folk construe beliefs as (...) patterns of living that contextualize behavior. (shrink)

This essay develops an inferential account of model explanation, based on Mauricio Suárez’s inferential conception of scientific representation and Alisa Bokulich’s counterfactual account of model explanation. It is suggested that the fact that a scientific model can explain is essentially linked to how a modeler uses an established model to make various inferences about the target system on the basis of results derived from the model. The inference practice is understood as a two-step activity, with the first step involving making (...) counterfactual statements about the model itself and the second step involving making hypothetical statements transferring over claims derived from the model onto the target. To illustrate how this two-step activity proceeds, an agent-based simulation model is discussed. (shrink)

Recently some philosophers suggested an exclusion problem for moral non-naturalism, which is similar to the exclusion problem in philosophy of mind. In this article, the author aims to advance the discussion of exclusion in morality by investigating two influential solutions to the exclusion problem: the autonomy solution and the overdetermination solution. The author attempts to show that the moral non-naturalist can solve the exclusion problem in a way that is different from the approach to solving mental-physical exclusion. First, the author (...) argues that while the autonomy solution to mental exclusion may work, a similar autonomy approach to moral exclusion is implausible. Second, the author argues that whereas the overdetermination solution to mental exclusion fails, a similar overdetermination solution to moral exclusion is promising. (shrink)

Mechanistic explanation is at present the received view of scientific explanation. One of its central features is the idea that mechanistic explanations are both “downward looking” and “upward looking”: they explain by offering information about the internal constitution of the mechanism as well as the larger environment in which the mechanism is situated. That is, they offer both constitutive and contextual explanatory information. Adequate mechanistic explanations, on this view, accommodate the full range of explanatory factors both “above” and “below” the (...) target phenomenon. The aim of this paper is to demonstrate that mechanistic explanation cannot furnish both constitutive and contextual information simultaneously, because these are different types of explanation with distinctly different aims. Claims that they can, I argue, depend on several intertwined confusions concerning the nature of explanation. Particularly, such claims tend to conflate mechanistic and functional explanation, which I argue ought to be understood as distinct. Conflating them threatens to oversell the explanatory power of mechanisms and obscures the means by which they explain. I offer two broad reasons in favor of distinguishing mechanistic and functional explanation: the first concerns the direction of explanation of each, and the second concerns the type of questions to which these explanations offer answers. I suggest an alternative picture on which mechanistic explanation is understood as fundamentally constitutive, and according to which an adequate understanding of a phenomenon typically requires supplementing the mechanistic explanation with a functional explanation. (shrink)

We show that five important elements of the ‘nomological package’— laws, counterfactuals, chances, dispositions, and counterfactuals—needn’t be a problem for the Growing-Block view. We begin with the framework given in Briggs and Forbes (in The real truth about the unreal future. Oxford studies in metaphysics. Oxford University Press, Oxford, 2012 ), and, taking laws as primitive, we show that the Growing-Block view has the resources to provide an account of possibility, and a natural semantics for non-backtracking causal counterfactuals. We show (...) how objective chances might ground a more fine-grained concept of feasibility, and furnished a places in the structure where causation and dispositions might fit. The Growing-Block view, thus understood, provides the resources to explain the close link between modality and tense, so that it predicts modal change as time passes. This account lets us capture not only what the future might hold for us, and also what might have been. (shrink)

I argue that transference is, ultimately, identity over time, and that identity over time can't possibly be causation. Transference, then, fails as an analysis of causation.

The transference theory reduces causation to the transmission of physical conserved quantities, like energy or momenta. Although this theory aims at applying to all felds of physics, we claim that it fails to account for a quantum electrodynamic effect, viz. the Aharonov-Bohm effect. After having argued that the Aharonov-Bohm effect is a genuine counter-example for the transference theory, we offer a new physicalist approach of causation, ontic and modal, in which this effect is embedded.

Traditionally, a scientific model is thought to provide a good scientific explanation to the extent that it satisfies certain scientific goals that are thought to be constitutive of explanation. Problems arise when we realize that individual scientific models cannot simultaneously satisfy all the scientific goals typically associated with explanation. A given model’s ability to satisfy some goals must always come at the expense of satisfying others. This has resulted in philosophical disputes regarding which of these goals are in fact necessary (...) for explanation, and as such which types of models can and cannot provide explanations. Explanatory monists argue that one goal will be explanatory in all contexts, while explanatory pluralists argue that the goal will vary based on pragmatic considerations. In this paper, I argue that such debates are misguided, and that both monists and pluralists are incorrect. Instead of any goal being given explanatory priority over others in a given context, the different goals are all deeply dependent on one another for their explanatory power. Any model that sacrifices some explanatory goals to attain others will always necessarily undermine its own explanatory power in the process. And so when forced to choose between individual scientific models, there can be no explanatory victors. Given that no model can satisfy all the goals typically associated with explanation, no one model in isolation can provide a good scientific explanation. Instead we must appeal to collections of models. Collections of models provide an explanation when they satisfy the web of interconnected goals that justify the explanatory power of one another. (shrink)

This paper advances the thesis of methodological mechanism, the claim that to be committed to mechanism is to adopt a certain methodological postulate, i.e. to look for causal pathways for the phenomena of interest. We argue that methodological mechanism incorporates a minimal account of understanding mechanisms, according to which a mechanism just is a causal pathway described in the language of theory. In order to argue for this position we discuss a central example of a biological mechanism, the mechanism of (...) cell death, known as apoptosis. We argue that this example shows that our philosophically deflationary account is sufficient in order to have an illuminating account of mechanisms as the concept is used in biology. (shrink)

This article aims to develop a new account of scientific explanation for computer simulations. To this end, two questions are answered: what is the explanatory relation for computer simulations? And what kind of epistemic gain should be expected? For several reasons tailored to the benefits and needs of computer simulations, these questions are better answered within the unificationist model of scientific explanation. Unlike previous efforts in the literature, I submit that the explanatory relation is between the simulation model and the (...) results of the simulation. I also argue that our epistemic gain goes beyond the unificationist account, encompassing a practical dimension as well. (shrink)

Concrete Causation centers about theories of causation, their interpretation, and their embedding in metaphysical-ontological questions, as well as the application of such theories in the context of science and decision theory. The dissertation is divided into four chapters, that firstly undertake the historical-systematic localization of central problems (chapter 1) to then give a rendition of the concepts and the formalisms underlying David Lewis' and Judea Pearl's theories (chapter 2). After philosophically motivated conceptual deliberations Pearl's mathematical-technical framework is drawn on for (...) an epistemic interpretation and for emphasizing the knowledge-organizing aspect of causality in an extension of the interventionist Bayes net account of causation (chapter 3). Integrating causal and non-causal knowledge in unified structures ultimately leads to an approach towards solving problems of (causal) decision theory and at the same time facilitates the representation of logical-mathematical, synonymical, as well as reductive relationships in efficiently structured, operational nets of belief propagation (chapter 4). (shrink)

This paper attempts to reconcile critics and defenders of inclusive fitness by constructing a synthesis that does justice to the insights of both. I argue that criticisms of the regression-based version of Hamilton’s rule, although they undermine its use for predictive purposes, do not undermine its use as an organizing framework for social evolution research. I argue that the assumptions underlying the concept of inclusive fitness, conceived as a causal property of an individual organism, are unlikely to be exactly true (...) in real populations, but they are approximately true given a specific type of weak selection that Hamilton took, on independent grounds, to be responsible for the cumulative assembly of complex adaptation. Finally, I reflect on the uses and limitations of “design thinking” in social evolution research. The debate about the foundations of inclusive fitness theory that has followed in the wake of Nowak, Tarnita and Wilson’s (1) critique has been remarkably polarizing. After several rounds of rebuttals and replies, there is still little evidence of any serious reconciliation between the theory’s critics (2-10) and its defenders (11-24). It doesn’t have to be this way. I believe that, on the main points of disagreement, it is possible to find a way forward that does justice to the insights of both camps. My aim in this paper is to find that way forward. (shrink)

If there is content that we reason on, cognitive content, it is in the head and accessible to reasoning mechanisms. This paper discusses the phenomenal theory of cognitive content, according to which cognitive contents are the contents of phenomenal consciousness. I begin by distinguishing cognitive content from the closely associated notion of narrow content. I then argue, drawing on prior work, that the phenomenal theory can plausibly account for the cognitive contents of many relatively simple mental states. My main focus (...) in this paper is the question whether the phenomenal theory can account for the apparently abstract and complex cognitive contents of "high-level" thoughts. It might seem dubious that the theory can account for such cognitive contents, because cognitive phenomenology is too scarce and too thin. However, I argue there are in fact few abstract or complex cognitive contents, so the phenomenal theory's predictions are correct. This position explains some apparently irrational behavior that is otherwise hard to explain, and it makes sense of the central role of inner speech and other forms of consciousness in reasoning. (shrink)

Characterization of a form of explanation involving grounding on the model of mechanistic causal explanation.

The biological sciences have always proven a fertile ground for philosophical analysis, one from which has grown a rich tradition stemming from Aristotle and flowering with Darwin. And although contemporary philosophy is increasingly becoming conceptually entwined with the study of the empirical sciences with the data of the latter now being regularly utilised in the establishment and defence of the frameworks of the former, a practice especially prominent in the philosophy of physics, the development of that tradition hasn’t received the (...) wider attention it so thoroughly deserves. This review will briefly introduce some recent significant topics of debate within the philosophy of biology, focusing on those whose metaphysical themes (in everything from composition to causation) are likely to be of wide-reaching, cross-disciplinary interest. (shrink)

Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, and an inductive (...) rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. We here develop such guidelines by first giving a philosophical analysis of the underpinnings of Hill’s viewpoints on causality. We then put forward an evidence-amalgamation framework adopting a Bayesian net approach to model causal inference in pharmacology for the assessment of harms. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance, and reliability. (shrink)

According to Antoine Augustine Cournot, chance events are the result of the intersection between independent causal chains. This coincidental notion of chance is not a new one, but—as Cournot remarks—it comes from Saint Thomas Aquinas, Boethius, and more probably from Jean de La Placette. Such a conception of chance phenomena seems to be very important, not only because it is closely related to the Principle of Causality, but also since it grounds Cournot’s theory of objective probability. Starting from Martin’s work, (...) the main attempt of this survey is to endorse the idea that Cournot’s coincidental notion of hasard is objective, that is it is ontic and it does not depend—in some sense—on our degree of knowledge. In order to do that, a central role in the discussion will be given to the meaning of the independence between the intersecting causal chains and to Cournot’s conception of causation. (shrink)

Batterman and Rice ([2014]) argue that minimal models possess explanatory power that cannot be captured by what they call ‘common features’ approaches to explanation. Minimal models are explanatory, according to Batterman and Rice, not in virtue of accurately representing relevant features, but in virtue of answering three questions that provide a ‘story about why large classes of features are irrelevant to the explanandum phenomenon’ ([2014], p. 356). In this article, I argue, first, that a method (the renormalization group) they propose (...) to answer the three questions cannot answer them, at least not by itself. Second, I argue that answers to the three questions are unnecessary to account for the explanatoriness of their minimal models. Finally, I argue that a common features account, what I call the ‘generalized ontic conception of explanation’, can capture the explanatoriness of minimal models. (shrink)

Why should a citizen vote? There are two ways to interpret this question: in a prudential sense, and in a moral sense. Under the first interpretation, the question asks why—or under what circumstances—it is in a citizen's self-interest to vote. Under the second interpretation, it asks what moral reasons citizens have for voting. I shall mainly try to answer the moral version of the question, but my answer may also, in some circumstances, bear on the prudential question. Before proceeding to (...) my own approach, let me briefly survey alternatives in the field. (shrink)

There is a systematic and suggestive analogy between grounding and causation. In my view, this analogy is no coincidence. Grounding and causation are alike because grounding is a type of causation: metaphysical causation. In this paper I defend the identification of grounding with metaphysical causation, drawing on the causation literature to explore systematic connections between grounding and metaphysical dependence counterfactuals, and I outline a non-reductive counterfactual theory of grounding along interventionist lines.

In recent years, metaphysics has undergone what some describe as a revolution: it has become standard to understand a vast array of questions as questions about grounding, a metaphysical notion of determination. Why should we believe in grounding, though? Supporters of the revolution often gesture at what I call the Argument from Explanatoriness: the notion of grounding is somehow indispensable to a metaphysical type of explanation. I challenge this argument and along the way develop a “reactionary” view, according to which (...) there is no interesting sense in which the notion of grounding is explanatorily indispensable. I begin with a distinction between two conceptions of grounding, a distinction which extant critiques of the revolution have usually failed to take into consideration: grounding qua that which underlies metaphysical explanation and grounding qua metaphysical explanation itself. Accordingly, I distinguish between two versions of the Argument from Explanatoriness: the Unexplained Explanations Version for the first conception of grounding, and the Expressive Power Version for the second. The paper’s conclusion is that no version of the Argument from Explanatoriness is successful. (shrink)

The paper provides a systematic overview of recent debates in epistemology and philosophy of science on the nature of understanding. We explain why philosophers have turned their attention to understanding and discuss conditions for “explanatory” understanding of why something is the case and for “objectual” understanding of a whole subject matter. The most debated conditions for these types of understanding roughly resemble the three traditional conditions for knowledge: truth, justification and belief. We discuss prominent views about how to construe these (...) conditions for understanding, whether understanding indeed requires conditions of all three types and whether additional conditions are needed. (shrink)

This paper argues for several related theses. First, the epistemological position that knowledge requires safe belief can be motivated by views in the philosophy of science, according to which good explanations show that their explananda are robust. This motivation goes via the idea—recently defended on both conceptual and empirical grounds—that knowledge attributions play a crucial role in explaining successful action. Second, motivating the safety requirement in this way creates a choice point—depending on how we understand robustness, we'll end up with (...) different conceptions of safety in epistemology. Lastly, and most controversially, there's an attractive choice at this point that will not vindicate some of the most influential applications of the safety-theoretic framework in epistemology, e.g., Williamson's arguments against the KK principle, and luminosity. (shrink)

We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...) construct new causal maps and that their learning is consistent with the Bayes net formalism. (shrink)

Abductive reasoning assigns special status to the explanatory power of a hypothesis. But how do people make explanatory judgments? Our study clarifies this issue by asking: How does the explanatory power of a hypothesis cohere with other cognitive factors? How does probabilistic information affect explanatory judgments? In order to answer these questions, we conducted an experiment with 671 participants. Their task was to make judgments about a potentially explanatory hypothesis and its cognitive virtues. In the responses, we isolated three constructs: (...) Explanatory Value, Rational Acceptability, and Entailment. Explanatory judgments strongly cohered with judgments of causal relevance and with a sense of understanding. Furthermore, we found that Explanatory Value was sensitive to manipulations of statistical relevance relations between hypothesis and evidence, but not to explicit information about the prior probability of the hypothesis. These results indicate that probabilistic information about statistical relevance is a strong determinant of Explanatory Value. More generally, our study suggests that abductive and probabilistic reasoning are two distinct modes of inference. (shrink)

There are many putative counterexamples to the view that all scientific explanations are causal explanations. Using a new theory of what it is to be a causal explanation, Bradford Skow has recently argued that several of the putative counterexamples fail to be non-causal. This paper defends some of the counterexamples by showing how Skow’s argument relies on an overly permissive theory of causal explanations.

The debate between the dynamical and the statistical interpretations of natural selection is centred on the question of whether all explanations that employ the concepts of natural selection and drift are reducible to causal explanations. The proponents of the statistical interpretation answer negatively, but insist on the fact that selection/drift arguments are explanatory. However, they remain unclear on where the explanatory power comes from. The proponents of the dynamical interpretation answer positively and try to reduce selection/drift arguments to some of (...) the most prominent accounts of causal explanation. In turn, they face the criticism raised by statisticalists that current accounts of causation have to be violated in some of their core conditions or otherwise used in a very loose manner in order to account for selection/drift explanations. We propose a reconciliation of both interpretations by conveying evolutionary explanations within the unificationist model of scientific explanation. Therefore, we argue that the explanatory power in natural selection arguments is a result of successful unification of individual- and population-level facts. A short case study based on research on sympatric speciation will be presented as an example of how population- and individual-level facts are unified to explain the morphological mosaic of bill shape in island scrub jays. (shrink)

In this paper, I consider the relationship between Inference to the Best Explanation and Bayesianism, both of which are well-known accounts of the nature of scientific inference. In Sect. 2, I give a brief overview of Bayesianism and IBE. In Sect. 3, I argue that IBE in its most prominently defended forms is difficult to reconcile with Bayesianism because not all of the items that feature on popular lists of “explanatory virtues”—by means of which IBE ranks competing explanations—have confirmational import. (...) Rather, some of the items that feature on these lists are “informational virtues”—properties that do not make a hypothesis \ more probable than some competitor \ given evidence E, but that, roughly-speaking, give that hypothesis greater informative content. In Sect. 4, I consider as a response to my argument a recent version of compatibilism which argues that IBE can provide further normative constraints on the objectively correct probability function. I argue that this response does not succeed, owing to the difficulty of defending with any generality such further normative constraints. Lastly, in Sect. 5, I propose that IBE should be regarded, not as a theory of scientific inference, but rather as a theory of when we ought to “accept” H, where the acceptability of H is fixed by the goals of science and concerns whether H is worthy of commitment as research program. In this way, IBE and Bayesianism, as I will show, can be made compatible, and thus the Bayesian and the proponent of IBE can be friends. (shrink)

Popper’s introduction of ‘‘propensity’’ was intended to provide a solid conceptual foundation for objective single-case probabilities. By considering the partly opposed contributions of Humphreys and Miller and Salmon, it is argued that when properly understood, propensities can in fact be understood as objective single-case causal probabilities of transitions between concrete events. The chief claim is that propensities are well-explicated by describing how they fit into the existing formal theory of branching space-times, which is simultaneously indeterministic and causal. Several problematic examples, (...) some commonsense and some quantum-mechanical, are used to make clear the advantages of invoking branching space-times theory in coming to understand propensities. (shrink)

I offer a novel defence of radically externalist theories of perception, via a strikingly spare and broadly physicalist metaphysics. The core, motivating claim is what I call a natural view of perception, according to which perception involves direct awareness of our environment, such that the phenomenology of experience consists of the worldly things perceived, as they appear to the perspective of the subject. To underpin this natural view, I propose a simple metaphysical picture of perception, which identifies the perceptual experience (...) with the relation of awareness holding between subject and object, a relation that can be described in familiar physical terms as a causal process involving the thing perceived and the perceiver. Distinctively, the simple metaphysical picture has no place for the notion of ‘experiences’ understood as distinctively ‘mental’ states or events internal or otherwise belonging to the subject. Although there is some limited precedent for the simple metaphysical picture of perception, I offer the first detailed argument for its role in underpinning the natural view. The thesis offers new and detailed arguments to show that the simple metaphysical picture can not only account for normal perceptual experiences, but can also accommodate and explain other forms of sensory experience that have widely been considered to undermine the natural view of perception. These ‘problem’ cases include perceptual illusion, hallucination, and the role of memory and beliefs in influencing how things appear perceptually. In all of these cases, the simple metaphysical picture accounts for the phenomenology of the experience purely in terms of awareness of worldly objects, albeit in some cases objects that are not currently present in the subject’s environment. The simple metaphysical picture thus promises to explain not just perceptual experience but phenomenal consciousness more generally. The natural view is explicitly a commitment of some varieties of naïve realism, but I argue that the two theses come apart. For one thing, the simple metaphysical picture offers a solution to hallucination and other ‘problem’ cases quite different to the solutions offered by naïve realists. However, the most striking and novel claim advanced here is that the natural view can be defended without a commitment to realism. In this regard, I cite evidence for the subject-relativity or experience-dependence of certain perceived qualities, notably colour, and show the simple metaphysical picture allows us to square this with the natural view that colours are ‘out there’ in the environment. I discuss the metaphysical implications of rejecting realism while adhering to the simple metaphysical picture, and outline a radical – and radically simple – metaphysics of the world in general that might preserve the natural view and accommodate the simple metaphysical picture of phenomenal consciousness more generally. This metaphysics takes the form of a process monism in which the governing metaphysical structuring principle is one of top-down determination, such that whole processes determine the nature of their constituent parts. (shrink)

Convincing disputes about explanatory reductionism in the philosophy of biology require a clear and precise understanding of what a reductive explanation in biology is. The central aim of this book is to provide such an account by revealing the features that determine the reductive character of a biological explanation. Chapters I-IV provide the ground, on which I can then, in Chapter V, develop my own account of explanatory reduction in biology: Chapter I reveals the meta-philosophical assumptions that underlie my analysis (...) of reduction, Chapter II introduces the previous debate about reduction in the philosophy of biology by pointing out the most crucial lessons one should learn from this debate, Chapter III critically discusses the two perspectives on explanatory reduction that have been proposed in the philosophy of biology so far, and Chapter IV clarifies in what ways the issue of reduction is entangled with the issue of explanation. In Chapter V I present my own account of explanatory reduction. My main claim is that reductive explanations in the biological sciences possess three main characteristics: they explain the behavior of a biological system S, first, exclusively in terms of factors that are located on a lower level of organization than S, second, by focusing on factors that are internal to S , and third, by describing the genuine parts of S only as “parts in isolation”. This account is ontic because it traces the reductivity of an explanation back to certain relations that exist in the world. (shrink)