Many philosophers now regard causal approaches to explanation as highly promising, even in physics. This is due in large part to James Woodward's influential argument that a wide variety of scientific explanations are causal, based on his interventionist approach to causation. This article argues that some derivations describing causal relations and satisfying Woodward's criteria for causal explanation fail to be explanatory. Further, causal relations are unnecessary for a range of explanations, widespread in physics, involving highly idealized models. These constitute significant (...) limitations on the scope of causal explanation. We have good reason to doubt that causal explanation is as widespread or important in physics as Woodward and other proponents maintain. (shrink)

Here I consider the relative merits of two recent models of explanation, James Woodward's interventionist-counterfactual model and the model model. According to the former, explanations are largely constituted by information about the consequences of counterfactual interventions. Problems arise for this approach because countless relevant interventions are possible in most cases and because it overlooks other kinds of equally relevant information. According the model model, explanations are largely constituted by cognitive models of actual mechanisms. On this approach, explanations tend not to (...) represent any of the aforementioned information explicitly but can instead be used to produce it on demand. The model model thus offers the more plausible account of the information of which we are aware when we have an explanation and of the ratiocinative process through which we derive many kinds of information that are relevant to the evaluation of explanations. (shrink)

Recent years have seen renewed interest in the emergence issue. The contemporary debate, in contrast with that of past times, has to do not so much with the mind–body problem as with the relationship between the physical and other domains; mostly with the biological domain. One of the main sources of this renewed interest is the study of complex and, in general, far-from-equilibrium self-preserving systems, which seem to fulfil one of the necessary conditions for an entity to be emergent; namely, (...) that its causal powers are not predictable from the causal powers of basic physical properties. However, I argue that much of the current emergentism debate has misfired by focusing on the interpretation of self-maintaining systems. In contrast, I claim that if we want to find emergent properties, we should look not at complex systems, but at selection (natural selection, in particular). I argue that selection processes make the causal world ‘exuberant’ by making non-physical functional and relational properties enter the causal web of the world. (shrink)

According to an increasing number of authors, the best, if not the only, argument in favour of physicalism is the so-called 'overdetermination argument'. This argument, if sound, establishes that all the entities that enter into causal interactions with the physical world are physical. One key premise in the overdetermination argument is the principle of the causal closure of the physical world, said to be supported by contemporary physics. In this paper, I examine various ways in which physics may support the (...) principle, either as a methodological guide or as depending on some other laws and principles of physics. (shrink)

How can we use models to understand real phenomena if models misrepresent the very phenomena we seek to understand? Some accounts suggest that models may afford understanding by providing causal knowledge about phenomena via how-possibly explanations. However, general equilibrium models, for example, pose a challenge to this solution since their contribution appears to be purely mathematical results. Despite this, practitioners widely acknowledge that it improves our understanding of the world. I argue that the Arrow–Debreu model provides a mathematical how-possibly explanation (...) which establishes claims of mathematical dependence. The account developed reveals how mathematical knowledge can inform claims about the world, allow ‘what-if-things-had-been-different’ inferences, and thus improve our understanding. (shrink)

The story of how Perrin’s experimental work established the reality of atoms and molecules has been a staple in (realist) philosophy of science writings (Wesley Salmon, Clark Glymour, Peter Achinstein, Penelope Maddy, …). I’ll argue that how this story is told distorts both what the work was and its significance, and draw morals for the understanding of how theories can be or fail to be empirically grounded.

In this paper I show that a novel ontic reading of explanation, intending to capture the de re essential features of individuals, can support the qualitative view of individual essences. It is argued further that the putative harmful consequences of the Leibniz Principle and its converse for the qualitative view can be avoided, provided that individual essences are not construed in the style of the naïve bundle theory with set-theoretical identity- conditions. Adopting either the more sophisticated two-tier BT or, alternatively, (...) the neo-Aristotelian position of taking essences as natures in the Aristotelian sense, can help to evade these main charges against the qualitative view. The functional parallels with the alternative haecceitistic view of individuation and individual essence will also be considered. (shrink)

I provide a brief history of pragmatics as it relates to explanation, highlighting the great neglect of pragmatics and pragmatic considerations in regard to explanation during the mid-twentieth century. In order to understand pragmatic considerations regarding explanation, I utilize the work of Bas C. van Fraassen, Peter Achinstein, and Jan Faye. These thinkers provide crucial tools for understanding pragmatics, especially with regard to concepts such as context and exigence. The work of these thinkers provides the platform from which I compose (...) a definition of pragmatic considerations. (shrink)

John Dewey provided a robust and thorough conception of scientific explanation within his philosophical writing. I provide an exegesis of Dewey's concept of scientific explanation and argue that this concept is important to contemporary philosophy of science for at least two reasons. First, Dewey's conception of scientific explanation avoids the reification of science as an entity separated from practical experience. Second, Dewey supplants the realist-antirealist debate within the philosophical literature concerning explanation, thus moving us beyond the current stalemate within philosophy (...) of science. (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)

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

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)

In 1983, Dr. J. Robin Warren and Dr. Barry Marshall reported finding a new kind of bacteria in the stomachs of people with gastritis. Warren and Marshall were soon led to the hypothesis that peptic ulcers are generally caused, not by excess acidity or stress, but by a bacterial infection. Initially, this hypothesis was viewed as preposterous, and it is still somewhat controversial. In 1994, however, a U. S. National Institutes of Health Consensus Development Panel concluded that infection appears to (...) play an important contributory role in the pathogenesis of peptic ulcers, and recommended that antibiotics be used in their treatment. Peptic ulcers are common, affecting up to 10% of the population, and evidence has mounted that many ulcers can be cured by eradicating the bacteria responsible for them. (shrink)

This target article presents a new computational theory of explanatory coherence that applies to the acceptance and rejection of scientific hypotheses as well as to reasoning in everyday life, The theory consists of seven principles that establish relations of local coherence between a hypothesis and other propositions. A hypothesis coheres with propositions that it explains, or that explain it, or that participate with it in explaining other propositions, or that offer analogous explanations. Propositions are incoherent with each other if they (...) are contradictory, Propositions that describe the results of observation have a degree of acceptability on their own. An explanatory hypothesis is acccpted if it coheres better overall than its competitors. The power of the seven principles is shown by their implementation in a connectionist program called ECHO, which treats hypothesis evaluation as a constraint satisfaction problem. Inputs about the explanatory relations are used to create a network of units representing propositions, while coherende and incoherence relations are encoded by excitatory and inbihitory links. ECHO provides an algorithm for smoothly integrating theory evaluation based on considerations of explanatory breadth, simplicity, and analogy. It has been applied to such important scientific cases as Lovoisier's argument for oxygen against the phlogiston theory and Darwin's argument for evolution against creationism, and also to cases of legal reasoning. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy. (shrink)

The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at a high (...) level can be reduced to causal relationships among simpler entities at a more detailed level. We argue that a similar hierarchy makes possible an equally productive approach toward cognitive modeling. The levels of models that we conceive in relation to cognition include, at the highest level, sociological/anthropological models of collective human behavior, behavioral models of individual performance, cognitive models involving detailed mechanisms, representations, and processes, as well as biological/physiological models of neural circuits, brain regions, and other detailed biological processes. (shrink)

In the physical sciences a rigorous theory is a hierarchy of descriptions in which causal relationships between many general types of entity at a phenomenological level can be derived from causal relationships between smaller numbers of simpler entities at more detailed levels. The hierarchy of descriptions resembles the modular hierarchy created in electronic systems in order to be able to modify a complex functionality without excessive side effects. Such a hierarchy would make it possible to establish a rigorous scientific theory (...) of consciousness. The causal relationships implicit in definitions of access consciousness and phe- nomenal consciousness are made explicit, and the corresponding causal relationships at the more detailed levels of perception, memory, and skill learning described. Extension of these causal relationships to physiological and neural levels is discussed. The general capability of a range of current consciousness models to support a modular hierarchy which could generate these causal relationships is reviewed, and the specific capabilities of two models with good general capabilities are compared in some detail. Ó 2003 Elsevier Inc. All rights reserved. (shrink)

Several authors have claimed that mechanisms play a vital role in distinguishing between causation and mere correlation in the social sciences. Such claims are sometimes interpreted to mean that without mechanisms, causal inference in social science is impossible. The author agrees with critics of this proposition but explains how the account of how mechanisms aid causal inference can be interpreted in a way that does not depend on it. Nevertheless, he shows that this more charitable version of the account is (...) still unsuccessful as it stands. Consequently, he advances a proposal for shoring up the account, which is founded on the possibility of acquiring knowledge of social mechanisms by linking together norms or practices found in a society. Key Words: causality • social mechanisms • interpretation • anthropology. (shrink)

This article examines methodological individualism in terms of the theory that invariance under intervention is the signal feature of generalizations that serve as a basis for causal explanation. This theory supports the holist contention that macro-level generalizations can explain, but it also suggests a defense of methodological individualism on the grounds that greater range of invariance under intervention entails deeper explanation. Although this individualist position is not threatened by multiple-realizability, an argument for it based on rational choice theory is called (...) into question by experimental results concerning preference reversals. Key Words: methodological individualism • mechanisms • explanation • invariance • preference reversal. (shrink)

Reductionism is often understood to include two theses: (1) every singular occurrence that the special sciences can explain also can be explained by physics; (2) every law in a higher-level science can be explained by physics. These claims are widely supposed to have been refuted by the multiple realizability argument, formulated by Putnam (1967, 1975) and Fodor (1968, 1975). The present paper criticizes the argument and identifies a reductionistic thesis that follows from one of the argument's premises.

Richard Boyd and Nicholas Sturgeon develop distinctive naturalistic arguments for scientific realism and moral realism. Each defends a realist position by an inference to the best explanation. In this paper, I suggest that these arguments for realism should be reformulated, with the law of likelihood replacing inference to the best explanation. The resulting arguments for realism do not work.

When philosophers discuss the topic of explanation, they usually have in mind the following question: given the beliefs one has and some proposition that one wishes to explain, which subset of the beliefs constitutes an explanation of the target proposition? That is, the philosophical ‘problem of explanation’ typically has bracketed the issue of how one obtains the beliefs; they are taken as given. The problem of explanation has been the problem of understanding the relation ‘x explains y’. Since Hempel did (...) so much to canonize this way of thinking about explanation, it deserves to be called ‘Hempel's problem’. (shrink)

Darwin'suse of final cause accords with the Aristotelian idea of finalcauses as explanatory types – as opposed to mechanical causes, which arealways particulars. In Wright's consequence etiology, anadaptation is explained by particular events, namely, its past consequences;hence, that etiology is mechanistic at bottom. This justifies Ghiselin'scharge that such versions of teleology trivialize the subject, But a purelymechanistic explanation of an adaptation allows it to appear coincidental.Patterns of outcome, whether biological or thermodynamic, cannot be explainedbytracing causal chains, even were that possible. (...) They are explicanda of aspecialkind. The form of their explanation, in statistical mechanics or by naturalselection, is not captured by statistical variants of the covering-law model orrelated models of explanation. In them as in classical teleology, types ofoutcome are cited to explain why there are outcomes of those types. But onlywhen types are explanatory by being selected for, as inexplanations of animal and human behavior as well as in Darwin's theory ofnatural selection, but not in statistical mechanics, is the explanationteleological. Darwin's theory is nontrivially teleological. (shrink)

Various claims regarding intertheoretic reduction, weak and strong notions of emergence, and explanatory fictions have been made in the context of first-order thermodynamic phase transitions. By appealing to John Norton’s recent distinction between approximation and idealization, I argue that the case study of anyons and fractional statistics, which has received little attention in the philosophy of science literature, is more hospitable to such claims. In doing so, I also identify three novel roles that explanatory fictions fulfill in science. Furthermore, I (...) scrutinize the claim that anyons, as they are ostensibly manifested in the fractional quantum Hall effect, are emergent entities and urge caution. Consequently, it is suggested that a particular notion of strong emergence signals the need for the development of novel physical–mathematical research programs. (shrink)

It has been recently debated whether there exists a so-called “easy road” to nominalism. In this essay, I attempt to fill a lacuna in the debate by making a connection with the literature on infinite and infinitesimal idealization in science through an example from mathematical physics that has been largely ignored by philosophers. Specifically, by appealing to John Norton’s distinction between idealization and approximation, I argue that the phenomena of fractional quantum statistics bears negatively on Mary Leng’s proposed path to (...) easy road nominalism, thereby partially defending Mark Colyvan’s claim that there is no easy road to nominalism. (shrink)

This article examines a metaphilosophical issue, namely existing disagreements in philosophy of science about the significance of using multiple means of determination in scientific practice. We argue that this disagreement can, in part, be resolved by separating different questions that can be asked about the use of multiple means of determination, including the following: what can be concluded from the convergence of data or the convergence of claims about phenomena? Are the conclusions drawn from the convergence of data and of (...) statements about phenomena of special importance to the debate about realism and antirealism? Do inferences based on multiple means of determination have stronger epistemic force than inferences that are secured in other ways? Is the epistemic goal of deploying multiple means of determination well entrenched within the scientific community? Most of these questions can be discussed both in a formal and in an empirical perspective. If the differences in perspective are taken into account, some disagreements can be easily resolved. In part, however, the disagreements reflect historiographical challenges that are very difficult, if not impossible to meet. (shrink)

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

This article suggests a revision of the theory of causal nets. In section 1 we introduce an axiomatization of TCN based on a realistic understanding. It is shown that the causal Markov condition entails three independent principles. In section 2 we analyze indeterministic decay as the major counterexample to one of these principles: screening off by common causes. We call SCC-violating common causes interactive causes. In section 3 we develop a revised version of TCN, called TCN*, which accounts for interactive (...) causes. It is shown that there are interactive causal models that admit of no faithful noninteractive reconstruction. (shrink)

One of the most discussed issues in psychology—presently and in the past—is how to define and measure the extent to which human cognition is rational. The rationality of human cognition is often evaluated in terms of normative standards based on a priori intuitions. Yet this approach has been challenged by two recent developments in psychology that we review in this article: ecological rationality and descriptivism. Going beyond these contributions, we consider it a good moment for psychologists and philosophers to join (...) forces and work toward a new foundation for the definition of rational cognition. We take a first step in this direction by proposing that the rationality of both cognitive and normative systems can be measured in terms of their cognitive success. Cognitive success can be defined and gauged in terms of two factors: ecological validity (the system's validity in conditions in which it is applicable) and the system's applicability (the scope of conditions under which it can be applied). As we show, prominent systems of reasoning—deductive reasoning, Bayesian reasoning, uncertain conditionals, and prediction and choice—perform rather differently on these two factors. Furthermore, we demonstrate that conceptualizing rationality according to its cognitive success offers a new perspective on the time-honored relationship between the descriptive (“is”) and the normative (“ought”) in psychology and philosophy. (shrink)

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

In this paper I assess the adequacy of no-conspiracy conditions employed in the usual derivations of the Bell inequality in the context of EPR correlations. First, I look at the EPR correlations from a purely phenomenological point of view and claim that common cause explanations of these cannot be ruled out. I argue that an appropriate common cause explanation requires that no-conspiracy conditions are re-interpreted as mere common cause-measurement independence conditions. In the right circumstances then, violations of measurement independence need (...) not entail any kind of conspiracy (nor backwards in time causation). To the contrary, if measurement operations in the EPR context are taken to be causally relevant in a specific way to the experiment outcomes, their explicit causal role provides the grounds for a common cause explanation of the corresponding correlations. (shrink)

The paper addresses the question of how different types of evidence ought to inform public health policy. By analysing case studies on obesity, the paper draws lessons about the different roles that different types of evidence play in setting up public health policies. More specifically, it is argued that evidence of difference-making supports considerations about ‘what works for whom in what circumstances’, and that evidence of mechanisms provides information about the ‘causal pathways’ to intervene upon.

In social science, one objection to causal analysis is that the assumption of the closure of the system makes the analysis too narrow in scope, that is, it considers only 'closed' and 'hermetic' systems thus neglecting many other external influences. On the contrary, system analysis deals with complex structures where every element is interrelated with everything else in the system. The question arises as to whether the two approaches can be compatible and whether causal analysis can be integrated into the (...) broader framework of system analysis. This article attempts a negative answer on the grounds of fundamental differences in their assumptions and suggests using system analysis as a post-hoc comparative tool. (shrink)

A wave of recent work in metaphysics seeks to undermine the anti-reductionist, functionalist consensus of the past few decades in cognitive science and philosophy of mind. That consensus apparently legitimated a focus on what systems do, without necessarily and always requiring attention to the details of how systems are constituted. The new metaphysical challenge contends that many states and processes referred to by functionalist cognitive scientists are epiphenomenal. It further contends that the problem lies in functionalism itself, and that, to (...) save the causal significance of mind, it is necessary to re-embrace reductionism. We argue that the prescribed return to reductionism would be disastrous for the cognitive and behavioral sciences, requiring the dismantling of most existing achievements and placing intolerable restrictions on further work. However, this argument fails to answer the metaphysical challenge on its own terms. We meet that challenge by going on to argue that the new metaphysical skepticism about functionalist cognitive science depends on reifying two distinct notions of causality (one primarily scientific, the other metaphysical), then equivocating between them. When the different notions of causality are properly distinguished, it is clear that functionalism is in no serious philosophical trouble, and that we need not choose between reducing minds or finding them causally impotent. The metaphysical challenge to functionalism relies, in particular, on a naïve and inaccurate conception of the practice of physics, and the relationship between physics and metaphysics. Key Words: explanation; functionalism; mental causation; metaphysics; reductionism. (shrink)

The aim of this paper is to elaborate a notion of explanation which is applicable to stochastic processes such as quantum processes. The model-theoretic approach was adopted in order to delimit appropriate classes, by defining set-theoretical predicates, of different kinds of physical transformations that quantum systems suffer, either of transitions or of transmutations, by interaction or in a spontaneous manner. To explain a singular quantum process consists in showing that it is feasible to model it as an indeterministic process of (...) certain specified kind. (shrink)

Mechanistic accounts of explanation have recently found popularity within philosophy of science. Presently, we introduce the idea of an extended mechanistic explanation, which makes explicit room for the role of environment in explanation. After delineating Craver and Bechtel’s account, we argue this suggestion is not sufficiently robust when we take seriously the mechanistic environment and modeling practices involved in studying contemporary complex biological systems. Our goal is to extend the already profitable mechanistic picture by pointing out the importance of the (...) mechanistic environment. It is our belief that extended mechanistic explanations, or mechanisms that take into consideration the temporal sequencing of the interplay between the mechanism and the environment, allow for mechanistic explanations regarding a broader group of scientific phenomena. (shrink)

ABSTRACTMeasurements of environmental toxicity from long-term exposure to oil contamination have delivered inaccurate and contradictory results regarding the potential harms for humans and ecosyste...

In Extragalactic Reality: The Case of Gravitational Lensing Hacking resumes the discussion of scientific realism from the last chapter of Representing and Intervening. Since the criterion of manipulability cannot be applied to astronomical objects, experimental entity realism seems to be restricted to terrestrial entities. In fact, Hacking explicitly argues against astronomical realism. The case at issue is the existence of gravitational lenses. In this paper, I question Hacking 's chief witness for astronomical antirealism: the gravitational lens system “0957+ 561”. It (...) will be shown that Hacking 's argumentation is misleading. Discussing astronomical realism as theory realism, Hacking focuses on the question of how to infer the existence of gravitational lenses from the truth of gravitational lens theory. But neither the reconstruction of gravitational lensing in terms of inference to the best explanation nor the argument of underdetermination are tenable under closer inspection. My thesis is that a realist account of gravitational lensing can be given by relying on observation, causal capacities and home truths. (shrink)

In this paper, we argue that rather than exclusively focusing on trying to determine if an idealized model fits a particular account of scientific explanation, philosophers of science should also work on directly analyzing various explanatory schemas that reveal the steps and justification involved in scientists’ use of highly idealized models to formulate explanations. We develop our alternative methodology by analyzing historically important cases of idealized statistical modeling that use a three-step explanatory schema involving idealization, mathematical operation, and explanatory interpretation.

In this paper, I aim to provide access to the current debate on non-causal explanations in philosophy of science. I will first present examples of non-causal explanations in the sciences. Then, I will outline three alternative approaches to non-causal explanations – that is, causal reductionism, pluralism, and monism – and, corresponding to these three approaches, different strategies for distinguishing between causal and non-causal explanation. Finally, I will raise questions for future research on non-causal explanations.

The patent system appears to make three ontological assumptions often associated with scientific realism: there is a natural world that is independent of human knowledge and technology; invented products can be unobservable things; and invented products have causal powers. Although a straightforward reading of patent laws implies these ontological commitments, it is not at all clear that what the patent system has to say about the world has any bearing on issues of scientific realism. While realists might embrace the patent (...) system’s apparent ontology as providing additional support for their view, anti-realists could propose an alternative interpretation of patent law that does not involve realist ontological commitments. Thus, questions about the patent system’s ontology depend on whether one takes patent laws at face value or defends an interpretation of patent law motivated by philosophical theory. Philosophers of science should pay attention to the practice of patenting, even if they ultimately reject the patent system’s apparent ontology and offer an alternative interpretation. (shrink)