To a large extent, the evidential base of claims in the philosophy of science has switched from thought experiments to case studies. We argue that abandoning thought experiments was a wrong turn, since they can effectively complement case studies. We make our argument via an analogy with the relationship between experiments and observations within science. Just as experiments and ‘natural’ observations can together evidence claims in science, each mitigating the downsides of the other, so too can thought experiments and case (...) studies be mutually supporting. After presenting the main argument, we look at potential concerns about thought experiments, suggesting that a judiciously applied mixed-methods approach can overcome them. (shrink)

Any successful account of the metaphysics of mechanistic causation must satisfy at least five key desiderata. In this article, I lay out these five desiderata and explain why existing accounts of the metaphysics of mechanistic causation fail to satisfy them. I then present an alternative account that does satisfy the five desiderata. According to this alternative account, we must resort to a type of ontological entity that is new to metaphysics, but not to science: constraints. In this article, I explain (...) how a constraints-based metaphysics fits best with the emerging consensus on the nature of mechanistic explanation. 1Introduction2Renormalizability2.1The first two desiderata: Intrinsicness and productivity2.2The third desideratum: Scientific validity or non-mysteriousness2.3The fourth desideratum: Directionality2.4The fifth desideratum: Perspectival nature of mechanisms3Constraints and Causation3.1Multi-perspectival realism and causal structure3.2Causal structure as laws3.3Causal structures in analytical mechanics: Constraints3.4A metaphysics inspired by analytical mechanics: Constraints as ontologically primitive modal structures4Constraints and Mechanistic Causal Powers 4.1Inter- versus intra-perspectival categories4.2Mechanistic causal powers are grounded by constraints4.3Intrinsicness and constraints4.4Constraints and productiveness4.5Constraints and directionality5Conclusion. (shrink)

Are causal explanations (e.g., “she switched careers because of the COVID pandemic”) treated differently from the corresponding claims that one factor caused another (e.g., “the COVID pandemic caused her to switch careers”)? We examined whether explanatory and causal claims diverge in their responsiveness to two different types of information: covariation strength and mechanism information. We report five experiments with 1,730 participants total, showing that compared to judgments of causal strength, explanatory judgments tend to bemoresensitive to mechanism andlesssensitive to covariation – (...) even though explanatory judgments respond to both types of information. We also report exploratory comparisons to judgments of understanding, and discuss implications of our findings for theories of explanation, understanding, and causal attribution. These findings shed light on the potentially unique role of explanation in cognition. (shrink)

The ontic conception of scientific explanation has been constructed and motivated on the basis of a putative lexical ambiguity in the term explanation. I raise a puzzle for this ambiguity claim, and then give a deflationary solution under which all ontically-rendered talk of explanation is merely elliptical; what it is elliptical for is a view of scientific explanation that altogether avoids the ontic conception. This result has revisionary consequences for New Mechanists and other philosophers of science, many of whom have (...) assimilated their conception of explanation to the ontic conception. (shrink)

In this dissertation, I argue that understanding possesses unique epistemic value. I propose and defend a novel account of understanding that I call the management account of understanding, which is the view that an agent A understands a subject matter S just in case A has the ability to extract the relevant information and exploit it with the relevant cognitive capacities to answer questions in S. Since inquiry is the process of raising and answering questions, I argue that without understanding, (...) it would be impossible to engage in successful inquiry. I argue that understanding is indispensable for effective cognition and that it is irreducible to other epistemic categories, such as knowledge, justification, or rationality. Understanding is an irreducible component of epistemic excellence. In arguing for this account, I focus on the nature and requirements of cognition and pay less attention to intuitions about hypothetical cases, in contrast to previous approaches to understanding. I draw upon psychology and cognitive science, especially work on the frame problem, to demonstrate the importance (and difficulty) of finding relevant information and exploiting it in relevant ways to answer questions. One’s cognitive system must be organized in such a way that one has the ability to think the relevant thoughts in the relevant ways to answer the relevant questions. This reveals well-defined directions in which future research on understanding should move. In addition to proposing and defending the management account of understanding, I explore several features of understanding, including some interesting consequences of the management account. I argue that the management account and commonly used methods of measuring degrees of understanding imply that understanding can be based on false beliefs. I argue that the management account (and any account of understanding that implies understanding is an ability) implies that understanding can be acquired by luck. I also argue that understanding is ontologically prior to explanation: a good explanation is essentially an act that produces understanding in the relevant cognitive background. (shrink)

This chapter offers a critique of intelligent design arguments against evolution and a philosophical discussion of the nature of science, drawing several lessons for the teaching of evolution and for science education in general. I discuss why Behe’s irreducible complexity argument fails, and why his portrayal of organismal systems as machines is detrimental to biology education and any under-standing of how organismal evolution is possible. The idea that the evolution of complex organismal features is too unlikely to have occurred by (...) random mutation and selection (as recently promoted by Dembski) is very widespread, but it is easy to show students why such small probability arguments are fallacious. While intelligent design proponents have claimed that the exclusion of supernatural causes mandated by scientific methods is dogmatically presupposed by science, scientists have an empirical justification for using such methods. This justification is instructive for my discussion of how to demarcate science from pseudoscience. I argue that there is no universal account of the nature of science, but that the criteria used to judge an intellectual approach vary across historical periods and have to be specific to the scientific domain. Moreover, intellectual approaches have to be construed as practices based on institutional factors and values, and to be evaluated in terms of the activities of their practitioners. Science educators should not just teach scientific facts, but present science as a practice and make students reflect on the nature of science, as this gives them a better appreciation of the ways in which intelligent design falls short of actual science. (shrink)

In this paper we investigate how inference to the best explanation (IBE) works in engineering science, focussing on the context of malfunction explanation. While IBE has gotten a lot of attention in the philosophy of science literature, few, if any, philosophical work has focussed on IBE in engineering science practice. We first show that IBE in engineering science has a similar structure as IBE in other scientific domains in the sense that in both settings IBE hinges on the weighing of (...) explanatory virtues. We then proceed to show that, due to the intimate connection between explanation and redesign in engineering science, there is a further engineering domain-specific virtue in terms of which engineering malfunction explanations are evaluated, viz. the virtue of redesign utility. This virtue entails that the explanatory information offered by a malfunction explanation should be instrumental in predicting counterfactual dependencies in redesigned systems. We illustrate and elaborate these points in terms of a number of engineering examples, focussing in particular on the 2009 crash of Air France Flight 447. Our extension of analyses of IBE and explanation to engineering science practice offers new insights by identifying a new explanatory virtue in malfunction explanation: redesign utility. (shrink)

This chapter examines the status of inference to the best explanation in naturalistic metaphysics. The methodology of inference to the best explanation in metaphysics is studied from the perspective of contemporary views on scientific explanation and explanatory inferences in the history and philosophy of science. This reveals serious shortcomings in prevalent attempts to vindicate metaphysical "explanationism" by reference to similarities between science and naturalistic metaphysics. This critique is brought out by considering a common gambit of methodological unity: (1) Both metaphysics (...) and science employ inference to the best explanation. (2) One has no reason to think that if explanationism is truth-conducive in science, it is not so in metaphysics. (3) One has a positive reason to think that if explanationism is truth-conducive in science, it is also so in metaphysics. (shrink)

Reduction and reductionism have been central philosophical topics in analytic philosophy of science for more than six decades. Together they encompass a diversity of issues from metaphysics and epistemology. This article provides an introduction to the topic that illuminates how contemporary epistemological discussions took their shape historically and limns the contours of concrete cases of reduction in specific natural sciences. The unity of science and the impulse to accomplish compositional reduction in accord with a layer-cake vision of the sciences, the (...) seminal contributions of Ernest Nagel on theory reduction and how they strongly conditioned subsequent philosophical discussions, and the detailed issues pertaining to different accounts of reduction that arise in both physical and biological science (e.g., limit-case and part-whole reduction in physics, the difference-making principle in genetics, and mechanisms in molecular biology) are explored. The conclusion argues that the epistemological heterogeneity and patchwork organization of the natural sciences encourages a pluralist stance about reduction. (shrink)

In this chapter we examine the relation between mechanisms and laws/counterfactuals by revisiting the main notions of mechanism found in the literature. We distinguish between two different conceptions of ‘mechanism’: mechanisms-of underlie or constitute a causal process; mechanisms-for are complex systems that function so as to produce a certain behavior. According to some mechanists, a mechanism fulfills both of these roles simultaneously. The main argument of the chapter is that there is an asymmetrical dependence between both kinds of mechanisms and (...) laws/counterfactuals: while some laws and counterfactuals must be taken as primitive (non-mechanistic) facts of the world, all mechanisms depend on laws/counterfactuals. (shrink)

Mechanisms are said to consist of two kinds of components, entities and activities. In the first half of this chapter, I examine what entities and activities are, how they relate to well-known ontological categories, such as processes or dispositions, and how entities and activities relate to each other (e.g., can one be reduced to the other or are they mutually dependent?). The second part of this chapter analyzes different criteria for individuating the components of mechanisms and discusses how real the (...) boundaries of mechanisms are. (shrink)

Accounts of mechanistic explanation have emphasized the importance of looking down—decomposing a mechanism into its parts and operations. Using research on visual processing as an exemplar, I illustrate how productive such research has been. But once multiple components of a mechanism have been identified, researchers also need to figure out how it is organized—they must look around and determine how to recompose the mechanism. Although researchers often begin by trying to recompose the mechanism in terms of sequential operations, they frequently (...) find that the components of a mechanism interact in complex ways involving positive and negative feedback and that the organization often exhibits highly interactive local networks linked by a few long-range connections (small-worlds organization) and power law distributions of connections. The mechanisms are themselves active systems that are perturbed by inputs but not set in motion by them. Researchers also need to look up —situate a mechanism in its context, which may be a larger mechanism that modulates its behavior. When looking down is combined with looking around and up, mechanistic research results in an integrated, multi-level perspective. (shrink)

How should we reason in science? Jan Sprenger and Stephan Hartmann offer a refreshing take on classical topics in philosophy of science, using a single key concept to explain and to elucidate manifold aspects of scientific reasoning. They present good arguments and good inferences as being characterized by their effect on our rational degrees of belief. Refuting the view that there is no place for subjective attitudes in 'objective science', Sprenger and Hartmann explain the value of convincing evidence in terms (...) of a cycle of variations on the theme of representing rational degrees of belief by means of subjective probabilities (and changing them by Bayesian conditionalization). In doing so, they integrate Bayesian inference—the leading theory of rationality in social science—with the practice of 21st century science. Bayesian Philosophy of Science thereby shows how modeling such attitudes improves our understanding of causes, explanations, confirming evidence, and scientific models in general. It combines a scientifically minded and mathematically sophisticated approach with conceptual analysis and attention to methodological problems of modern science, especially in statistical inference, and is therefore a valuable resource for philosophers and scientific practitioners. (shrink)

The sensorimotor theory of vision and visual consciousness is often described as a radical alternative to the computational and connectionist orthodoxy in the study of visual perception. However, it is far from clear whether the theory represents a significant departure from orthodox approaches or whether it is an enrichment of it. In this study, I tackle this issue by focusing on the explanatory structure of the sensorimotor theory. I argue that the standard formulation of the theory subscribes to the same (...) theses of the dynamical hypothesis and that it affords covering-law explanations. This however exposes the theory to the mere description worry and generates a puzzle about the role of representations. I then argue that the sensorimotor theory is compatible with a mechanistic framework, and show how this can overcome the mere description worry and solve the problem of the explanatory role of representations. By doing so, it will be shown that the theory should be understood as an enrichment of the orthodoxy, rather than an alternative. (shrink)

We contend that diagrams are tools not only for communication but also for supporting the reasoning of biologists. In the mechanistic research that is characteristic of biology, diagrams delineate the phenomenon to be explained, display explanatory relations, and show the organized parts and operations of the mechanism proposed as responsible for the phenomenon. Both phenomenon diagrams and explanatory relations diagrams, employing graphs or other formats, facilitate applying visual processing to the detection of relevant patterns. Mechanism diagrams guide reasoning about how (...) the parts and operations work together to produce the phenomenon and what experiments need to be done to improve on the existing account. We examine how these functions are served by diagrams in circadian rhythm research. (shrink)

The received view of dynamical explanation is that dynamical cognitive science seeks to provide covering law explanations of cognitive phenomena. By analyzing three prominent examples of dynamicist research, I show that the received view is misleading: some dynamical explanations are mechanistic explanations, and in this way resemble computational and connectionist explanations. Interestingly, these dynamical explanations invoke the mathematical framework of dynamical systems theory to describe mechanisms far more complex and distributed than the ones typically considered by philosophers. Therefore, contemporary dynamicist (...) research reveals the need for a more sophisticated account of mechanistic explanation. (shrink)

This article compares causal and constitutive explanation. While scientific inquiry usually addresses both causal and constitutive questions, making the distinction is crucial for a detailed understanding of scientific questions and their interrelations. These explanations have different kinds of explananda and they track different sorts of dependencies. Constitutive explanations do not address events or behaviors, but causal capacities. While there are some interesting relations between building and causal manipulation, causation and constitution are not to be confused. Constitution is a synchronous and (...) asymmetric relation between relata that cannot be conceived as independent existences. However, despite their metaphysical differences, the same key ideas about explanation largely apply to both. Causal and constitutive explanations face similar challenges (such as the problems of relevance and explanatory regress) and both are in the business of mapping networks of counterfactual dependence—i.e. mechanisms—although the relevant counterfactuals are of a different sort. In the final section the issue of developmental explanation is discussed. It is argued that developmental explanations deserve their own place in taxonomy of explanations, although ultimately developmental dependencies can be analyzed as combinations of causal and constitutive dependencies. Hence, causal and constitutive explanation are distinct, but not always completely separate forms of explanation. (shrink)

One way social scientists explain phenomena is by building structural models. These models are explanatory insofar as they manage to perform a recursive decomposition on an initial multivariate probability distribution, which can be interpreted as a mechanism. Explanations in social sciences share important aspects that have been highlighted in the mechanisms literature. Notably, spelling out the functioning the mechanism gives it explanatory power. Thus social scientists should choose the variables to include in the model on the basis of their function (...) in the mechanism. This paper examines the notion of ‘function’ within structural modelling. We argue that ‘functions’ ought to be understood as the theoretical underpinnings of the causes, namely as the role that causes play in the functioning of the mechanism. (shrink)

What features will something have if it counts as an explanation? And will something count as an explanation if it has those features? In the second half of the 20th century, philosophers of science set for themselves the task of answering such questions, just as a priori conceptual analysis was generally falling out of favor. And as it did, most philosophers of science just moved on to more manageable questions about the varieties of explanation and discipline-specific scientific explanation. Often, such (...) shifts are sound strategies for problem-solving. But leaving fallow certain basic conceptual issues can also result in foundational debates. (shrink)

Existing accounts of mechanistic causation are not suited for understanding causation in biological and neural mechanisms because they do not have the resources to capture the unique causal structure of control heterarchies. In this paper, we provide a new account on which the causal powers of mechanisms are grounded by time-dependent, variable constraints. Constraints can also serve as a key bridge concept between the mechanistic approach to explanation and underappreciated work in theoretical biology that sheds light on how biological systems (...) channel energy to actively respond to the environment in adaptive ways, perform work, and fulfill the requirements to maintain themselves far from equilibrium. We show how the framework applies to several concrete examples of control in simple organisms as well as the nervous system of complex organisms. (shrink)

Part I of this paper introduces a range of mechanistic theories of causality, including process theories and the complex-systems theories, and some of the problems they face. Part II argues that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient, and describes one way of providing an analysis of causality which reaps the rewards of the mechanistic approach without succumbing to its pitfalls.

The mechanistic and causal accounts of explanation are often conflated to yield a ‘causal-mechanical’ account. This paper prizes them apart and asks: if the mechanistic account is correct, how can causal explanations be explanatory? The answer to this question varies according to how causality itself is understood. It is argued that difference-making, mechanistic, dualist and inferentialist accounts of causality all struggle to yield explanatory causal explanations, but that an epistemic account of causality is more promising in this regard.

Although philosophy has been only a minor contributor to cognitive science to date, this paper describes two projects in naturalistic philosophy of mind and one in naturalistic philosophy of science that have been pursued during the past 30 years and that can make theoretical and methodological contributions to cognitive science. First, stances on the mind–body problem (identity theory, functionalism, and heuristic identity theory) are relevant to cognitive science as it negotiates its relation to neuroscience and cognitive neuroscience. Second, analyses of (...) mental representations address both their vehicles and their contents; new approaches to characterizing how representations have content are particularly relevant to understanding the relation of cognitive agents to their environments. Third, the recently formulated accounts of mechanistic explanation in philosophy of science both provide perspective on the explanatory project of cognitive science and may offer normative guidance to cognitive science (e.g., by providing perspective on how multiple disciplinary perspectives can be integrated in understanding a given mechanism). (shrink)

This paper addresses questions about how the levels of causality (generic and single-case causality) are related. One question is epistemological: can relationships at one level be evidence for relationships at the other level? We present three kinds of answer to this question, categorised according to whether inference is top-down, bottom-up, or the levels are independent. A second question is metaphysical: can relationships at one level be reduced to relationships at the other level? We present three kinds of answer to this (...) second question, categorised according to whether single-case relations are reduced to generic, generic relations are reduced to single-case, or the levels are independent. We then explore causal inference in autopsy. This is an interesting case study, we argue, because it refutes all three epistemologies and all three metaphysics. We close by sketching an account of causality that survives autopsy—the epistemic theory. (shrink)

We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini :1509–1534. doi: 10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves, and from a greater appreciation of the problems (...) solved by bio-cognitive systems, chiefly regulation and prediction. We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of dynamical models as a central postulate in neurocognitive research. (shrink)

Some recent accounts of constitutive relevance have identified mechanism components with entities that are causal intermediaries between the input and output of a mechanism. I argue that on such accounts there is no distinctive inter-level form of mechanistic explanation and that this highlights an absence in the literature of a compelling argument that there are such explanations. Nevertheless, the entities that these accounts call ‘components’ do play an explanatory role. Studying causal intermediaries linking variables Xand Y provides knowledge of the (...) counterfactual conditions under which X will continue to bring about Y. This explanatory role does not depend on whether intermediate variables count as components. The question of whether there are distinctively mechanistic explanations remains open. (shrink)

Some recent accounts of constitutive relevance have identified mechanism components with entities that are causal intermediaries between the input and output of a mechanism. I argue that on such accounts there is no distinctive inter-level form of mechanistic explanation and that this highlights an absence in the literature of a compelling argument that there are such explanations. Nevertheless, the entities that these accounts call ‘components’ do play an explanatory role. Studying causal intermediaries linking variables Xand Y provides knowledge of the (...) counterfactual conditions under which X will continue to bring about Y. This explanatory role does not depend on whether intermediate variables count as components. The question of whether there are distinctively mechanistic explanations remains open. (shrink)

Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting (...) the functional properties of the system, which may not coincide with its mechanistic organization. I describe these techniques and show that despite being non-mechanistic, these cognitive models can satisfy the normative constraints on good explanations. (shrink)

In the current literature on scientific explanation unification became unfashionable in favour of causal approaches. We want to bring unification back into the picture. In this paper we demonstrate that resemblance questions do occur in scientific practice and that they cannot be properly answered without unification. Our examples show that resemblance questions about particular facts demand what we call causal network unification, while resemblance questions about regularities require what we call mechanism unification. We clarify how these types of unification relate (...) to Philip Kitcher’s account, but also to causal and mechanistic accounts of explanation. (shrink)

Experiments in cognitive neuroscience build a setup whose set of controlled stimuli and rules elicits a cognitive process in a participant. This setup requires researchers to decide the value of quite a few parameters along several dimensions. We call ‘’contextual factors’’ the parameters often assumed not to change the cognitive process elicited and are free to vary across the experiment’s repetitions. Against this assumption, empirical evidence shows that many of these contextual factors can significantly influence cognitive performance. Nevertheless, it is (...) not entirely clear what it means for a cognitive phenomenon to be context-sensitive and how to identify context-sensitivity experimentally. We claim that a phenomenon can be context-sensitive either because it is only triggered within a specific context or because different contexts change its manifestation conditions. Assessing which of these forms of context-sensitivity is present in a given phenomenon requires a criterion for individuating it across contextual variations. We argue that some inter-level experiments that, within the mechanistic approach to explanation, are required to identify relations of constitutive relevance between a phenomenon and a mechanism, are also necessary for individuating the phenomenon across its contextual variations. We articulate a criterion according to which behavioral variations across contexts indicate different phenomena if and only if the mechanistic activities, components and/or organizational properties recruited in each context are different. We support this approach by showing how it is applied in paradigmatic studies addressing cognitive performance differences resulting from contextual variations of task features, such as stimulus type and response modality. Finally, we address the challenge that a form of context-sensitivity possessed by the so-called ‘multifunctional mechanisms’ is incompatible with our proposal because it entails that the same mechanism can be recruited in different contexts to produce different phenomena. We examine key cases of multifunctionality and argue that they are consistent with our proposal because a single mechanism can have different components, activities and/or organizational properties in different contexts. Thus, these modifications may not affect the identity of a mechanism, and they could explain how it produced different phenomena in those contexts. (shrink)

Analysis of the adequacy of engineering design methods, as well as analysis of the utility of concepts of function often invoked in these methods, is a neglected topic in both philosophy of technology and in engineering proper. In this paper, I present an approach—dubbed an explanationist perspective—for assessing the adequacy of function-based design methods. Engineering design is often intertwined with explanation, for instance, in reverse engineering and subsequent redesign, knowledge base-assisted designing, and diagnostic reasoning. I argue that the presented approach (...) is useful for validating function-based design methods with respect to their explanatory elements and that it supports assessment of the explanatory and design utility of “function”, and the different conceptualizations thereof, as used in such engineering design methods. I deploy two key desiderata from the explanation literature to assess the viability of function-based design methods: explanatorily relevant difference-making factors and counterfactual understanding defined in terms of what-if-things-had-been-different questions. I explicate the approach and its merits in terms of two case studies drawn from the engineering functional modeling literature: reverse engineering and redesign and malfunction analysis. I close the paper by discussing ramifications of the presented approach for the philosophy of design and the philosophy of explanation. (shrink)

In this paper I address the current debate on ontic versus epistemic conceptualizations of mechanistic explanation in the mechanisms literature. Illari recently argued that good explanations are subject to both ontic and epistemic constraints: they must describe mechanisms in the world in such fashion that they provide understanding of their workings. Elaborating upon Illari’s ‘integration’ account, I argue that causal role function discovery of mechanisms and their components is an epistemic prerequisite for achieving these two aims. This analysis extends Illari’s (...) account in important ways, putting more pressure on ontic readings of mechanistic explanation and providing an answer to the question how ontic and epistemic constraints on mechanistic explanation are related. I argue these point in terms of cases on memory research drawn from neuroscience and research on extinct neurogenetic mechanisms from early nervous systems biology. (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)

In the recent literature on explanation in biology, increasing attention is being paid to the connection between design explanation and mechanistic explanation, viz. the role of design principles and heuristics for mechanism discovery and mechanistic explanation. In this paper we extend the connection between design explanation and mechanism discovery by prizing apart two different types of design explanation and by elaborating novel heuristics that one specific type offers for mechanism discovery across species. We illustrate our claims in terms of two (...) lines of biological research on the biological advantages of organismal traits, one on the eye-size of giant squid, the other on foraging habits of specific bat species. We argue that this research illustrates useful heuristics for mechanism discovery across species, viz. reasoning strategies to infer likely mechanisms for a certain biological role based on assessments of the environmental conditions in which the role is performed efficiently and less or in-efficiently. We bring out the novel features of our analysis in terms of a comparison with mechanistic approaches to mechanism discovery, amongst which graph-theoretical ones, and by comparing the different types of design explanation and the discovery heuristics they support. (shrink)

Assume that water reduces to H2O. If so water is identical to H2O. At the same time, if water reduces to H2O then H2O does not reduce to water–the reduction relation is asymmetric. This generates a puzzle–if water just is H2O it is hard to see how we can account for the asymmetry of the reduction relation. The paper proposes a solution to this puzzle. It is argued that the reduction predicate generates intensional contexts and that in order to account (...) for the asymmetry, we should develop conditions on the meanings of expressions that flank the reduction predicate in true reduction statements. Finally, it is argued that if we adopt this interpretation, we can illuminate the epistemological difference between reduced and reducing item commonly referred to in the literature. (shrink)

Current philosophical theorizing about technical functions is mainly focused on specifying conditions under which agents are justified in ascribing functions to technical artifacts. Yet, assessing the precise explanatory relevance of such function ascriptions is, by and large, a neglected topic in the philosophy of technical artifacts and technical functions. We assess the explanatory utility of ascriptions of technical functions in the following three explanation-seeking contexts: why was artifact x produced?, why does artifact x not have the expected capacity to ϕ?, (...) how does artifact x realize its capacity to ϕ? We argue that while function ascriptions serve a mere heuristic role in the first context, they have substantial explanatory leverage in the second and third context. In addition, we assess the relevance of function ascriptions in the context of engineering redesign. Here, function ascriptions also play a relevant role: they enable normative statements of the sort that component b functions better than component a. We unpack these claims by considering philosophical theories of technical functions, in particular the ICE theory, and engineering work on function ascription and explanation. We close the paper by relating our analysis to current debates on the explanatory power of mechanistic vis-à-vis functional explanations. (shrink)

The causal efficacy of a material system is usually thought to be produced by the law-like actions and interactions of its constituents. Here, a specific system is constructed and explained that produces a cause that cannot be understood in this way, but instead has novel and autonomous efficacy. The construction establishes a proof-of-feasibility of strong emergence. The system works by utilizing randomness in a targeted and cyclical way, and by relying on sustained evolution by natural selection. It is not vulnerable (...) to standard arguments against strong emergence, in particular ones that assume that the physical realm is causally closed. Moreover, it does not suffer from epiphenomenalism or causal overdetermination. The system uses only standard material components and processes, and is fully consistent with naturalism. It is discussed whether the emergent cause can still be viewed as ‘material’ in the way that term is commonly understood. (shrink)

A way to argue that something plays an explanatory role in science is by linking explanatory relevance with importance in the context of an explanation. The idea is deceptively simple: a part of an explanation is an explanatorily relevant part of that explanation if removing it affects the explanation either by destroying it or by diminishing its explanatory power, i.e. an important part is an explanatorily relevant part. This can be very useful in many ontological debates. My aim in this (...) paper is twofold. First of all, I will try to assess how this view on explanatory relevance can affect the recent ontological debate in the philosophy of mathematics—as I will argue, contrary to how it may appear at first glance, it does not help very much the mathematical realists. Second of all, I will show that there are big problems with it. (shrink)

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

The current antireductionist consensus rests in part on the indefensibility of the deductive-nomological model of explanation, on which classical reductionism depends. I argue that the DN model is inessential to the reductionist program and that mechanism provides a better framework for thinking about reductionism. This runs counter to the contemporary mechanists’ claim that mechanism is an alternative to reductionism. I demonstrate that mechanists are committed to reductionism, as evidenced by the historical roots of the contemporary mechanist program. This view shares (...) certain core commitments with reductionism. It is these shared commitments that constitute the essential elements of the reductionist program. (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)

This paper uses the economic crisis of 2008 as a case study to examine the explanatory validity of collective mental representations. Distinguished economists such as Paul Krugman and Joseph Stiglitz attribute collective beliefs, desires, intentions, and emotions to organizations such as banks and governments. I argue that the most plausible interpretation of these attributions is that they are metaphorical pointers to a complex of multilevel social, psychological, and neural mechanisms. This interpretation also applies to collective knowledge in science: scientific communities (...) do not literally have collective representations, but social mechanisms do make important contributions to scientific knowledge. (shrink)

Philosophers of science have developed an account of causal-mechanical explanation that captures regularity, but this account neglects variation. In this article I amend the philosophy of mechanisms to capture variation. The task is to explicate the relationship between regular causal mechanisms responsible for individual development and causes of variation responsible for variation in populations. As it turns out, disputes over this relationship have rested at the heart of the nature–nurture debate. Thus, an explication of the relationship between regular causal mechanisms (...) and causes of variation and between individual development and variation offers both the necessary amendment to the philosophy of mechanisms and the resources to mediate the dispute. (shrink)