Mechanistic Explanation

Viktor Hamburger was a developmental biologist interested in the ontogenesis of the vertebrate nervous system. A student of Hans Spemann at Freiburg in the 1920s, Hamburger picked up a holistic view of the embryo that precluded him from treating it in a reductionist way; at the same time, he was committed to a materialist and analytical approach that eschewed any form of vitalism or metaphysics. This paper explores how Hamburger walked this thin line between mechanistic reductionism and metaphysical vitalism in (...) light of his work on the factors influencing growth of neurons into limb buds, and the discovery of nerve growth factor, work carried out with Rita Levi-Montalcini and Stanley Cohen. (shrink)

A finer-grained delineation of a given explanandum reveals a nexus of closely related causal and non- causal explanations, complementing one another in ways that yield further explanatory traction on the phenomenon in question. By taking a narrower construal of what counts as a causal explanation, a new class of distinctively mathematical explanations pops into focus; Lange’s characterization of distinctively mathematical explanations can be extended to cover these. This new class of distinctively mathematical explanations is illustrated with the Lotka-Volterra equations. There (...) are at least two distinct ways those equations might hold of a system, one of which yields straightforwardly causal explanations, but the other of which yields explanations that are distinctively mathematical in terms of nomological strength. In the first, one first picks out a system or class of systems, finds that the equations hold in a causal -explanatory way; in the second, one starts with the equations and explanations that must apply to any system of which the equations hold, and only then turns to the world to see of what, if any, systems it does in fact hold. Using this new way in which a model might hold of a system, I highlight four specific avenues by which causal and non- causal explanations can complement one another. (shrink)

How regular do mechanisms need to be, in order to count as mechanisms? This paper addresses two arguments for dropping the requirement of regularity from the definition of a mechanism, one motivated by examples from the sciences and the other motivated by metaphysical considerations regarding causation. I defend a broadened regularity requirement on mechanisms that takes the form of a taxonomy of kinds of regularity that mechanisms may exhibit. This taxonomy allows precise explication of the degree and location of regular (...) operation within a mechanism, and highlights the role that various kinds of regularity play in scientific explanation. I defend this regularity requirement in terms of regularity’s role in individuating mechanisms against a background of other causal processes, and by prioritizing mechanisms’ ability to serve as a model of scientific explanation, rather than as a metaphysical account of causation. It is because mechanisms are regular, in the expanded sense described here, that they are capable of supporting the kinds of generalizations that figure prominently in scientific explanations. (shrink)

Leuridan (2010) argued that mechanisms cannot provide a genuine alternative to laws of nature as a model of explanation in the sciences, and advocates Mitchell’s (1997) pragmatic account of laws. I first demonstrate that Leuridan gets the order of priority wrong between mechanisms, regularity, and laws, and then make some clarifying remarks about how laws and mechanisms relate to regularities. Mechanisms are not an explanatory alternative to regularities; they are an alternative to laws. The existence of stable regularities in nature (...) is necessary for either model of explanation: regularities are what laws describe and what mechanisms explain. (shrink)

Kausales Denken spielt sowohl im Alltag wie auch im wissenschaftlichen Forschungsprozess eine zentrale Rolle. Es erlaubt uns, Phänomene vorherzusagen, zu kontrollieren und zu verstehen. Kausales Denken geht über die Angabe der Ursachen eines Phänomens hinaus: Wollen wir verstehen, warum ein Fahrrad fährt, so versuchen wir, Schritt für Schritt nachzuvollziehen, wie die einzelnen Bestandteile des Fahrrads zusammenwirken, um miteinander die Bewegung zu produzieren. Wir sind an dem Mechanismus interessiert, durch den das Phänomen zustande kommt. Dieses Vorgehen wird in der Wissenschaftsphilosophie wie (...) auch in der Kognitionspsychologie mit Hilfe mechanistischer Theorien kausalen Denkens beschrieben. Der Sammelband bringt erstmals Vertreter beider Disziplinen in sechs aufeinander abgestimmten Beiträgen zusammen. Neben einer Diskussion der Kernmerkmale mechanistischen Denkens stehen insbesondere Fragen nach dem Erwerb mechanistischer Denkstrukturen und nach der Rolle von Handlungen und Handlungserfahrungen im Fokus. (shrink)

The sixteenth and seventeenth centuries marks a period of transition between the vitalistic ontology that had dominated Renaissance natural philosophy and the Early Modern mechanistic paradigm endorsed by, among others, the Cartesians and Newtonians. This paper will focus on how the tensions between vitalism and mechanism played themselves out in the context of sixteenth and seventeenth century chemistry and chemical philosophy, particularly in the works of Paracelsus, Jan Baptista Van Helmont, Robert Fludd, and Robert Boyle. Rather than argue that these (...) natural philosophers each embraced either fully vitalistic or fully mechanistic ontologies, I hope to demonstrate that these thinkers adhered to complicated and nuanced ontologies that cannot be described in either purely vitalistic or purely mechanistic terms. A central feature of my argument is the claim that a corpuscularian theory of matter does not entail a strictly mechanistic and reductionistic account of chemical properties. I also argue that what marks the shift from pre-modern vitalistic chemical philosophy to the modern chemical philosophy that marked the Chemical Revolution is not the victory of mechanism and reductionism in chemistry but, rather, the shift to a physicalistic and naturalistic account of chemical properties and vital spirits. (shrink)

Instances of negative causation—preventions, omissions, and the like—have long created philosophical worries. In this paper, I argue that concerns about negative causation can be addressed in the context of causal explanation generally, and mechanistic explanation specifically. The gravest concern about negative causation is that it exacerbates the problem of causal promiscuity—that is, the problem that arises when a particular account of causation identifies too many causes for a particular effect. In the explanatory context, the problem of promiscuity can be solved (...) by characterizing the phenomenon to be explained as a contrast between two or more events or non-events. This contrastive strategy also can solve other problems that negative causation presents for the leading accounts of mechanistic explanation. Along the way, I argue that to be effective, accounts of causal explanation must incorporate negative causation. I also develop a taxonomy of negative causation and incorporate each variety of negative causation into the leading accounts of mechanistic explanation. (shrink)

The first part of this paper finds Craver’s (2007) mutual manipulability theory (MM) of constitution inadequate, as it definitionally ties constitution to the feasibility of idealized experiments, which, however, are unrealizable in principle. As an alternative, the second part develops an abductive theory of constitution (NDC), which exploits the fact that phenomena and their constituents are unbreakably coupled via common causes. The best explanation for this common-cause coupling is the existence of an additional dependence relation, viz. constitution. Apart from adequately (...) capturing the essential characteristics of constitution missed by MM, NDC has important ramifications for constitutional discovery—most notably, that there is no experimentum crucis for constitution, not even under ideal discovery circumstances. (shrink)

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the (...) analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an opportunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would suggest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to implement novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engineering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engineering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. (shrink)

Philosophical discussions of systems biology have enriched the notion of mechanistic explanation by pointing to the role of mathematical modeling. However, such accounts still focus on explanation in terms of tracking a mechanism's operation across time (by means of mental or computational simulation). My contention is that there are explanations of molecular systems where the explanatory understanding does not consist in tracking a mechanism's operation and productive continuity. I make this case by a discussion of bifurcation analysis in dynamical systems, (...) articulating the distinctive way in which explanatory understanding is provided, especially about the reversibility or irreversibility of molecular processes. (shrink)

Ongoing empirical discoveries in molecular biology have generated novel conceptual challenges and perspectives. Philosophers of biology have reacted to these trends when investigating the practice of molecular biology and contributed to scientific debates on methodological and conceptual matters. This article reviews some major philosophical issues in molecular biology. First, philosophical accounts of mechanistic explanation yield a notion of explanation in the context of molecular biology that does not have to rely on laws of nature and comports well with molecular discovery. (...) Second, reductionism continues to be debated and increasingly be rejected by scientists. Philosophers have likewise moved away from reduction toward integration across fields or integrative explanations covering several levels of organization. Third, although the gene concept has undergone substantial transformation and even fragmentation, it still enjoys widespread use by molecular biologists, which has prompted philosophers to understand the empirical reasons for this. At the same time, it has been argued the notion of ‘genetic information’ is largely an empty metaphor, which generates the illusion of explanatory understanding without offering an adequate explanation of molecular and developmental mechanisms. (shrink)

Evolutionary developmental biology (evo-devo) is considered a ‘mechanistic science,’ in that it causally explains morphological evolution in terms of changes in developmental mechanisms. Evo-devo is also an interdisciplinary and integrative approach, as its explanations use contributions from many fields and pertain to different levels of organismal organization. Philosophical accounts of mechanistic explanation are currently highly prominent, and have been particularly able to capture the integrative nature of multifield and multilevel explanations. However, I argue that evo-devo demonstrates the need for a (...) broadened philosophical conception of mechanisms and mechanistic explanation. Mechanistic explanation (in terms of the qualitative interactions of the structural parts of a whole) has been developed as an alternative to the traditional idea of explanation as derivation from laws or quantitative principles. Against the picture promoted by Carl Craver, that mathematical models describe but usually do not explain, my discussion of cases from the strand of evo-devo which is concerned with developmental processes points to qualitative phenomena where quantitative mathematical models are an indispensable part of the explanation. While philosophical accounts have focused on the actual organization and operation of mechanisms, properties of developmental mechanisms that are about how a mechanism reacts to modifications are of major evolutionary significance, including robustness, phenotypic plasticity, and modularity. A philosophical conception of mechanisms is needed that takes into account quantitative changes, transient entities and the generation of novel types of entities, feedback loops and complex interaction networks, emergent properties, and, in particular, functional-dynamical aspects of mechanisms, including functional (as opposed to structural) organization and distributed, system-wide phenomena. I conclude with general remarks on philosophical accounts of explanation. (shrink)

The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models—which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation have failed to address how a mathematical model could contribute to such explanations. I discuss how mathematical (...) equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena, where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism’s ability to respond to perturbations. I offer general conclusions for philosophical accounts of explanation. (shrink)

We address the question of whether and to what extent explanatory and modelling strategies in systems biology are mechanistic. After showing how dynamic mathematical models are actually required for mechanistic explanations of complex systems, we caution readers against expecting all systems biology to be about mechanistic explanations. Instead, the aim may be to generate topological explanations that are not standardly mechanistic, or to arrive at design principles that explain system organization and behaviour in general, but not specific mechanisms. These abstraction (...) strategies serve various aims, including prediction and control, that are central to understanding the epistemic diversity of systems biology. (shrink)

Life-science phenomena are often explained by specifying the mechanisms that bring them about. The new mechanistic philosophers have done much to substantiate this claim and to provide us with a better understanding of what mechanisms are and how they explain. Although there is disagreement among current mechanists on various issues, they share a common core position and a seeming commitment to some form of scientific realism. But is such a commitment necessary? Is it the best way to go about mechanistic (...) explanation? In this article, we propose an alternative antirealist account that also fits explanatory practice in the life sciences. We pay special attention to mechanistic models, i.e. scientific models that involve a mechanism, and to the role of coherence considerations in building such models. To illustrate our points, we consider the mechanism for the action potential. 1 Introduction2 Some Core Features of Mechanistic Explanation3 Scientific Realism and Mechanistic Explanation4 Antirealist Mechanistic Explanation: The Case of the Action Potential5 Some Outstanding Issues for the Antirealist Mechanist6 Two Problems for the Realist Mechanist7 Conclusions. (shrink)

This paper will examine the nature of mechanisms and the distinction between the relevant and irrelevant parts involved in a mechanism’s operation. I first consider Craver’s account of this distinction in his book on the nature of mechanisms, and explain some problems. I then offer a novel account of the distinction that appeals to some resources from Mackie’s theory of causation. I end by explaining how this account enables us to better understand what mechanisms are and their various features.

Despite widespread recognition that psychiatry would be better served by a classificatory system based on etiology rather than mere description, it goes without saying that much of the necessary work is yet to be done. In this chapter I take up the increasingly important question of how mechanistic explanation fits into the larger effort to build a scientifically sound etiological and nosological framework. I sketch a rough picture of what mechanistic explanation should look like in the context of psychiatric research, (...) with a focus on several potential challenges posed by the special features of many psychiatric conditions. These include the role of social and environmental factors, the relatively transient nature of symptoms, the presumably complex organization of underlying systems, and the likelihood that many disorders are the product of nonstandard development. I suggest that these explanatory challenges can be met with a sufficiently broad notion of mechanism, one that allows for something less than the flawless execution of internal operations, appeals to the influence of contextual factors, and attends to organizational relations both within the mechanism itself and across the wider cognitive system. (shrink)

Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what is (...) required of an adequate mechanistic model. Mechanistic models are explanatory. (shrink)

Die philosophische Kausaldebatte hat in den vergangenen vier Jahrzehnten eine neue Blüte erlebt. Kontrafaktische, interventionistische, mechanistische und transfertheoretische Ansätze haben sich neben den bislang dominierenden Regularitätstheorien etabliert. Vertreter aller dieser Ansätze sehen sich jedoch mit Gegenbeispielen konfrontiert, keine Theorie scheint allen unseren intuitiven Kausalurteilen gerecht werden zu können. Dieses Buch führt anhand ausgewählter Beispiele in die aktuelle Debatte ein und liefert eine Erklärung für die derzeitige Patt-Situation. Der Grund dafür, dass sich zu jedem Ansatz offenbar mühelos Gegenbeispiele finden lassen, liegt, (...) so zeigt die Autorin, in einer bislang unbemerkten Mehrdeutigkeit des Ausdrucks »kausaler Zusammenhang«. Wer danach fragt, was ein kausaler Zusammenhang sei, kann damit entweder einen konkreten Vorgang – Verursachung – oder einen Zusammenhang zwischen zwei Tatsachen – kausale Relevanz – meinen. Der Unterschied zwischen diesen beiden Begriffen wird mit Hilfe einer sprachphilosophischen Analyse singulärer Kausalaussagen belegt und auf zwei verschiedene Zugänge zu kausalen Zusammenhängen – Kausalwahrnehmung und kausales Schließen – zurückgeführt. (shrink)

I use Carl Gillett’s much heralded dimensioned theory of realization as a platform to develop a plausible part–whole theory. I begin with some basic desiderata for a theory of realization that its key terms should be defined and that it should be explanatory. I then argue that Gillett’s original theory violates these conditions because its explanatory force rests upon an unspecified “in virtue of” relation. I then examine Gillett’s later version that appeals instead to theoretical terms tied to “mechanisms.” Yet (...) I argue that it too violates the desiderata, since it defines realization for mechanisms in terms of two undefined ideas whose explanatory credentials have not been established—“implementation” and “grounds.” Thus I drop those ideas in favor of an explicit constraint that the parts and properties provide a mechanistic explanation. I also distinguish a special mechanistic theory from a preferred general theory that incorporates other kinds of part–whole explanations that target causal powers or capacities. The result is a theory that has the explanatory virtues of mechanistic theories as well as a broader scope desired by Gillett. I also compare the result to a similar idea from Robert Cummins that has been neglected in recent discussions of realization, namely, his general property analysis rather than his functional analysis. Finally, I defend the preferred general theory against possible objections that attempt to show a conflict between metaphysical demands on a theory of realization versus facts about good scientific explanation. (shrink)

Philosophers almost universally believe that concepts of supervenience fail to satisfy the standards for physicalism because they offer mere property correlations that are left unexplained. They are thus compatible with non-physicalist accounts of those relations. Moreover, many philosophers not only prefer some kind of functional-role theory as a physically acceptable account of mind-body and other inter-level relations, but they use it as a form of “superdupervenience” to explain supervenience in a physically acceptable way. But I reject a central part of (...) this common narrative. I argue that functional-role theories fail by the same standards for physicalism because they merely state without explaining how a physical property plays or occupies a functional role. They are thus compatible with non-physicalist accounts of that role-occupying relation. I also argue that one cannot redeploy functional-role theory at a deeper level to explain role occupation, specifically by iterating the role-occupant scheme. Instead, one must use part-whole structural and mechanistic explanations that differ from functional-role theory in important ways. These explanations represent a form of “superduperfunctionalism” that stand to functional-role theory as concepts of superdupervenience stand to concepts of supervenience. (shrink)

Mechanistic frameworks of investigation and explanation dominate the cognitive, neural, and psychological sciences. In this dissertation, I argue that mechanistic frameworks cannot, in principle, explain some kinds of cognition. In its place, I argue that complexity science has methods and theories more appropriate for investigating and explaining some cognitive phenomena. -/- I begin with an examination of the term 'cognition.' I defend the idea that "cognition" has been a moving target of investigation in the relevant sciences. As such it is (...) not historically true that there has been a thoroughly entrenched and agreed upon conception of "cognition." Next, I take up mechanistic frameworks. Although 'mechanism' is an umbrella term for a set of loosely related characteristics, there are common features: linearity, localization, and component dominance. I then describe complexity science, with emphasis on its utilization of dynamical systems modeling. Next, I discuss two phenomena that typically fall under the purview of complexity science: nonlinearity and interaction dominance. -/- A complexity science framework guided by the theory of self-organized criticality and utilizing the methods of dynamical systems modeling can surmount a number of challenges that face mechanistic frameworks when investigating some kinds of cognition. The first challenge is epistemic and concerns the inadequacy of mechanistic frameworks to facilitate the comprehensibility of massive amounts of data across various scales and areas of inquiry. I argue that complexity science is more appropriate for making big data comprehensible when investigating cognition, particularly across disciplines. I demonstrate this via an approach called nested dynamical modeling (NDM). NDM can facilitate comprehensibility of large amounts of data obtained from various scales of investigation by eliminating irrelevant degrees of freedom of that system as relates to the target of investigation. -/- The second shortcoming concerns ontological blind spots within mechanistic frameworks. Cognitive phenomena like extended cognition often fail to meet most, if not all, of the criteria assumed by many mechanistic approaches, especially component dominance. I argue that research guided by the notion of interaction dominance allows for extended cognition to be a real, empirically supported phenomenon within complex systems frameworks. In this chapter I discuss some of my experimental work on extended cognitive systems. -/- The search for mechanisms can be a reasonable starting position when attempting to explain natural phenomena in the life sciences. However, too strict of an adherence to theoretical and methodological commitments such as linearity, localization, and component dominance can result in intractable epistemic challenges and ontological blind spots. Complexity science has theories and methods to overcome such challenges in the investigation of cognition. (shrink)

There is more than one explanation for the evolution of sexual reproduction. This paper investigates the possibility that this pluralism exists because these different explanations rely on intuitions provided by different philosophical theories of explanation, namely unifying views and causal mechanical views. I conclude that this is not the case.

This paper investigates the relationship between structural explanation and the New Mechanistic account of explanation. The aim of this paper is twofold: firstly, to argue that some phenomena in the domain of fundamental physics, although mechanically brute, are structurally explained; and secondly, by elaborating on the contrast between SE and mechanistic explanation to better clarify some features of SE. Finally, this paper will argue that, notwithstanding their apparently antithetical character, SE and ME can be reconciled within a unified account of (...) general scientific explanation. (shrink)

The interventionist account of causal explanation, in the version presented by Jim Woodward, has been recently claimed capable of buttressing the widely felt—though poorly understood—hunch that high-level, relatively abstract explanations, of the sort provided by sciences like biology, psychology and economics, are in some cases explanatorily optimal. It is the aim of this paper to show that this is mistaken. Due to a lack of effective constraints on the causal variables at the heart of the interventionist causal-explanatory scheme, as presently (...) formulated it is either unable to prefer high-level explanations to low, or systematically overshoots, recommending explanations at so high of a level as to be virtually vacuous. (shrink)

This paper critiques the new mechanistic explanatory program on grounds that, even when applied to the kinds of examples that it was originally designed to treat, it does not distinguish correct explanations from those that blunder. First, I offer a systematization of the explanatory account, one according to which explanations are mechanistic models that satisfy three desiderata: they must 1) represent causal relations, 2) describe the proper parts, and 3) depict the system at the right ‘level.’ Second, I argue that (...) even the most developed attempts to fulfill these desiderata fall short by failing to appropriately constrain explanatorily apt mechanistic models. -/- *This paper used to be called "The Emperor's New Mechanisms". (shrink)

This article presents a distinct sense of ‘mechanism’, which I call the functional sense of mechanism. According to this sense, mechanisms serve functions, and this fact places substantive restrictions on the kinds of system activities ‘for which’ there can be a mechanism. On this view, there are no mechanisms for pathology; pathologies result from disrupting mechanisms for functions. Second, on this sense, natural selection is probably not a mechanism for evolution because it does not serve a function. After distinguishing this (...) sense fromsimilar explications of ‘mechanism’, I argue that it is ubiquitous in biology and has valuable epistemic benefits. (shrink)

Casini, Illari, Russo, and Williamson (2011) suggest to model mechanisms by means of recursive Bayesian networks (RBNs) and Clarke, Leuridan, and Williamson (2014) extend their modelling approach to mechanisms featuring causal feedback. One of the main selling points of the RBN approach should be that it provides answers to questions concerning manipulation and control. In this paper I demonstrate that the method to compute the effects of interventions the authors mentioned endorse leads to absurd results under the additional assumption of (...) faithfulness, which can be expected to hold for many RBN models of mechanisms. (shrink)

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...) shows the need for quantitative, probabilistic models of mechanisms, too. In this paper we argue that the formal framework of causal graph theory is well-suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the ba-sis of an example from contemporary biological practice, namely feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multi-level character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes, while being insufficient for others. (shrink)

Mechanisms play an important role in many sciences when it comes to questions concerning explanation, prediction, and control. Answering such questions in a quantitative way requires a formal represention of mechanisms. Gebharter (2014) suggests to represent mechanisms by means of one or more causal arrows of an acyclic causal net. In this paper we show how this approach can be extended in such a way that it can also be fruitfully applied to mechanisms featuring causal feedback.

One of the main early forms of “functionalism,” developed by writers like Jerry Fodor and William Lycan, focused on “mechanistic” explanation in the special sciences and argued that “functional properties” in psychology were continuous in nature with the special science properties posited in such mechanistic explanations. I dub the latter position“Continuity Functionalism” and use it to critically examine the “Standard Picture” of the metaphysics of functionalism which takes “functional” properties to be second-order properties and claims there are two metaphysical forms (...) of “functionalism,” in so-called “role” and “realizer” functionalism. Looking at mechanistic explanations, I show that the “functional properties” posited in the special sciences are not second-order properties and that Continuity Functionalism is a distinctive metaphysical version of functionalism not covered by the Standard Picture. My conclusion is that the Standard Picture offers us a false dichotomy of ways to be a “functionalist” and that “functionalists” thus need to look once more to the special sciences to configure their views, thus eschewing the machinery inherited from “analytic” philosophers. (shrink)

I characterize Wimsatt’s approach to philosophy of science as philosophy for science and then briefly consider a theme emerging from his work that informs just one of the many current developments in philosophy of biology that he inspired: scaffolding as a problem of mechanistic explanation for functionalists.

L’Homme presents what has been termed Descartes’ “physiological psychology”. It envisions and seeks to explain how the brain and nerves might yield situationally appropriate behavior through mechanical means. On occasion in the past 150 years, this aim has been recognized, described, and praised. Still, acknowledgement of this aspect of Descartes’ writing has been spotty in histories of neuroscience and histories of psychology. In recent years, there has been something of a resurgence. This chapter argues that, in seeking to explain psychological (...) functions such as sense perception, attention, memory, and emotional response, Descartes ascribed a range of active functions to the brain acting on its own (independently of mind). (shrink)

During the past decade, social mechanisms and mechanism-based ex- planations have received considerable attention in the social sciences as well as in the philosophy of science. This article critically reviews the most important philosophical and social science contributions to the mechanism approach. The first part discusses the idea of mechanism- based explanation from the point of view of philosophy of science and relates it to causation and to the covering-law account of explanation. The second part focuses on how the idea (...) of mechanisms has been used in the social sciences. The final part discusses recent developments in analytical sociology, covering the nature of sociological explananda, the role of theory of action in mechanism-based explanations, Merton’s idea of middle-range theory, and the role of agent-based simulations in the development of mechanism-based explanations. (shrink)

Historically embryogenesis has been among the most philosophically intriguing phenomena. In this paper I focus on one aspect of biological development that was particularly perplexing to the ancients: self-organisation. For many ancients, the fact that an organism determines the important features of its own development required a special model for understanding how this was possible. This was especially true for Aristotle, Alexander, and Simplicius, who all looked to contemporary technology to supply that model. However, they did not all agree on (...) what kind of device should be used. In this paper I explore the way these ancients made use of technology as a model for the developing embryo. I argue that their different choices of device reveal fundamental differences in the way each thinker understood the nature of biological development itself. In the final section of the paper I challenge the traditional view (dating back to Alexander's interpretation of Aristotle) that the use of automata in GA can simply be read off from their use in the de motu. (shrink)

Researchers in the enactivist tradition have recently argued that social interaction can constitute social cognition, rather than simply serve as the context for social cognition. They contend that a focus on social interaction corrects the overemphasis on mechanisms inside the individual in the explanation of social cognition. I critically assess enactivism’s claims about the explanatory role of social interaction in social cognition. After sketching the enactivist approach to cognition in general and social cognition in particular, I identify problems with an (...) enactivist taxonomy of roles for social interaction in the explanation of social cognition (contextual, enabling, and constitutive). In particular, I show that this enactivist taxonomy does not clearly distinguish between enabling conditions and constitutive elements, which would make them in danger of committing the coupling-constitution fallacy found in some attempts to extend cognition. I explore resources enactivism has to more clearly demarcate constitutive parts of a cognitive system, but identify problems in applying them to some of the main cases of social cognition enactivists characterize as being constituted by social interaction. I offer the mechanistic approach to explanation as an alternative that captures much of what enactivists want to say about the relations between social and individual levels, but views social interactions from the perspective of embedded cognition rather than as being constitutive of social cognition. (shrink)

Psychological construction represents an important new approach to psychological phenomena, one that has the promise to help us reconceptualize the mind both as a behavioral and as a biological system. It has so far been developed in the greatest detail for emotion, but it has important implications for how researchers approach other mental phenomena such as reasoning, memory, and language use. Its key contention is that phenomena that are characterized in (folk) psychological vocabulary are not themselves basic features of the (...) mind, but are constructed from more basic psychological operations. The framework of mechanistic explanation, currently under development in philosophy of science, can provide a useful perspective on the psychological construction approach. A central insight of the mechanistic account of explanation is that biological and psychological phenomena result from mechanisms in which component parts and operations do not individually exhibit the phenomena of interest but function together in an orchestrated and sometimes in a complex dynamical manner to generate it. While at times acknowledging the compatibility of the mechanist approach with constructionist approach (Lindquist, Wager, Kober, Bliss-Moreau, & Barrett, 2012), proponents of the constructionist approach have at other times pitched their approach as anti-mechanist. For example, Barrett (2009) claims that the psychological constructionist approach rejects machines as the primary metaphor for understanding the mind, instead favoring a recipe metaphor; constructionism also purportedly rejects the “mechanistic” picture of causation, which it portrays as linear or sequential in nature (see also Barrett, Wilson-Mendenhall, & Barsalou, in press). While some mechanistic accounts do fit this description, we will see that the mechanisms generating phenomena can be complex and dynamic, producing phenomena far less stereotypic and more adaptive than people often associate with machines. Our goal, however, is not just to render constructionism and mechanism compatible. Philosophers of science have been examining the nature of mechanistic explanation in biology with the goal of gaining new insights into the operation of science. We will identify some of the places where the mechanistic account can shed new light on the constructionist project. (shrink)

Traditional accounts of explanation fail to illuminate the explanatory relevance of “models that are descriptively false” in the sense that the regularities they entail fail to obtain. In this paper, I propose an account of explanation, which I call ‘explanation by concretization’, that serves to explicate the explanatory relevance of such models. Starting from a highly abstract and idealized model, causal explanations of the absence of regularities are sought by adding complexity to the model or by concretizing it. Whether this (...) process is successful depends on whether the abstractions and idealizations in the basic model succeed in isolating a mechanism, i.e. in representing how it operates when interfering factors are absent. This account is developed in the context of economics and contrasted to those of Daniel Hausman and Nancy Cartwright. I go on to provide an account of how unrealistic models can be used for providing understanding of the way mechanisms work. (shrink)

In this paper, we examine what is to be said in defence of Machamer, Darden and Craver’s (MDC) controversial dualism about activities and entities (Machamer, Darden and Craver’s in Philos Sci 67:1–25, 2000). We explain why we believe the notion of an activity to be a novel, valuable one, and set about clearing away some initial objections that can lead to its being brushed aside unexamined. We argue that substantive debate about ontology can only be effective when desiderata for an (...) ontology are explicitly articulated. We distinguish three such desiderata. The first is a more permissive descriptive ontology of science, the second a more reductive ontology prioritising understanding, and the third a more reductive ontology prioritising minimalism. We compare MDC’s entities-activities ontology to its closest rival, the entities-capacities ontology, and argue that the entities-activities ontology does better on all three desiderata. (shrink)

While mechanistic explanation and, to a lesser extent, nomological explanation are well-explored topics in the philosophy of biology, topological explanation is not. Nor is the role of diagrams in topological explanations. These explanations do not appeal to the operation of mechanisms or laws, and extant accounts of the role of diagrams in biological science explain neither why scientists might prefer diagrammatic representations of topological information to sentential equivalents nor how such representations might facilitate important processes of explanatory reasoning unavailable to (...) scientists who restrict themselves to sentential representations. Accordingly, relying upon a case study about immune system vulnerability to attacks on CD4+ T-cells, I argue that diagrams group together information in a way that avoids repetition in representing topological structure, facilitate identification of specific topological properties of those structures, and make available to controlled processing explanatorily salient counterfactual information about topological structures, all in ways that sentential counterparts of diagrams do not. (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)