This paper proposes an analysis of the discursive dynamics of high-impact concepts in the humanities. These are concepts whose formation and development have a lasting and wide-ranging effect on research and our understanding of discursive reality in general. The notion of a conceptual practice, based on a normative conception of practice, is introduced, and practices are identified, on this perspective, according to the way their respective performances are held mutually accountable. This normative conception of practices is then combined with recent (...) work from philosophy of science that characterizes concepts in terms of conceptual capacities that are productive, open-ended, and applicable beyond the original context they were developed in. It is shown that the formation of concepts can be identified by changes in how practitioners hold exercise of their conceptual capacities accountable when producing knowledge about a phenomenon. In a manner similar to the use of operational definitions in scientific practices, such concepts can also be used to intervene in various discourses within or outside the conceptual practice. Using the formation of the concepts “mechanism” and “performative” as examples, the paper shows how high-impact concepts reconfigure what is at issue and at stake in conceptual practices. As philosophy and other humanities disciplines are its domain of interest, it is a contribution to the methodology of the humanities. (shrink)

Philosophers of psychology debate, among other things, which psychological models, if any, are (or provide) mechanistic explanations. This should seem a little strange given that there is rough consensus on the following two claims: 1) a mechanism is an organized collection of entities and activities that produces, underlies, or maintains a phenomenon, and 2) a mechanistic explanation describes, represents, or provides information about the mechanism producing, underlying, or maintaining the phenomenon to be explained (i.e. the explanandum phenomenon) (Bechtel and Abrahamsen (...) 2005; Craver 2007). If there is a rough consensus on what mechanisms are and that mechanistic explanations describe, represent, or provide information about them, then how is there no consensus on which psychological models are (or provide) mechanistic explanations? Surely the psychological models that are mechanistic explanations are the models that describe, represent, or provide information about mechanisms. That is true, of course; the trouble arises when determining what exactly that involves. Philosophical disagreement over which psychological models are mechanistic explanations is often disagreement about what it means to describe, represent, or provide information about a mechanism, among other things (Hochstein 2016; Levy 2013). In addition, one's position in this debate depends on a host of other seemingly arcane metaphysical issues, such as the nature of mechanisms, computational and functional properties (Piccinini 2016), and realization (Piccinini and Maley 2014), as well as the relation between models, methodologies, and explanations (Craver 2014; Levy 2013; Zednik 2015). Although I inevitably advocate a position, my primary aim in this chapter is to spell out all these relationships and canvas the positions that have been taken (or could be taken) with respect to mechanistic explanation in psychology, using dynamical systems models and cognitive models (or functional analyses) as examples. (shrink)

Mechanisms, in the prominent biological sense of the term, are historical entities. That is, whether or not something is a mechanism for something depends on its history. Put differently, while your spontaneously-generated molecule-for-molecule double has a heart, and its heart pumps blood around its body, its heart does not have a mechanism for pumping, since it does not have the right history. My argument for this claim is that mechanisms have proper functions; proper functions are historical entities; so, mechanisms are (...) historical entities, too. This thesis runs against the mainstream new mechanist way of thinking about mechanisms, where mechanisms are generally thought of in an ahistorical way. After arguing for this thesis, I draw out some consequences for philosophy of science and metaphysics. (shrink)

We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: Are networks explainable, and if so, what does it mean to explain the output of a network? And what does it mean for a network to be interpretable? We argue that accounts of “explanation” tailored specifically to neural networks have ineffectively reinvented the wheel. In (...) response to, we show how four familiar accounts of explanation apply to neural networks as they would to any scientific phenomenon. We diagnose the confusion about explaining neural networks within the machine learning literature as an equivocation on “explainability,” “understandability” and “interpretability.” To remedy this, we distinguish between these notions, and answer by offering a theory and typology of interpretation in machine learning. Interpretation is something one does to an explanation with the aim of producing another, more understandable, explanation. As with explanation, there are various concepts and methods involved in interpretation: Total or Partial, Global or Local, and Approximative or Isomorphic. Our account of “interpretability” is consistent with uses in the machine learning literature, in keeping with the philosophy of explanation and understanding, and pays special attention to medical artificial intelligence systems. (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)

I consider three explanatory strategies from recent systems biology that are driven by mathematics as much as mechanistic detail. Analysis of differential equations drives the first strategy; topological analysis of network motifs drives the second; mathematical theorems from control engineering drive the third. I also distinguish three abstraction types: aggregations, which simplify by condensing information; generalizations, which simplify by generalizing information; and structurations, which simplify by contextualizing information. Using a common explanandum as reference point—namely, the robust perfect adaptation of chemotaxis (...) in Escherichia coli—I argue that each strategy invokes a different combination of abstraction types and that each targets its abstractions to different mechanistic details. (shrink)

In discussion of mechanisms, philosophers often debate about whether quantitative descriptions of generalizations or qualitative descriptions of operations are explanatorily fundamental. I argue that these debates have erred by conflating the explanatory roles of generalizations and patterns. Patterns are types of variations within or between quantities in a mechanism over time or across conditions. While these patterns must often be represented in addition to descriptions of operations in order to explain a phenomenon, they are not equivalent to generalizations because their (...) explanatory role does not depend on any specific facts about their scope or domain of invariance. (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)

Biological processes are often explained by identifying the underlying mechanisms that generate a phenomenon of interest. I characterize a basic account of mechanistic explanation and then present three challenges to this account, illustrated with examples from molecular biology. The basic mechanistic account is insufficient for explaining nonsequential and nonlinear dynamic processes, is insufficient for explaining the inherently stochastic nature of many biological mechanisms, and fails to give a proper framework for analyzing organization. I suggest that biological processes are best approached (...) as a multidimensional gradient—with some processes being paradigmatic cases of mechanisms and some being marginal cases. (shrink)

A common kind of explanation in cognitive neuroscience might be called functiontheoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals (...) the causal structure of the mechanism underlying the capacity. If they are right, then a cognitive model that resists a transparent mapping to known neural mechanisms fails to be explanatory. I argue that a functiontheoretic characterization of a cognitive capacity can be genuinely explanatory even absent an account of how the capacity is realized in neural hardware. (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)

Invasion ecology addresses the spread of species outside of their native ranges. A central aim of this field is to find mechanistic explanations for why species are able to establish and spread in an area in which they did not evolve. Usually it remains unclear, however, what exactly is meant by ‘mechanistic explanation’ or ‘mechanism’. The paper argues that the field can benefit from the philosophical discussion of what a mechanism is. Based on conceptions of mechanisms as processes in concrete (...) systems, causal mechanisms can be defined as one type of mechanism, representing recurring networks of causal relationships. With the example of a well-known hypothesized mechanism in invasion ecology, namely enemy release, the paper demonstrates how such causal mechanisms can be depicted as causal network diagrams. This approach could facilitate the development of step-by-step explanations, enhance clear argumentation and allow for more precise linkage of empirical tests to theory. Challenges to assessing the empirical relevance of hypothesized mechanisms are discussed, and suggestions are made concerning how the proposed approach could help in overcoming some of them. (shrink)

An important strand in philosophy of science takes scientific explanation to consist in the conveyance of some kind of information. Here I argue that this idea is also implicit in some core arguments of mechanists, some of whom are proponents of an ontic conception of explanation that might be thought inconsistent with it. However, informational accounts seem to conflict with some lay and scientific commonsense judgments and a central goal of the theory of explanation, because information is relative to the (...) background knowledge of agents. Sometimes we make lay judgments about whether a model is an explanation simpliciter, not just an explanation relative to some particular agent. And as philosophers of explanation, we would like a philosophical account to tell us when a model is an explanation simpliciter, not just when a model is an explanation relative to some particular agent. Thus, even if one’s account of explanation is not concerned with explanation qua communicative or speech act, the account’s reliance on the concept of information generates a prima facie conflict between the claims that 1) explanation is the conveyance of information, 2) information is relative to the background knowledge of an agent, and 3) some models are explanations not relative to the background knowledge of any particular agent. I sketch a solution to this puzzle by distinguishing informationally what I call “explanation simpliciter” from what I call “explanation-to,” relativizing the latter to an individual’s background knowledge and the former to what I call “total scientific background knowledge”. (shrink)

Current debates about the integration of traditional and academic ecological knowledge struggle with a dilemma of division and assimilation. On the one hand, the emphasis on differences between traditional and academic perspectives has been criticized as creating an artificial divide that brands TEK as “non-scientific” and contributes to its marginalization. On the other hand, there has been increased concern about inadequate assimilation of Indigenous and other traditional perspectives into scientific practices that disregards the holistic nature and values of TEK. The (...) aim of this article is to develop a practice-based account of the epistemic relations between TEK and AEK that avoids both horns of the dilemma. While relations between TEK and AEK are often described in terms of the “holistic” nature of the former and the “mechanistic” character of the latter, we argue that a simple holism–mechanism divide misrepresents the epistemic resources of both TEK and AEK. Based on the literature on mechanistic explanations in philosophy of science, we argue that holders of TEK are perfectly capable of identifying mechanisms that underlie ecological phenomena while AEK often relies on non-mechanistic strategies of dealing with ecological complexity. Instead of generic characterizations of knowledge systems as either mechanistic or holistic, we propose to approach epistemic relations between knowledge systems by analyzing their heuristics in practice. (shrink)

. Philosophers and scientists often cite ontic factors when explaining the methods and success of scientific inquiry. That is, the adoption of a method or approach is explained in reference to the kind of system in which the scientist is interested: these are explanations of why scientists do what they do, that appeal to properties of their target systems. We present a framework for understanding such “Opticks to his Principia. Newton’s optical work is largely experiment-driven, while the Principia is primarily (...) mathematical, so usually, each work is taken to exemplify a different kind of science. However, Newton himself often presented them in terms of a largely consistent method. We use our framework to articulate an original and plausible position: that the differences between the Opticks and the Principia are due to the kinds of systems targeted. That is, we provide an ontic-driven explanation of methodological differences. We suspect that ontic factors should have a more prominent role in historical explanations of scientific method and development. (shrink)

A common kind of explanation in cognitive neuroscience might be called function-theoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes the exercise of the cognitive capacity (in the system's normal environment). Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals (...) the causal structure of the mechanism underlying the capacity. If they are right, then a cognitive model that resists a transparent mapping to known neural mechanisms fails to be explanatory. I argue that a function-theoretic characterization of a cognitive capacity can be genuinely explanatory even absent an account of how the capacity is realized in neural hardware. (shrink)

Medical explanations have often been thought on the model of biological ones and are frequently defined as mechanistic explanations of a biological dysfunction. In this paper, I argue that topological explanations, which have been described in ecology or in cognitive sciences, can also be found in medicine and I discuss the relationships between mechanistic and topological explanations in medicine, through the example of network medicine and medical genetics. Network medicine is a recent discipline that relies on the analysis of various (...) disease networks in order to find organizing principles in disease explanation. My aim is to show how topological explanations in network medicine can help solving the conceptual issues that pure mechanistic explanations of the genetics of disease are currently facing, namely the crisis of the concept of genetic disease, the progressive geneticization of diseases and the dissolution of the distinction between monogenic and polygenic diseases. However, I will also argue that topological explanations should not be considered as independent and radically different from mechanistic explanations for at least two reasons. First, in network medicine, topological explanations depend on and use mechanistic information. Second, they leave out some missing gaps in disease explanation that require, in turn, the development of new mechanistic explanations. Finally, I will insist on the specific contribution of topological explanations in medicine: they push us to develop an explanation of disease in general, instead of focusing on single explanations of individual diseases. This last point may have major consequences for biomedical research. (shrink)

Geologists, Paleontologists and other historical scientists are frequently concerned with narrative explanations targeting single cases. I show that two distinct explanatory strategies are employed in narratives, simple and complex. A simple narrative has minimal causal detail and is embedded in a regularity, whereas a complex narrative is more detailed and not embedded. The distinction is illustrated through two case studies: the ‘snowball earth’ explanation of Neoproterozoic glaciation and recent attempts to explain gigantism in Sauropods. This distinction is revelatory of historical (...) science. I argue that at least sometimes which strategy is appropriate is not a pragmatic issue, but turns on the nature of the target. Moreover, the distinction reveals a counterintuitive pattern of progress in some historical explanation: shifting from simple to complex. Sometimes, historical scientists rightly abandon simple, unified explanations in favour of disunified, complex narratives. Finally I compare narrative and mechanistic explanation, arguing that mechanistic approaches are inappropriate for complex narrative explanations. (shrink)

Species are considered the basic unit of biological classification and evolution. Hence, they are used as a benchmark in several fields, although the ontological status of such a category has always been a matter of debate. This paper aims to discuss the problem of the definition of species within the new mechanistic approach. Nevertheless, the boundary between entities, activities, and mechanisms remains difficult to establish and always requires an analysis of what is meant by explanation. As a case study, the (...) paper describes the debate concerning the species category by considering different kingdoms: Animals, Bacteria, and Fungi. The inherently biological differences between those groups prevent the use of a single, universally applicable concept of species that could fit the mechanisms responsible for the variability present in these kingdoms. The same issue is encountered within each group, as highlighted through a focus on mammals and microbes. This controversy has given rise to opposite approaches, namely: monism, which looks for a single definition that might account for all species, and pluralism, which admits that different groups of organisms require somewhat different definitions. In order to develop an adequate definition of species, we propose to apply a new mechanistic framework, which considers the ontic-epistemic dimensions of scientific explanation in close parallel. The apt correlation between epistemic and the ontic aspects highlights the way in which the concept of species and the reference to data are strictly co-determined. This suggests that the concept of species is better understandable within a dual ontic-epistemic approach. (shrink)

Mechanisms consist of component parts and processes organized in a specific way to produce changes that may give rise to one or more phenomena. I aim to examine the generative mechanism of generative material models in the production of new material models. A generative material model in biology is a living material model that is capable of generating new material models. I contend that generative mechanisms of a generative material model are not to be conflated with biological mechanisms: the former (...) is a mechanism manipulated and altered by the modeler while the latter is a naturally occurring mechanism. I examine the ways in which idealization may contribute to the generative mechanism in generating new model organisms. I articulate that the component parts and activities in a generative mechanism are to a large extent shaped by idealization in generative material modeling. Although biological mechanisms do have a role to play in the generation of model organisms, I propose that we shall view the generative mechanism as a hybrid of biological mechanisms and modeling activities. I shall argue that the notion of generative mechanisms as hybrid mechanisms is different from the extant conceptions of mechanisms in various aspects. (shrink)

This paper derives from a broader project dealing with the notion of causal order. I use this term to signify two kinds of parts-whole dependence: Orderly systems have rich, decomposable, internal structure; specifically, parts play differential roles, and interactions are primarily local. Disorderly systems, in contrast, have a homogeneous internal structure, such that differences among parts and organizational features are less important. Orderliness, I suggest, marks one key difference between individuals and collectives. My focus here will be the connection between (...) order and robustness, i.e. functional resilience in the face of internal or environmental perturbations. I distinguish three varieties of robustness. Ordered robustness is grounded in the system’s specific organizational pattern. In contrast, disorderly robustness stems from the aggregate outcome of many similar parts. In between, we find semi-ordered robustness, wherein a messy ensemble of elements is subjected to a selection or stabilization mechanism. I give brief characterizations of each category, discuss examples and remark on the connection between the order/disorder axis and the notions of individual versus collective. (shrink)

The experimental interventions that provide evidence of causal relations are notably similar to those that provide evidence of constitutive relevance relations. In the first two sections, I show that this similarity creates a tension: there is an inconsistent triad between Woodward’s popular interventionist theory of causation, Craver’s mutual manipulability account of constitutive relevance in mechanisms, and a variety of arguments for the incoherence of inter-level causation. I argue for an interpretation of the views in which the tension is merely apparent. (...) I propose to explain inter-level relations without inter-level causation by appealing to the notion of fat-handed interventions, and an argument against inter-level causation which dissolves the problem. (shrink)

Abstraction and idealization are the two notions that are most often discussed in the context of assumptions employed in the process of model building. These notions are also routinely used in philosophical debates such as that on the mechanistic account of explanation. Indeed, an objection to the mechanistic account has recently been formulated precisely on these grounds: mechanists cannot account for the common practice of idealizing difference-making factors in models in molecular biology. In this paper I revisit the debate and (...) I argue that the objection does not stand up to scrutiny. This is because it is riddled with a number of conceptual inconsistencies. By attempting to resolve the tensions, I also draw several general lessons regarding the difficulties of applying abstraction and idealization in scientific practice. Finally, I argue that more care is needed only when speaking of abstraction and idealization in a context in which these concepts play an important role in an argument, such as that on mechanistic explanation. (shrink)

In this thesis I investigate causal inquiry in the social sciences, drawing on examples from various disciplines and in particular from conflict studies. In a backlash against the pervasiveness of statistical methods, in the last decade certain social scientists have focused on finding the causal mechanisms behind observed correlations. To provide evidence for such mechanisms, researchers increasingly rely on ‘process tracing’, a method which attempts to give evidence for causal relations by specifying the chain of events connecting a putative cause (...) and effect of interest. I will ask whether the causal claims process tracers make are defensible, and where they are not defensible I will ask how we can improve the method. Throughout these investigations, I show that the conclusions of process tracing are constrained both by the causal structure ofthe social world and by social scientists’ aims and values. My central argument is this: all instances of social phenomena have causally relevant differences, which implies that any research design that requires some comparison between cases is limited by how we systematize these phenomena. Moreover, such research cannot rely on stable regularities. Nevertheless, to forego causal conclusions altogether is not the right response to these limitations; by carefully outlining our epistemic assumptions we can make progress in causal inquiry. While I use philosophical theories of causation to comment on the feasibility of a social scientific method, I also do the reverse: by investigating a popular contemporary method in the social sciences, I show to what extent our philosophical theories of causation are workable in practice. Thus, this thesis is both a methodological and a philosophical work. Every chapter discusses both a fundamental philosophical position on the social sciences and a relevant case study from the social sciences. (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)

Cognitive science has been beset for thirty years by foundational disputes about the nature and extension of cognition—e.g. whether cognition is necessarily representational, whether cognitive processes extend outside the brain or body, and whether plants or microbes have them. Whereas previous philosophical work aimed to settle these disputes, I aim to understand what conception of cognition scientists could share given that they disagree so fundamentally. To this end, I develop a number of variations on traditional conceptual explication, and defend a (...) novel explication of cognition called the sensitive management hypothesis. -/- Since expert judgments about the extension of “cognition” vary so much, I argue that there is value in explication that accurately models the variance in judgments rather than taking sides or treating that variance as noise. I say of explications that accomplish this that they are ecumenically extensionally adequate. Thus, rather than adjudicating whether, say, plants can have cognitive processes like humans, an ecumenically adequate explication should classify these cases differently: human cognitive processes as paradigmatically cognitive, and plant processes as controversially cognitive. -/- I achieve ecumenical adequacy by articulating conceptual explications with parameters, or terms that can be assigned a number of distinct interpretations based on the background commitments of participants in a discourse. For example, an explication might require that cognition cause “behavior,” and imply that plant processes are cognitive or not depending on whether anything plants do can be considered “behavior.” Parameterization provides a unified treatment of embattled concepts by isolating topics of disagreement in a small number of parameters. -/- I incorporate these innovations into an account on which cognition is the “sensitive management of organismal behavior.” The sensitive management hypothesis is ecumenically extensionally adequate, accurately classifying a broad variety of cases as paradigmatically or controversially cognitive phenomena. I also describe an extremely permissive version of the sensitive management hypothesis, arguing that it has the potential to explain several features of cognitive scientific discourse, including various facts about the way cognitive scientists ascribe representations to cognitive systems. (shrink)

Evolutionary systems biology is an emerging hybrid approach that integrates methods, models, and data from evolutionary and systems biology. Drawing on themes that arose at a cross-disciplinary meeting on ESB in 2013, we discuss in detail some of the explanatory friction that arises in the interaction between evolutionary and systems biology. These tensions appear because of different modeling approaches, diverse explanatory aims and strategies, and divergent views about the scope of the evolutionary synthesis. We locate these discussions in the context (...) of long-running philosophical deliberations on explanation, modeling, and theoretical synthesis. We show how many of the issues central to ESB’s progress can be understood as general philosophical problems. The benefits of addressing these philosophical issues feed back into philosophy too, because ESB provides excellent examples of scientific practice for the development of philosophy of science and philosophy of biology. (shrink)

I examine a major objection to the mechanistic view of concrete computation, stemming from an apparent tension between the abstract nature of computational explanation and the tenets of the mechanistic framework: while computational explanation is medium-independent, the mechanistic framework insists on the importance of providing some degree of structural detail about the systems target of the explanation. I show that a common reply to the objection, i.e. that mechanistic explanation of computational systems involves only weak structural constraints, is not enough (...) to save the standard mechanistic view of computation—it trivialises the appeal to mechanism, and thus makes the account collapse into a purely functional view. I claim, however, that the objection can be put to rest once the account is appropriately amended: computational individuation is indeed functional, while mechanistic explanation plays a role in accounting for computational implementation. Since individuation and implementation are crucial elements in a satisfying account of computation in physical systems, mechanism keeps its central importance in the theory of concrete computation. Finally, I argue that my version of the mechanistic view helps to provide a convincing reply to a powerful objection against non-semantic theories of concrete computation: the argument from the multiplicity of computations. (shrink)

In addition to theorizing about the role and value of mechanisms in scientific explanation or the causal structure of the world, there is a fundamental task of getting straight what a ‘mechanism’ is in the first place. Broadly, this paper is about the challenge of application: the challenge of aligning one's philosophical account of a scientific concept with the manner in which that concept is actually used in scientific practice. This paper considers a case study of the challenge of application (...) as it pertains to the concept of a mechanism: the debate about whether natural selection is a mechanism. By making clear what is and is not at stake in this debate, this paper considers various strategies for dealing with the challenge of application and makes a case for definitional pluralism about mechanism concepts. (shrink)

A strong case has been made for the role and value of mechanistic explanation in neuroscience and molecular biology. A similar demonstration in other domains of scientific investigation, however, remains an important challenge of scope for the new mechanists. This article helps answer that challenge by demonstrating one valuable role mechanisms play in phylogenetics. Using the transition/transversion rate parameter as a case example, this article argues that models embedded with mechanisms produce stronger phylogenetic tree hypotheses, as measured by maximum likelihood (...) logL values. Two important implications for the new mechanistic account of explanation are considered. (shrink)

A widespread and influential characterization of synthetic biology emphasizes that synthetic biology is the application of engineering principles to living systems. Furthermore, there is a strong tendency to express the engineering approach to organisms in terms of what seems to be an ontological claim: organisms are machines. In the paper I investigate the ontological and heuristic significance of the machine analogy in synthetic biology. I argue that the use of the machine analogy and the aim of producing rationally designed organisms (...) does not necessarily imply a commitment to mechanical biology. The ideal of applying engineering principles to biology is best understood as expressing recognition of the machine-unlikeness of natural organisms and the limits of human cognition. The paper suggests an interpretation of the identification of organisms with machines in synthetic biology according to which it expresses a strategy for representing, understanding, and constructing living systems that are more machine-like than natural organisms. (shrink)

Much of the recent philosophical debate on causation and causal explanation in the biological and biomedical sciences has focused on the notion of mechanism. Mechanisms, their nature and epistemic roles have been tackled by a range of so-called neo-mechanistic theories, and widely discussed. Without denying the merits of this approach, our paper aims to show how lately it has failed to give proper credit to processes, which are central to the field, especially of contemporary molecular biology. Processes can be summed (...) up in the notion of ‘pathway’, which is far from being just equivalent to that of ‘mechanism’ and has a profound epistemological and explanatory relevance. It is argued that an adequate consideration of pathways impels some rethinking of scientific explanation in molecular biology, namely its functional and contextual features. A number of examples are given to suggest that the focus of philosophical attention in this disciplinary field should shift from the notion of mechanism to the notion of pathway. (shrink)

Recently, Gualtiero Piccinini and Carl Craver have argued that functional analyses in psychology lack explanatory autonomy from explanations in neuroscience. In this thesis I argue against this claim by motivating and defending a pragmatic-epistemic conception of autonomous psychological explanation. I argue that this conception of autonomy need not require that functional analyses be distinct in kind from neural-mechanistic explanations. I use the framework of Bas van Fraassen’s Pragmatic Theory of Explanation to show that explanations in psychology and neuroscience can be (...) seen as seeking understanding of autonomous levels of mechanistic phenomena. (shrink)

In this paper, we critically analyze the mechanicist approach to scientific explanation and propose to complement this view with a conception of level as interval of quasi-decompositionality. This conception allows us to metaphysically base the mechanicist proposals on characteristic systemic features. By means of concrete examples, we claim that this proposal is able to overcome some of the alleged limitations of predominant models of mechanistic explanation.