We lay groundwork for applying ethnographic methods in philosophy of science. We frame our analysis in terms of two tasks: to identify the benefits of an ethnographic approach in philosophy of science and to structure an ethnographic approach for philosophical investigation best adapted to provide information relevant to philosophical interests and epistemic values. To this end, we advocate for a purpose-guided form of cognitive ethnography that mediates between the explanatory and normative interests of philosophy of science, while maintaining openness and (...) independence when framing such an investigation to achieve robust unbiased results. (shrink)

Evidence-Based Medicine has little consideration for mechanisms and philosophers of science and medicine have recently made pleas to increase the place of mechanisms in the medical evidence hierarchy. However, in this debate the notions of mechanisms seem to be limited to 'mechanistic processes' and 'complex-systems mechanisms,' understood as 'componential causal systems'. I believe that this will not do full justice to how mechanisms are used in biological, psychological and social sciences and, consequently, in a more biopsychosocial approach to medicine. Here, (...) I propose, following (Kuorikoski, 2009), to pay more attention to 'abstract forms of interaction' mechanisms. The present work scrutinized review articles on depression and medically unexplained pain, which are considered to be of multifactorial pathogenesis, for their use of mechanisms. In review articles on these disorders there seemed to be a range of uses between more 'abstract forms of interaction' and 'componential causal system' mechanisms. I therefore propose to expand the notions of mechanisms considered in medicine to include that of more 'abstract forms of interaction' to better explain and manage biopsychosocial disorders. (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)

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)

Theory of illness causation is an important issue in all biomedical sciences, and solid etiological explanations are needed in order to develop therapeutic approaches in medicine and preventive interventions in public health. Until now, the literature about the theoretical underpinnings of illness causation research has been scarce and fragmented, and lacking a convenient summary. This interdisciplinary book provides a convenient and accessible distillation of the current status of research into this developing field, and adds a personal flavor to the discussion (...) by proposing the etiological stance as a comprehensive approach to identify modifiable causes of illness. (shrink)

This book discusses the two main construals of the explanatory goals of semantic theories. The first, externalist conception, understands semantic theories in terms of a hermeneutic and interpretive explanatory project. The second, internalist conception, understands semantic theories in terms of the psychological mechanisms in virtue of which meanings are generated. It is argued that a fruitful scientific explanation is one that aims to uncover the underlying mechanisms in virtue of which the observable phenomena are made possible, and that a scientific (...) semantics should be doing just that. If this is the case, then a scientific semantics is unlikely to be externalist, for reasons having to do with the subject matter and form of externalist theories. It is argued that semantics construed hermeneutically is nevertheless a valuable explanatory project. (shrink)

This volume argues for a new image of science that understands both natural and social phenomena to be the product of mechanisms, casting the work of science as an effort to understand those mechanisms. Glennan offers an account of the nature of mechanisms and of the models used to represent them in physical, life, and social sciences.

This paper explores the question of whether all or most explanations in biology are, or ideally should be, ‘mechanistic’. I begin by providing an account of mechanistic explanation, making use of the interventionist ideas about causation I have developed elsewhere. This account emphasizes the way in which mechanistic explanations, at least in the biological sciences, integrate difference‐making and spatio‐temporal information, and exhibit what I call fine‐tunedness of organization. I also emphasize the role played by modularity conditions in mechanistic explanation. I (...) will then argue, in agreement with John Dupré, that, given this account, it is plausible that many biological systems require explanations that are relatively non‐mechanical or depart from expectations one associates with the behaviour of machines. (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)

When does a developing human being acquire moral status? I outline three different positions based on substance ontology that attempt to solve the question by locating some morally salient event in the process of human development question. In the second section, I consider some specific empirical objections to one of these positions, refute them, and then show how similar objections and responses would generalize to the other substance-based positions on the question. The crucial finding is that all the attempts to (...) answer the question that involve substance ontology need to appeal to dispositions. (shrink)

Since cognitive neuroscience aims at giving an integrated account of mind and brain, its ontology should include both neural and cognitive entities and specify their relations. According to what we call the standard ontological framework of cognitive neuroscience, the aim of cognitive neuroscience should be to establish one-to-one mappings between neural and cognitive entities. Where such entities do not yet closely align, this can be achieved by reforming the cognitive ontology, the neural ontology, or both. In order to assess the (...) limits and the possibilities of the SOFCN, we will examine a paradigmatic case study: the concept of Broca’s area, which indicates an alleged mapping between the left inferofrontal gyrus and the production of language. We review evidence showing that such a mapping does not hold, thus calling into question either the status of Broca’s area or the validity of the SOFCN. We then propose some strategies for addressing the issue and suggest that it may be solved within the SOFCN by adopting both of the following strategies: first, more accurately defining the relevant neural structures and second, switching the focus of neural ontology from structures to events, individuated by a where conjoint with a how. (shrink)

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given (...) that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description—four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity, to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene–environment interactions. We argue that gene–behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description. (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)

A tension exists between those who do—e.g. Meyer (The British Journal for the Philosophy of Science 71:959–985, 2020 ) and Chemero ( 2011 )—and those who do not—e.g. Kaplan and Craver (Philosophy of Science 78:601–627, 2011 ) Piccinini and Craver (Synthese 183:283–311, 2011 )—afford nonmechanistic explanations a role in cognitive science. Here, I argue that one’s perspective on this matter will cohere with one’s interpretation of the tasks of cognitive science; that is, of the actions for which cognitive scientists are (...) specially fitted. To make this point concrete, I consider two tasks of cognitive science—establishing causal claims and unifying—and show how these tasks are interpreted differently by mechanists and nonmechanists respectively. I then argue that, as a result of these different interpretations, we cannot expect to resolve the tension between mechanists and nonmechanists by appeal to cognitive scientific practice. I conclude that we should give up on the debate between mechanists and nonmechanists, and search for progress elsewhere. (shrink)

Some philosophers argue that we should eschew cross-explanatory integrations of mechanistic, dynamicist, and psychological explanations in cognitive science, because, unlike integrations of mechanistic explanations, they do not deliver genuine, cognitive scientific explanations. Here I challenge this claim by comparing the theoretical virtues of both kinds of explanatory integrations. I first identify two theoretical virtues of integrations of mechanistic explanations—unification and greater qualitative parsimony—and argue that no cross-explanatory integration could have such virtues. However, I go on to argue that this is (...) only a problem for those who think that cognitive science aims to specify one fundamental structure responsible for cognition. For those who do not, cross-explanatory integration will have at least two theoretical virtues to a greater extent than integrations of mechanistic explanations: explanatory depth and applicability. I conclude that one’s views about explanatory integration in cognitive science cannot be segregated from one’s views about the explanatory task of cognitive science. (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)

Several articles have recently appeared arguing that there really are no viable alternatives to mechanistic explanation in the biological sciences (Kaplan and Bechtel; Kaplan and Craver). We argue that mechanistic explanation is defined by localization and decomposition. We argue further that systems neuroscience contains explanations that violate both localization and decomposition. We conclude that the mechanistic model of explanation needs to either stretch to now include explanations wherein localization or decomposition fail or acknowledge that there are counterexamples to mechanistic explanation (...) in the biological sciences. (shrink)

This paper proposes an integrated-structure notion of interlevel emergence, from a dynamic relational ontological perspective. First, I will argue that only the individualist essentialism of atomistic metaphysics can block the possibility of interlevel emergence. Then I will show that we can make sense of emergence by recognizing the formation of structures of transformative and interdependent causal relations in the generation and development of a particular class of mereological complexes called integrated systems. Finally, I shall argue that even though the emergent (...) structural attributes of such systems are not micro-determined nor micro-reducible, they can still be accounted for by an interlevel integrative neo-mechanistic form of explanation. (shrink)

In the last two decades few topics in philosophy of science have received as much attention as mechanistic explanation. A significant motivation for these accounts is that scientists frequently use the term “mechanism” in their explanations of biological phenomena. While scientists appeal to a variety of causal concepts in their explanations, many philosophers argue or assume that all of these concepts are well understood with the single notion of mechanism. This reveals a significant problem with mainstream mechanistic accounts– although philosophers (...) use the term “mechanism” interchangeably with other causal concepts, this is not something that scientists always do. This paper analyses two causal concepts in biology–the notions of “mechanism” and “pathway”–and how they figure in biological explanation. I argue that these concepts have unique features, that they are associated with distinct strategies of causal investigation, and that they figure in importantly different types of explanation. (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)

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

In a recent article, Erasmus, Brunet, and Fisher (2021) argue that Artificial Neural Networks (ANNs) are explainable. They survey four influential accounts of explanation: the Deductive-Nomological model, the Inductive-Statistical model, the Causal-Mechanical model, and the New-Mechanist model. They argue that, on each of these accounts, the features that make something an explanation is invariant with regard to the complexity of the explanans and the explanandum. Therefore, they conclude, the complexity of ANNs (and other Machine Learning models) does not make them (...) less explainable. In this reply, it is argued that Erasmus et al. left out one influential account of explanation from their discussion: the Unificationist model. It is argued that, on the Unificationist model, the features that makes something an explanation is sensitive to complexity. Therefore, on the Unificationist model, ANNs (and other Machine Learning models) are not explainable. It is emphasized that Erasmus et al.’s general strategy is correct. The literature on explainable Artificial Intelligence can benefit by drawing from philosophical accounts of explanation. However, philosophical accounts of explanation do not settle the problem of whether ANNs are explainable because they do not unanimously declare that explanation is invariant with regard to complexity. (shrink)

In this paper we defend structural representations, more specifically neural structural representation. We are not alone in this, many are currently engaged in this endeavor. The direction we take, however, diverges from the main road, a road paved by the mathematical theory of measure that, in the 1970s, established homomorphism as the way to map empirical domains of things in the world to the codomain of numbers. By adopting the mind as codomain, this mapping became a boon for all those (...) convinced that a representation system should bear similarities with what was being represented, but struggled to find a precise account of what such similarities mean. The euforia was brief, however, and soon homomorphism revealed itself to be affected by serious weaknesses, the primary one being that it included systems embarrassingly alien to representations. We find that the defense attempts that have followed, adopt strategies that share a common format: valid structural representations come as “homomorphism plus X”, with various “X”, provided in descriptive format only. Our alternative direction stems from the observation of the overlooked departure from homomorphism as used in the theory of measure and its later use in mental representations. In the former case, the codomain or the realm of numbers, is the most suited for developing theorems detailing the existence and uniqueness of homomorphism for a wide range of empirical domains. In the latter case, the codomain is the realm of the mind, possibly more vague and more ill-defined than the empirical domain itself. The time is ripe for articulating the mapping between represented domains and the mind in formal terms, by exploiting what is currently known about coding mechanisms in the brain. We provide a sketch of a possible development in this direction, one that adopts the theory of neural population coding as codomain. We will show that our framework is not only not in disagreement with the “plus X” proposals, but can lead to natural derivation of several of the “X”. (shrink)

Causal approaches to explanation often assume that a model explains by describing features that make a difference regarding the phenomenon. Chirimuuta claims that this idea can be also used to understand non-causal explanation in computational neuroscience. She argues that mathematical principles that figure in efficient coding explanations are non-causal difference-makers. Although these principles cannot be causally altered, efficient coding models can be used to show how would the phenomenon change if the principles were modified in counterpossible situations. The problem is (...) that efficient coding models also involve difference-makers that, prima facie, cannot be characterized as non-causal in this sense. Mathematical principles always involve variables which have counterfactual relations between them. However, we cannot simply assume that these difference-makers are causal. They can also be found in paradigmatic non-causal explanations and therefore they must be characterized as non-causal in some sense. I argue that, despite appearances, Chirimuuta's view can be applied to these cases. The mentioned counterfactual relations presuppose the counterpossible conditionals that describe the modification of a relevant mathematical principle. If these conditionals are the hallmark of non-causal relations, then Chirimuuta’s criterion has the desired implication that variables in mathematical principles are non-causal difference-makers. (shrink)

Multilevel research strategies characterize contemporary molecular inquiry into biological systems. We outline conceptual, methodological, and explanatory dimensions of these multilevel strategies in microbial ecology, systems biology, protein research, and developmental biology. This review of emerging lines of inquiry in these fields suggests that multilevel research in molecular life sciences has significant implications for philosophical understandings of explanation, modeling, and representation.

We situate the debate on intentionality within the rise of cognitive neuroscience and argue that cognitive neuroscience can explain intentionality. We discuss the explanatory significance of ascribing intentionality to representations. At first, we focus on views that attempt to render such ascriptions naturalistic by construing them in a deflationary or merely pragmatic way. We then contrast these views with staunchly realist views that attempt to naturalize intentionality by developing theories of content for representations in terms of information and biological function. (...) We echo several other philosophers by arguing that these theories over-generalize unless they are constrained by a theory of the functional role of representational vehicles. This leads to a discussion of the functional roles of representations, and how representations might be realized in the brain. We argue that there’s work to be done to identify a distinctively mental kind of representation. We close by sketching a way forward for the project of naturalizing intentionality. This will not be achieved simply by ascribing the content of mental states to generic neural representations, but by identifying specific neural representations that explain the puzzling intentional properties of mental states. (shrink)

The primary challenge for generating a useful scientific definition of life comes from competing concepts of biological activity and our failure to make them explicit in our models. I set forth a three-part scheme for characterizing definitions of life, identifying a binary , a range , and a preference . The three components together form a proper activity in biology . To be clear, I am not proposing that proper activity be adopted as the best definition of life or even (...) as a desirable definition for life. Instead, I am arguing that some notion of proper activity already exists within common scientific definitions. By making the implicit elements explicit, the notion can be analyzed to see whether it is useful and appropriate in the context of the biological sciences. (shrink)

This article demonstrates that non-mechanistic, dynamical explanations are a viable approach to explanation in the special sciences. The claim that dynamical models can be explanatory without reference to mechanisms has previously been met with three lines of criticism from mechanists: the causal relevance concern, the genuine laws concern, and the charge of predictivism. I argue, however, that these mechanist criticisms fail to defeat non-mechanistic, dynamical explanation. Using the examples of Haken et al.’s model of bimanual coordination, and Thelen et al.’s (...) dynamical field model of infant perseverative reaching, I show how each mechanist criticism fails once the standards of Woodward’s interventionist framework are applied to dynamical models. An even-handed application of Woodwardian interventionism reveals that dynamical models are capable of producing genuine explanations without appealing to underlying mechanistic details. 1Introduction2Interventionism and Mechanistic Explanation 2.1Causal relevance and ideal interventions2.2Invariance2.3Explanation3Covering-Laws and Dynamical Explanation 3.1Dynamical models3.2Covering-law explanation3.3Prediction4Causal Relevance 4.1The causal relevance concern4.2Intervening on dynamical models4.3Test case I: The Haken–Kelso–Bunz model4.4Test case II: Dynamical field model5Genuine Laws 5.1The genuine laws concern5.2Using invariance in place of laws5.3Test case I: The Haken–Kelso–Bunz model5.4Test case II: Dynamical field model6Prediction 6.1Predictivism6.2Crude and invariant prediction7Interventionist Criticism of the Haken–Kelso–Bunz Model8Dynamical Explanation8Conclusion. (shrink)

In the 1950s and 1960s, the search for the mechanism of oxidative phosphorylation by biochemists paralleled the description of mitochondrial form by George Palade and Fritiof Sjöstrand using electron microscopy. This paper explores the extent to which biochemists studying oxidative phosphorylation took mitochondrial form into account in the formulation of hypotheses, design of experiments, and interpretation of results. By examining experimental approaches employed by the biochemists studying oxidative phosphorylation, and their interactions with Palade, I suggest that use of mitochondrial form (...) as a guide to experimentation and interpretation varied considerably among investigators. Most notably, Peter Mitchell, whose chemiosmotic hypothesis was ultimately the basis of the correct mechanism of oxidative phosphorylation, incorporated crucial aspects of mitochondrial form into his model that others failed to recognize. I discuss these historical observations in terms of the background and training of the biochemists, as well as a proposed heuristic of form, whose use may increase the possibility that biologically meaningful molecular mechanisms will be discovered. (shrink)

It is argued that once biological systems reach a certain level of complexity, mechanistic explanations provide an inadequate account of many relevant phenomena. In this article, I evaluate such claims with respect to a representative programme in systems biological research: the study of regulatory networks within single-celled organisms. I argue that these networks are amenable to mechanistic philosophy without need to appeal to some alternate form of explanation. In particular, I claim that we can understand the mathematical modelling techniques of (...) systems biologists as part of a broader practice of constructing and evaluating mechanism schemas. This argument is elaborated by considering the case of bacterial chemotactic networks, where some research has been interpreted as explaining phenomena by means of abstract design principles. (shrink)

Causal relations among components and activities are intentionally misrepresented in mechanistic explanations found routinely across the life sciences. Since several mechanists explicitly advocate accurately representing factors that make a difference to the outcome, these idealizations conflict with the stated rationale for mechanistic explanation. We argue that these idealizations signal an overlooked feature of reasoning in molecular and cell biology—mechanistic explanations do not occur in isolation—and suggest that explanatory practices within the mechanistic tradition share commonalities with model-based approaches prevalent in population (...) biology. (shrink)

Scientific explanation is a perennial topic in philosophy of science, but the literature has fragmented into specialized discussions in different scientific disciplines. An increasing attention to scientific practice by philosophers is (in part) responsible for this fragmentation and has put pressure on criteria of adequacy for philosophical accounts of explanation, usually demanding some form of pluralism. This commentary examines the arguments offered by Fagan and Woody with respect to explanation and understanding in scientific practice. I begin by scrutinizing Fagan's concept (...) of collaborative explanation, highlighting its distinctive advantages and expressing concern about several of its assumptions. Then I analyze Woody's attempt to reorient discussions of scientific explanation around functional considerations, elaborating on the wider implications of this methodological recommendation. I conclude with reflections on synergies and tensions that emerge when the two papers are juxtaposed and how these draw attention to critical issues that confront ongoing philosophical analyses of scientific explanation. (shrink)

Evolution exploits the physics of non‐neural bioelectricity to implement anatomical homeostasis: a process in which embryonic patterning, remodeling, and regeneration achieve invariant anatomical outcomes despite external interventions. Linear “developmental pathways” are often inadequate explanations for dynamic large‐scale pattern regulation, even when they accurately capture relationships between molecular components. Biophysical and computational aspects of collective cell activity toward a target morphology reveal interesting aspects of causation in biology. This is critical not only for unraveling evolutionary and developmental events, but also for (...) the design of effective strategies for biomedical intervention. Bioelectrical controls of growth and form, including stochastic behavior in such circuits, highlight the need for the formulation of nuanced views of pathways, drivers of system‐level outcomes, and modularity, borrowing from concepts in related disciplines such as cybernetics, control theory, computational neuroscience, and information theory. This approach has numerous practical implications for basic research and for applications in regenerative medicine and synthetic bioengineering. (shrink)

What are scientific theories and how should they be represented? In this article, I propose a causal–structural account, according to which scientific theories are to be represented as sets of interrelated causal and credal nets. In contrast with other accounts of scientific theories (such as Sneedian structuralism, Kitcher’s unificationist view, and Darden’s theory of theoretical components), this leaves room for causality to play a substantial role. As a result, an interesting account of explanation is provided, which sheds light on explanatory (...) unification within a causalist framework. The theory of classical genetics is used as a case study. 1 Introduction2 The Theory of Classical Genetics3 Three Philosophical Accounts of the Theory of Classical Genetics3.1 The structuralist account3.2 Kitcher’s unificationism3.3 Darden and theory change in science4 A Common Lacuna: Where is Causality?5 Woodward’s Interventionist Account of Causation6 Causal Bayes Nets and Their Interrelations6.1 Causal Bayes nets6.2 Relations among causal nets6.3 Credal nets and their interrelations7 The Theory of the Gene and its Causal Graph8 A First Exemplar: Stem Length in Pea Plants8.1 Three crosses on stem length in pea plants8.2 The causal graph for stem length in pea plants8.3 Morgan’s explanatory principles and the credal net for stem length in pea plants9 Explaining Mendel’s Crosses: A Causal–Structural Account10 Monohybrid Crosses with Complete Dominance11 Exemplars,Explanatory Patterns, Generic Credal Nets, and Mechanism Schemas12 Incomplete Dominance13 Anomalies14 Multi-hybrid Crosses with Independent Assortment15 Multi-hybrid Crosses with Linkage and Crossing-Over16 Double Crossing-Over and the Linear Order of the Gene17 Causal–Structural Explanation18 Explanatory Unification19 Concluding Remarks. (shrink)

The picture of synthetic biology as a kind of engineering science has largely created the public understanding of this novel field, covering both its promises and risks. In this paper, we will argue that the actual situation is more nuanced and complex. Synthetic biology is a highly interdisciplinary field of research located at the interface of physics, chemistry, biology, and computational science. All of these fields provide concepts, metaphors, mathematical tools, and models, which are typically utilized by synthetic biologists by (...) drawing analogies between the different fields of inquiry. We will study analogical reasoning in synthetic biology through the emergence of the functional meaning of noise, which marks an important shift in how engineering concepts are employed in this field. The notion of noise serves also to highlight the differences between the two branches of synthetic biology: the basic science-oriented branch and the engineering-oriented branch, which differ from each other in the way they draw analogies to various other fields of study. Moreover, we show that fixing the mapping between a source domain and the target domain seems not to be the goal of analogical reasoning in actual scientific practice. (shrink)

Although the interdisciplinary nature of contemporary biological sciences has been addressed by philosophers, historians, and sociologists of science, the different ways in which engineering concepts and methods have been applied in biology have been somewhat neglected. We examine - using the mechanistic philosophy of science as an analytic springboard - the transfer of network methods from engineering to biology through the cases of two biology laboratories operating at the California Institute of Technology. The two laboratories study gene regulatory networks, but (...) in remarkably different ways. The research strategy of the Davidson lab fits squarely into the traditional mechanist philosophy in its aim to decompose and reconstruct, in detail, gene regulatory networks of a chosen model organism. In contrast, the Elowitz lab constructs minimal models that do not attempt to represent any particular naturally evolved genetic circuits. Instead, it studies the principles of gene regulation through a template-based approach that is applicable to any kinds of networks, whether biological or not. We call for the mechanists to consider whether the latter approach can be accommodated by the mechanistic approach, and what kinds of modifications it would imply for the mechanistic paradigm of explanation, if it were to address modelling more generally. (shrink)

Recently, Bechtel and Abrahamsen have argued that mathematical models study the dynamics of mechanisms by recomposing the components and their operations into an appropriately organized system. We will study this claim through the practice of combinational modeling in circadian clock research. In combinational modeling, experiments on model organisms and mathematical/computational models are combined with a new type of model—a synthetic model. We argue that the strategy of recomposition is more complicated than what Bechtel and Abrahamsen indicate. Moreover, synthetic modeling as (...) a kind of material recomposition strategy also points beyond the mechanistic paradigm. (shrink)

ABSTRACT Is there something specific about modelling that distinguishes it from many other theoretical endeavours? We consider Michael Weisberg’s thesis that modelling is a form of indirect representation through a close examination of the historical roots of the Lotka–Volterra model. While Weisberg discusses only Volterra’s work, we also study Lotka’s very different design of the Lotka–Volterra model. We will argue that while there are elements of indirect representation in both Volterra’s and Lotka’s modelling approaches, they are largely due to two (...) other features of contemporary model construction processes that Weisberg does not explicitly consider: the methods-drivenness and outcome-orientedness of modelling. 1Introduction 2Modelling as Indirect Representation 3The Design of the Lotka–Volterra Model by Volterra 3.1Volterra’s method of hypothesis 3.2The construction of the Lotka–Volterra model by Volterra 4The Design of the Lotka–Volterra Model by Lotka 4.1Physical biology according to Lotka 4.2Lotka’s systems approach and the Lotka–Volterra model 5Philosophical Discussion: Strategies and Tools of Modelling 5.1Volterra’s path from the method of isolation to the method of hypothesis 5.2The template-based approach of Lotka 5.3Modelling: methods-driven and outcome-oriented 6Conclusion. (shrink)

Synthetic biology is often understood in terms of the pursuit for well-characterized biological parts to create synthetic wholes. Accordingly, it has typically been conceived of as an engineering dominated and application oriented field. We argue that the relationship of synthetic biology to engineering is far more nuanced than that and involves a sophisticated epistemic dimension, as shown by the recent practice of synthetic modeling. Synthetic models are engineered genetic networks that are implanted in a natural cell environment. Their construction is (...) typically combined with experiments on model organisms as well as mathematical modeling and simulation. What is especially interesting about this combinational modeling practice is that, apart from greater integration between these different epistemic activities, it has also led to the questioning of some central assumptions and notions on which synthetic biology is based. As a result synthetic biology is in the process of becoming more “biology inspired.”. (shrink)

This paper criticizes the role of intuition-based ascriptions of cognition that are closely related to the ascription of mind. This practice hinders the explication of a clear and stable target domain for the cognitive sciences. To move forward, the proposal is to cut the notion of cognition free from such ascriptions and the intuition-based judgments that drive them. Instead, cognition is reinterpreted and developed as a scientific concept that is tied to a material domain of research. In this reading, cognition (...) becomes a changeable theoretical concept that can and must be adapted to the findings within this target domain. Taking humans as the best-established existing example of the relevant material target domain, this central case is extended to include all living systems. To clarify what it is about living systems that warrants their role as cognitive target domain, the new concept of cobolism is introduced as a complement to metabolism. Cobolism refers to the systematic ways in which each living system encompasses structures, processes and external events that maintain the fundamental metabolic processes that constitute the core of each living system. Cobolism is perfectly general, applies to bacterial and human cases alike, and provides a general format to describe wildly different cognitive organizations. It provides a clear target for the cognitive sciences to work on, turning them into what we can call the cognitive life sciences. (shrink)

Different why-questions emerge under different contexts and require different information in order to be addressed. Hence a relevance relation can hardly be invariant across contexts. However, what is indeed common under any possible context is that all explananda require scientific information in order to be explained. So no scientific information is in principle explanatorily irrelevant, it only becomes so under certain contexts. In view of this, scientific thought experiments can offer explanations, should we analyze their representational strategies. Their representations involve (...) empirical as well as hypothetical statements. I call this the “representational mingling” which bears scientific information that can explain events. Buchanan’s thought experiment from constitutional economics is examined to show how mingled representations explain. (shrink)

The central aim of this article is to specify the ontological nature of constitutive mechanistic phenomena. After identifying three criteria of adequacy that any plausible approach to constitutive mechanistic phenomena must satisfy, we present four different suggestions, found in the mechanistic literature, of what mechanistic phenomena might be. We argue that none of these suggestions meets the criteria of adequacy. According to our analysis, constitutive mechanistic phenomena are best understood as what we will call ‘object-involving occurrents’. Furthermore, on the basis (...) of this notion, we will clarify what distinguishes constitutive mechanistic explanations from etiological ones. 1 Introduction 2 Criteria of Adequacy 2.1 Descriptive adequacy 2.2 Constitutive–etiological distinction 2.3 Constitution 3 The Ontological Nature of Constitutive Mechanistic Phenomena 3.1 Phenomena as input–output relations 3.2 Phenomena as end states 3.3 Phenomena as dispositions 3.4 Phenomena as behaviours 4 Phenomena as Object-Involving Occurrents 4.1 What object-involving occurrents are and why we need them 4.2 The object in the phenomenon 4.3 The adequacy of option 5 Conclusion. (shrink)

This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and cognitive science. Clearly, (...) defences of non-causal explanation are far from new (e.g. Batterman, Br J Philos Sci 53:21–38, 2002a; The devil in the details: asymptotic reasoning in explanation, reduction, and emergence, 2002b; Pincock, Noûs 41:253–275, 2007; Mathematics and scientific representation 2012; Rice, Noûs. doi:10.1111/nous.12042, 2013; Biol Philos 27:685–703, 2012), so the targets here are focused on a particular type of robust phenomenon and how strong invariance to interventions can block a range of causal explanations. By focusing on a common form of model construction, the paper also ties functional or computational style explanations found in cognitive science and biology more firmly with explanatory practices across model-based science in general. (shrink)

Craver claims that mechanistic explanation is ontic, while Bechtel claims that it is epistemic. While this distinction between ontic and epistemic explanation originates with Salmon, the ideas have changed in the modern debate on mechanistic explanation, where the frame of the debate is changing. I will explore what Bechtel and Craver’s claims mean, and argue that good mechanistic explanations must satisfy both ontic and epistemic normative constraints on what is a good explanation. I will argue for ontic constraints by drawing (...) on Craver’s work in Sect. 2.1, and argue for epistemic constraints by drawing on Bechtel’s work in Sect. 2.2. Along the way, I will argue that Bechtel and Craver actually agree with this claim. I argue that we should not take either kind of constraints to be fundamental, in Sect. 3, and close in Sect. 4 by considering what remains at stake in making a distinction between ontic and epistemic constraints on mechanistic explanation. I suggest that we should not concentrate on either kind of constraint, to the neglect of the other, arguing for the importance of seeing the relationship as one of integration. (shrink)

In addition to considering sociocultural, political, economic, and ethical factors, effectively engaging socioscientific issues requires that students understand and apply scientific explanations and the nature of science. Promoting such understandings can be achieved through immersing students in authentic real-world contexts where the SSI impacts occur and teaching those students about how scientists comprehend, research, and debate those SSI. This triangulated mixed-methods investigation explored how 60 secondary students’ trophic cascade explanations changed through their experiencing place-based SSI instruction focused on the Yellowstone (...) wolf reintroduction, including scientists’ work and debates regarding that issue. Furthermore, this investigation determined the association between the students’ post place-based SSI instruction trophic cascade explanations and NOS views. Findings from this investigation demonstrate that through the place-based SSI instruction students’ trophic cascade explanations became significantly more accurate and complex and included more ecological causal mechanisms. Also, significant and moderate to moderately large correlations were found between the accuracy and contextualization of students’ post place-based SSI instruction NOS views and the complexity of their trophic cascade explanations. Empirical substantiation of the association between the complexity of students’ scientific explanations and their NOS views responds to an understudied area in the science education research. It also encourages the consideration of several implications, drawn from this investigation’s findings and others’ prior work, which include the need for NOS to be forefront alongside and in connection with science content in curricular standards and through instruction focused on relevant and authentic place-based SSI. (shrink)