Alfred North Whitehead's SCIENCE AND THE MODERN WORLD, originally published in 1925, redefines the concept of modern science.

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)

Hodgkin and Huxley’s model of the action potential is an apparent dream case of covering‐law explanation in biology. The model includes laws of physics and chemistry that, coupled with details about antecedent and background conditions, can be used to derive features of the action potential. Hodgkin and Huxley insist that their model is not an explanation. This suggests either that subsuming a phenomenon under physical laws is insufficient to explain it or that Hodgkin and Huxley were wrong. I defend Hodgkin (...) and Huxley against Weber’s heteronomy thesis and argue that explanations are descriptions of mechanisms. †To contact the author, please write to: Department of Philosophy, Philosophy‐Neuroscience‐Psychology Program, Washington University in St. Louis, One Brookings Drive, Wilson Hall, St. Louis, MO 63130; e‐mail: [email protected]. (shrink)

Glennan appeals to interventions to solve the ontological and explanatory regresses that threaten his mechanistic account of causality . I argue that Glennan’s manoeuvre fails. The appeal to interventions is not able to address the ontological regress, and it blocks the explanatory regress only at the cost of making the account inapplicable to non-modular mechanisms. I offer a solution to the explanatory regress that makes use of dynamic Bayesian networks. My argument is illustrated by a case study from systems biology, (...) namely, the mechanism for the irreversibility of apoptosis. I conclude by pointing out the implications of my argument for Glennan’s mechanistic account of causality and, more generally, for accounts of mechanistic explanation based on interventions. 1 Introduction2 Glennan’s Account of Causality3 Objections to Glennan’s Account4 Glennan’s Replies5 Ontological Symmetry?6 Explanatory Symmetry?7 A Solution to the Explanatory Regress8 The Prospects of Glennan’s Account. (shrink)

Recent work on the evolution of signaling systems provides a novel way of thinking about genetic information, where information is passed between genes in a regulatory network. I use examples from evolutionary developmental biology to show how information can be created in these networks and how it can be reused to produce rapid phenotypic change.

Essentialism is widely regarded as a mistaken view of biological kinds, such as species. After recounting why (sections 2-3), we provide a brief survey of the chief responses to the “death of essentialism” in the philosophy of biology (section 4). We then develop one of these responses, the claim that biological kinds are homeostatic property clusters (sections 5-6) illustrating this view with several novel examples (section 7). Although this view was first expressed 20 years ago, and has received recent discussion (...) and critique, it remains underdeveloped and is often misrepresented by its critics (section 8). (shrink)

By linking the concepts of homology and morphological organization to evolvability, this paper attempts to (1) bridge the gap between developmental and phylogenetic approaches to homology and to (2) show that developmental constraints and natural selection are compatible and in fact complementary. I conceive of a homologue as a unit of morphological evolvability, i.e., as a part of an organism that can exhibit heritable phenotypic variation independently of the organism’s other homologues. An account of homology therefore consists in explaining how (...) an organism’s developmental constitution results in different homologues/characters as units that can evolve independently of each other. The explanans of an account of homology is developmental, yet the very explanandum is an evolutionary phenomenon: evolvability in a character-by-character fashion, which manifests itself in phylogenetic patterns as recognized by phylogenetic approaches to homology. While developmental constraints and selection have often been viewed as antagonistic forces, I argue that both are complementary as they concern different parts of the evolutionary process. Developmental constraints, conceived of as the presence of the same set of homologues across phenotypic change, pertain to how heritable variation can be generated in the first place (evolvability), while natural selection operates subsequently on the produced variation. (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)

Despite the traditional focus on metaphysical issues in discussions of natural kinds in biology, epistemological considerations are at least as important. By revisiting the debate as to whether taxa are kinds or individuals, I argue that both accounts are metaphysically compatible, but that one or the other approach can be pragmatically preferable depending on the epistemic context. Recent objections against construing species as homeostatic property cluster kinds are also addressed. The second part of the paper broadens the perspective by considering (...) homologues as another example of natural kinds, comparing them with analogues as functionally defined kinds. Given that there are various types of natural kinds, I discuss the different theoretical purposes served by diverse kind concepts, suggesting that there is no clear-cut distinction between natural kinds and other kinds, such as functional kinds. Rather than attempting to offer a unique metaphysical account of ‘natural’ kind, a more fruitful approach consists in the epistemological study of how different natural kind concepts are employed in scientific reasoning. (shrink)

Biologists rely heavily on the language of information, coding, and transmission that is commonplace in the field of information theory developed by Claude Shannon, but there is open debate about whether such language is anything more than facile metaphor. Philosophers of biology have argued that when biologists talk about information in genes and in evolution, they are not talking about the sort of information that Shannon’s theory addresses. First, philosophers have suggested that Shannon’s theory is only useful for developing a (...) shallow notion of correlation, the so-called causal sense of information. Second, they typically argue that in genetics and evolutionary biology, information language is used in a semantic sense, whereas semantics are deliberately omitted from Shannon’s theory. Neither critique is well-founded. Here we propose an alternative to the causal and semantic senses of information: a transmission sense of information, in which an object X conveys information if the function of X is to reduce, by virtue of its sequence properties, uncertainty on the part of an agent who observes X. The transmission sense not only captures much of what biologists intend when they talk about information in genes, but also brings Shannon’s theory back to the fore. By taking the viewpoint of a communications engineer and focusing on the decision problem of how information is to be packaged for transport, this approach resolves several problems that have plagued the information concept in biology, and highlights a number of important features of the way that information is encoded, stored, and transmitted as genetic sequence. (shrink)

Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...) considerably richer than those suggested by a nomological framework. Second, the fact that mechanisms involve organized systems of component parts and operations provides direction to both the discovery and testing of mechanistic explanations. Finally, models of mechanisms are developed for specific exemplars and are not represented in terms of universally quantified statements. Generalization involves investigating both the similarity of new exemplars to those already studied and the variations between them. (shrink)

Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction with their (...) styles of modeling. In particular, mental operations often are conceptualized as comparable to the processes employed in classical symbolic AI or neural network models. These models, in turn, have been interpreted by some as themselves intelligent systems since they employ the same type of operations as does the mind. For this paper, what is significant about these approaches to modeling is that they are constructed specifically to account for behavior and are evaluated by how well they do so—not by independent evidence that they describe actual operations in mental mechanisms. (shrink)

Skipper and Millstein (2005) argued that existing conceptions of mechanisms failed to "get at" natural selection, but left open the possibility that a refined conception of mechanisms could resolve the problems that they identified. I respond to Skipper and Millstein, and argue that while many of their points have merit, their objections can be overcome and that natural selection can be characterized as a mechanism. In making this argument, I discuss the role of regularity in mechanisms, and develop an account (...) of stochastic (i.e., probabilistic) mechanisms. Explaining the phenomenon of adaptation through the mechanism of natural selection illustrates the power and flexibility of using mechanistic strategies to explain natural phenomena. (shrink)

An important strategy in the discovery of biological mechanisms involves the piecing together of experimental results from interventions. However, if mechanisms are investigated by means of ideal interventions, as defined by James Woodward and others, then the kind of information revealed is insufficient to discriminate between modular and non-modular causal contributions. Ideal interventions suffice for constructing webs of causal dependencies that can be used to make some predictions about experimental outcomes, but tell us little about how causally relevant factors are (...) organized together and how they interact with each other in order to produce a phenomenon. I argue that lab research relies on more elaborated types of interventions targeting in a controlled fashion multiple variables at the same time in order to probe the temporal organization of causally-relevant factors along distinct causal pathways and to test for non-modular interaction effects, thus providing crucial spatial-temporal constraints guiding the formulation of more detailed mechanistic explanations. (shrink)

According to the proponents of Developmental Systems Theory and the Causal Parity Thesis, the privileging of the genome as “first among equals” with respect to the development of phenotypic traits is more a reflection of our own heuristic prejudice than of ontology - the underlying causal structures responsible for that specified development no more single out the genome as primary than they do other broadly “environmental” factors. Parting with the methodology of the popular responses to the Thesis, this paper offers (...) a novel criterion for ‘causal primacy’, one that is grounded in the ontology of the unique causal role of dispositional properties. This paper argues that, if the genome is conceptualised as realising dispositional properties that are “directed toward” phenotypic traits, the parity of ‘causal roles’ between genetic and extra-genetic factors is no longer apparent, and further, that the causal primacy of the genome is both plausible and defensible. (shrink)

In the recent literature on causal and non-causal scientific explanations, there is an intuitive assumption according to which an explanation is non-causal by virtue of being abstract. In this context, to be ‘abstract’ means that the explanans in question leaves out many or almost all causal microphysical details of the target system. After motivating this assumption, we argue that the abstractness assumption, in placing the abstract and the causal character of an explanation in tension, is misguided in ways that are (...) independent of which view of causation or causal explanation one takes to be most accurate. On major accounts of causation, as well as on major accounts of causal explanation, the abstractness of an explanation is not sufficient for it being non-causal. That is, explanations are not non-causal by dint of being abstract. (shrink)

This chapter contains sections titled: Introduction A Brief History of Developmental Explanations of Phenotypic Evolution Research Questions of Evo‐Devo Unifying Themes of the Conceptual Basis of Evo‐Devo Conclusion: A Mechanistic Theory of Evo‐Devo Challenges the Modern Synthesis Postscript: Counterpoint References.

Our intuitive assumption that only organisms are the real individuals in the natural world is at odds with developments in cell biology, ecology, genetics, evolutionary biology, and other fields. Although organisms have served for centuries as nature’s paradigmatic individuals, science suggests that organisms are only one of the many ways in which the natural world could be organized. When living beings work together—as in ant colonies, beehives, and bacteria-metazoan symbiosis—new collective individuals can emerge. In this book, leading scholars consider the (...) biological and philosophical implications of the emergence of these new collective individuals from associations of living beings. The topics they consider range from metaphysical issues to biological research on natural selection, sociobiology, and symbiosis. -/- The contributors investigate individuality and its relationship to evolution and the specific concept of organism; the tension between group evolution and individual adaptation; and the structure of collective individuals and the extent to which they can be defined by the same concept of individuality. These new perspectives on evolved individuality should trigger important revisions to both philosophical and biological conceptions of the individual. -/- Contributors: Frédéric Bouchard, Ellen Clarke, Jennifer Fewell, Andrew Gardner, Peter Godfrey-Smith, Charles J. Goodnight, Matt Haber, Andrew Hamilton, Philippe Huneman, Samir Okasha, Thomas Pradeu, Scott Turner, Minus van Baalen. (shrink)

Talk of a “genetic program” has become almost as common in cell and evolutionary biology as talk of “genetic information”. But what is a genetic program? I understand the claim that an organism’s genome contains a program to mean that its genes not only carry information about which proteins to make, but also about the conditions in which to make them. I argue that the program description, while accurate in some respects, is ultimately misleading and should be abandoned. After that, (...) I sketch an alternative framework which is better suited to capturing the full informational nature of genes. This framework is centered on the notion of a signaling game, as originally developed by David Lewis, but expanded upon considerably by Brian Skyrms in more recent years. On the view I develop, genes turn out to be the producers and consumers of regulatory or developmental information, rather than entities encoding such information. This finding has consequences that link up with a broader debate in the philosophy of biology concerning inheritance systems. I take this to be one form of theoretical payoff that results from applying the signaling games framework to genes. (shrink)

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

The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view (a version of the epistemic conception) is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. (...) Professor Salmon's theory furnishes a robust argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the "reviewed view" of scientific explanation (that explanations are arguments) to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts. (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)

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)

The mathematical theory of communication.

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

This paper presents a counterfactual account of what a mechanism is. Mechanisms consist of parts, the behavior of which conforms to generalizations that are invariant under interventions, and which are modular in the sense that it is possible in principle to change the behavior of one part independently of the others. Each of these features can be captured by the truth of certain counterfactuals.

This paper presents a counterfactual account of what a mechanism is. Mechanisms consist of parts, the behavior of which conforms to generalizations that are invariant under interventions, and which are modular in the sense that it is possible in principle to change the behavior of one part independently of the others. Each of these features can be captured by the truth of certain counterfactuals.

Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.

This paper attempts to elucidate three characteristics of causal relationships that are important in biological contexts. Stability has to do with whether a causal relationship continues to hold under changes in background conditions. Proportionality has to do with whether changes in the state of the cause “line up” in the right way with changes in the state of the effect and with whether the cause and effect are characterized in a way that contains irrelevant detail. Specificity is connected both to (...) David Lewis’ notion of “influence” and also with the extent to which a causal relation approximates to the ideal of one cause–one effect. Interrelations among these notions and their possible biological significance are also discussed. (shrink)

I analyze the importance of parts in the style of biological theorizing that I call compositional biology. I do this by investigating various aspects, including partitioning frames and explanatory accounts, of the theoretical perspectives that fall under and are guided by compositional biology. I ground this general examination in a comparative analysis of three different disciplines with their associated compositional theoretical perspectives: comparative morphology, functional morphology, and developmental biology. I glean data for this analysis from canonical textbooks and defend the (...) use of such texts for the philosophy of science. I end with a discussion of the importance of recognizing formal and compositional biology as two genuinely different ways of doing biology – the differences arising more from their distinct methodologies than from scientific discipline included or natural domain studied. Ultimately, developing a translation manual between the two styles would be desirable as they currently are, at times, in conflict. (shrink)

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

Biologists studying complex causal systems typically identify some factors as causes and treat other factors as background conditions. For example, when geneticists explain biological phenomena, they often foreground genes and relegate the cellular milieu to the background. But factors in the milieu are as causally necessary as genes for the production of phenotypic traits, even traits at the molecular level such as amino acid sequences. Gene-centered biology has been criticized on the grounds that because there is parity among causes, the (...) “privileging” of genes reflects a reductionist bias, not an ontological difference. The idea that there is an ontological parity among causes is related to a philosophical puzzle identified by John Stuart Mill: what, other than our interests or biases, could possibly justify identifying some causes as the actual or operative ones, and other causes as mere background? The aim of this paper is to solve this conceptual puzzle and to explain why there is not an ontological parity among genes and the other factors. It turns out that solving this puzzle helps answer a seemingly unrelated philosophical question: what kind of causal generality matters in biology? (shrink)

An assessment is offered of the recent debate on information in the philosophy of biology, and an analysis is provided of the notion of information as applied in scientific practice in molecular genetics. In particular, this paper deals with the dependence of basic generalizations of molecular biology, above all the 'central dogma', on the socalled 'informational talk'. It is argued that talk of information in the 'central dogma' can be reduced to causal claims. In that respect, the primary aim of (...) the paper is to consider a solution to the major difficulty of the causal interpretation of genetic information: how to distinguish the privileged causal role assigned to nucleic acids, DNA in particular, in the processes of replication and protein production. A close reading is proposed of Francis H. C. Crick's On Protein Synthesis and related works, to which we owe the first explicit definition of information within the scientific practice of molecular biology. (shrink)

An assessment is offered of the recent debate on information in the philosophy of biology, and an analysis is provided of the notion of information as applied in scientific practice in molecular genetics. In particular, this paper deals with the dependence of basic generalizations of molecular biology, above all the ‘central dogma’, on the so-called ‘informational talk’ (Maynard Smith [2000a]). It is argued that talk of information in the ‘central dogma’ can be reduced to causal claims. In that respect, the (...) primary aim of the paper is to consider a solution to the major difficulty of the causal interpretation of genetic information: how to distinguish the privileged causal role assigned to nucleic acids, DNA in particular, in the processes of replication and protein production. A close reading is proposed of Francis H. C. Crick's On Protein Synthesis (1958) and related works, to which we owe the first explicit definition of information within the scientific practice of molecular biology. Introduction 1.1 The basic questions of the information debate 1.2 The causal interpretation (CI) of biological information and Crick's ‘central dogma’ Crick's definitions of genetic information The main requirement for (CI) Types of causation in molecular biology 4.1 Structural causation in molecular biology 4.2 Nucleic acids as correlative causal factors The ‘central dogma’ without the notion of information Concluding remarks This is a new version of this article as there were errors in the abstract and full text in the previous version. (shrink)

The concept of genetic information is controversial because it attributes semantic properties to what seem to be ordinary biochemical entities. I argue that nucleic acids contain information in a semantic sense, but only about a limited range of effects. In contrast to other recent proposals, however, I analyze genetic information not in terms of a naturalized account of biological functions, but instead in terms of the way in which molecules determine their products during processes known as template-directed syntheses. I argue (...) that determining an outcome in a certain way is constitutive for being an instruction. On this account, the content of genetic information is identified with the template's properties, which determine the product in the way constitutive for instructions. (shrink)

In this paper I develop three conceptions of the relationship between evolutionary and developmental biology. I further argue that: (a) the choice between them largely turns on as yet unresolved empirical considerations; (b) none of these conceptions demand a fundamental conceptual reevaluation of evolutionary biology; and (c) while developmental systems theorists have constructed an important and innovative alternative to the standard view of the genotype/phenotype relations, in considering the general issue of the relationship between evolutionary and developmental biology, we can (...) remain neutral on this debate. (shrink)

Ernst Mayr has argued that Darwinian theory discredited essentialist modes of thought and replaced them with what he has called "population thinking". In this paper, I characterize essentialism as embodying a certain conception of how variation in nature is to be explained, and show how this conception was undermined by evolutionary theory. The Darwinian doctrine of evolutionary gradualism makes it impossible to say exactly where one species ends and another begins; such line-drawing problems are often taken to be the decisive (...) reason for thinking that essentialism is untenable. However, according to the view of essentialism I suggest, this familiar objection is not fatal to essentialism. It is rather the essentialist's use of what I call the natural state model for explaining variation which clashes with evolutionary theory. This model implemented the essentialist's requirement that properties of populations be defined in terms of properties of member organisms. Requiring such constituent definitions is reductionistic in spirit; additionally, evolutionary theory shows that such definitions are not available, and, moreover, that they are not needed to legitimize population-level concepts. Population thinking involves the thesis that population concepts may be legitimized by showing their connections with each other, even when they are not reducible to concepts applying at lower levels of organization. In the paper, I develop these points by describing Aristotle's ideas on the origins of biological variation; they are a classic formulation of the natural state model. I also describe how the development of statistical ideas in the 19th century involved an abandoning of the natural state model. (shrink)

The use of informational terms is widespread in molecular and developmental biology. The usage dates back to Weismann. In both protein synthesis and in later development, genes are symbols, in that there is no necessary connection between their form (sequence) and their effects. The sequence of a gene has been determined, by past natural selection, because of the effects it produces. In biology, the use of informational terms implies intentionality, in that both the form of the signal, and the response (...) to it, have evolved by selection. Where an engineer sees design, a biologist sees natural selection. (shrink)