Stuart Kauffman here presents a brilliant new paradigm for evolutionary biology, one that extends the basic concepts of Darwinian evolution to accommodate recent findings and perspectives from the fields of biology, physics, chemistry and mathematics. The book drives to the heart of the exciting debate on the origins of life and maintenance of order in complex biological systems. It focuses on the concept of self-organization: the spontaneous emergence of order widely observed throughout nature. Kauffman here argues that self-organization plays an (...) important role in the emergence of life itself and may play as fundamental a role in shaping life's subsequent evolution as does the Darwinian process of natural selection. Yet until now no systematic effort has been made to incorporate the concept of self-organization into evolutionary theory. The construction requirements which permit complex systems to adapt remain poorly understood, as is the extent to which selection itself can yield systems able to adapt more successfully. This book explores these themes. It shows how complex systems, contrary to expectations, can spontaneously exhibit stunning degrees of order, and how this order, in turn, is essential for understanding the emergence and development of life on Earth. Topics include the new biotechnology of applied molecular evolution, with its important implications for developing new drugs and vaccines; the balance between order and chaos observed in many naturally occurring systems; new insights concerning the predictive power of statistical mechanics in biology; and other major issues. Indeed, the approaches investigated here may prove to be the new center around which biologicalscience itself will evolve. The work is written for all those interested in the cutting edge of research in the life sciences. (shrink)

We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are (...) explored. (shrink)

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

This paper argues that besides mechanistic explanations, there is a kind of explanation that relies upon “topological” properties of systems in order to derive the explanandum as a consequence, and which does not consider mechanisms or causal processes. I first investigate topological explanations in the case of ecological research on the stability of ecosystems. Then I contrast them with mechanistic explanations, thereby distinguishing the kind of realization they involve from the realization relations entailed by mechanistic explanations, and explain how both (...) kinds of explanations may be articulated in practice. The second section, expanding on the case of ecological stability, considers the phenomenon of robustness at all levels of the biological hierarchy in order to show that topological explanations are indeed pervasive there. Reasons are suggested for this, in which “neutral network” explanations are singled out as a form of topological explanation that spans across many levels. Finally, I appeal to the distinction of explanatory regimes to cast light on a controversy in philosophy of biology, the issue of contingence in evolution, which is shown to essentially involve issues about realization. (shrink)

This paper uses formal Darwinism as elaborated by Alan Grafen to articulate an explanatory pluralism that casts light upon two strands of controversies running across evolutionary biology, viz., the place of organisms versus genes, and the role of adaptation. Formal Darwinism shows that natural selection can be viewed either physics-style, as a dynamics of alleles, or in the style of economics as an optimizing process. After presenting such pluralism, I argue first that whereas population genetics does not support optimization, optimality (...) can still be taken as a default hypothesis when modeling evolutionary processes; and second, that organisms have an explanatory role in evolutionary theory, since they are involved in the economic perspective of optimization. Finally, in order to ask whether the Modern Synthesis can indeed provide a theory of organisms, I apply a Kantian-inspired theoretical view of organisms (underlying much developmental modeling), according to which they are both designed entities and subjects of intrinsic circular processes involving the whole organism and its parts. I first show that the design aspect is accountable for in terms of the Modern Synthesis understood in the formal Darwinism framework. I then question whether the latter aspect of organisms can also be ultimately captured in the same framework, and to this purpose devise an empirical test relying on an assessment of the relative weight of genetic elements in developmental and functional gene regulatory networks. (shrink)

In this paper I offer an analysis of causation based upon a theory of mechanisms-complex systems whose internal parts interact to produce a system's external behavior. I argue that all but the fundamental laws of physics can be explained by reference to mechanisms. Mechanisms provide an epistemologically unproblematic way to explain the necessity which is often taken to distinguish laws from other generalizations. This account of necessity leads to a theory of causation according to which events are causally related when (...) there is a mechanism that connects them. I present reasons why the lack of an account of fundamental physical causation does not undermine the mechanical account. (shrink)

In Darwinism's Struggle for Survival Jean Gayon offers a philosophical interpretation of the history of theoretical Darwinism. He begins by examining the different forms taken by the hypothesis of natural selection in the nineteenth century and the major difficulties which it encountered, particularly with regard to its compatibility with the theory of heredity. He then shows how these difficulties were overcome during the seventy years which followed the publication of Darwin's Origin of Species, and he concludes by analysing the major (...) features of the genetic theory of natural selection, as it developed from 1920 to 1960. This rich and wide-ranging study will appeal to philosophers and historians of science and to evolutionary biologists. (shrink)

It is usually supposed that the central limit theorem explains why various quantities we find in nature are approximately normally distributed—people's heights, examination grades, snowflake sizes, and so on. This sort of explanation is found in many textbooks across the sciences, particularly in biology, economics, and sociology. Contrary to this received wisdom, I argue that in many cases we are not justified in claiming that the central limit theorem explains why a particular quantity is normally distributed, and that in some (...) cases, we are actually wrong. 1 Introduction2 Normal Distributions and the Central Limit Theorem2.1 Normal distributions2.2 The central limit theorem2.3 Terminology3 Explaining Normality3.1 Loaves of bread3.2 Varying variances and probability densities3.3 Tensile strengths and problems with summation3.4 Products of factors and log-normal distributions3.5 Transforming factors and sub-factors3.6 Transformations of quantities3.7 Quantitative genetics3.8 Inference to the best explanation4 Maximum Entropy Explanations5 Conclusion. (shrink)

Recently philosophers of science have begun to pay more attention to the use of highly idealized mathematical models in scientific theorizing. An important example of this kind of highly idealized modeling is the widespread use of optimality models within evolutionary biology. One way to understand the explanations provided by these models is as a censored causal explanation: an explanation that omits certain causal factors in order to focus on a modular subset of the causal processes that led to the explanandum. (...) In this paper, I first argue that the censored causal model approach fails to establish a permanent explanatory role for optimality models in biology and mischaracterizes the explanatory virtues of biological optimality modeling. In addition, I argue that many biological optimality explanations cannot be characterized as censored causal explanations. In response, I propose an alternative approach that analyzes optimality models’ reliance on synchronically representing a system’s constraints and tradeoffs as well as their employment of various kinds of idealization in order to provide equilibrium explanations. (shrink)

Carl F. Craver investigates what we are doing when we use neuroscience to explain what's going on in the brain. When does an explanation succeed and when does it fail? Craver offers explicit standards for successful explanation of the workings of the brain, on the basis of a systematic view about what neuroscientific explanations are.

Does mathematics ever play an explanatory role in science? If so then this opens the way for scientific realists to argue for the existence of mathematical entities using inference to the best explanation. Elsewhere I have argued, using a case study involving the prime-numbered life cycles of periodical cicadas, that there are examples of indispensable mathematical explanations of purely physical phenomena. In this paper I respond to objections to this claim that have been made by various philosophers, and I discuss (...) potential future directions of research for each side in the debate over the existence of abstract mathematical objects. (shrink)

Carl Craver investigates what we are doing when we sue neuroscience to explain what's going on in the brain.

The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection is (...) the only important influence on the evolutionary outcome in question and is thus linked to adaptationism. However, biologists seldom intend this strong use of optimality models. One common alternative that I term the weak use simply involves the claim that an optimality model accurately represents the role of selection in bringing about the outcome. This and other weaker uses of optimality models insulate the optimality approach from criticisms of adaptationism, and they account for the prominence of optimality modeling (broadly construed) in population biology. The centrality of these uses of optimality models ensures a continuing role for the optimality approach, regardless of the fate of adaptationism. (shrink)

We argue that intelligible appeals to interlevel causes (top-down and bottom-up) can be understood, without remainder, as appeals to mechanistically mediated effects. Mechanistically mediated effects are hybrids of causal and constitutive relations, where the causal relations are exclusively intralevel. The idea of causation would have to stretch to the breaking point to accommodate interlevel causes. The notion of a mechanistically mediated effect is preferable because it can do all of the required work without appealing to mysterious interlevel causes. When interlevel (...) causes can be translated into mechanistically mediated effects, the posited relationship is intelligible and should raise no special philosophical objections. When they cannot, they are suspect. (shrink)

In this paper we argue that quantum mechanics provides a genuine kind of structural explanations of quantum phenomena. Since structural explanations only rely on the formal properties of the theory, they have the advantage of being independent of interpretative questions. As such, they can be used to claim that, even in the current absence of one agreed-upon interpretation, quantum mechanics is capable of providing satisfactory explanations of physical phenomena. While our proposal clearly cannot be taken to solve all interpretive issues (...) raised by quantum theory, we will argue that it can be successfully applied to some of its most puzzling phenomena, such Heisenberg's uncertainty relations and quantum non-locality. The discussion of these two case studies will also serve to illustrate the main properties of structural explanations and compare them to the DN and the unificationist models. Finally, we briefly discuss how structural explanations might relate to structural realism. (shrink)

With In Search of Mechanisms, Carl F. Craver and Lindley Darden offer both a descriptive and an instructional account of how biologists discover mechanisms. Drawing on examples from across the life sciences and through the centuries, Craver and Darden compile an impressive toolbox of strategies that biologists have used and will use again to reveal the mechanisms that produce, underlie, or maintain the phenomena characteristic of living things. They discuss the questions that figure in the search for mechanisms, characterizing the (...) experimental, observational, and conceptual considerations used to answer them, all the while providing examples from the history of biology to highlight the kinds of evidence and reasoning strategies employed to assess mechanisms. At a deeper level, Craver and Darden pose a systematic view of what biology is, of how biology makes progress, of how biological discoveries are and might be made, and of why knowledge of biological mechanisms is important for the future of the human species. (shrink)

La vie est formation de formes, la connaissance est analyse des matieres informees. Les sept etudes reunies par Canguilhem dans ce volume temoignent de cette inspiration commune: l'idee d'une irreductibilite de la vie a une serie d'analyses ou de divisions des formes vitales. La specificite du vivant engage au contraire une vision de l'objet biologique qui depasse la comprehension mecaniste des phenomenes physiques. Concue comme un approfondissement de divers enjeux conceptuels en philosophie et en histoire des sciences, La connaissance de (...) la vie est devenue une oeuvre fondamentale dont l'influence sur l'epistemologie contemporaine reste majeure. (shrink)

Darwin's theory of evolution by natural selection provided the first, and only, causal-mechanistic account of the existence of adaptations in nature. As such, it provided the first, and only, scientific alternative to the “argument from design”. That alone would account for its philosophical significance. But the theory also raises other philosophical questions not encountered in the study of the theories of physics. Unfortunately the concept of natural selection is intimately intertwined with the other basic concepts of evolutionary theory—such as the (...) concepts of fitness and adaptation —that are themselves philosophically controversial. Fortunately we can make considerable headway in getting clear on natural selection without solving all of those outstanding problems. (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)

This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story (...) explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems. (shrink)

In this book Ron Amundson examines two hundred years of scientific views on the evolution-development relationship from the perspective of evolutionary developmental biology. This perspective challenges several popular views about the history of evolutionary thought by claiming that many earlier authors had made history come out right for the Evolutionary Synthesis. The book starts with a revised history of nineteenth-century evolutionary thought. It then investigates how development became irrelevant with the Evolutionary Synthesis. It concludes with an examination of the contrasts (...) that persist between mainstream evolutionary theory and evo-devo. This book will appeal to students and professionals in the philosophy and history of science, and biology. (shrink)

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)

This paper applies the conceptual toolkit of Evolutionary Developmental Biology (evo‐devo) to the evolution of the genome and the role of the genome in organism development. This challenges both the Modern Evolutionary Synthesis, the dominant view in evolutionary theory for much of the 20th century, and the typically unreflective analysis of heredity by evo‐devo. First, the history of the marginalization of applying system‐thinking to the genome is described. Next, the suggested framework is presented. Finally, its application to the evolution of (...) genome modularity, the evolution of induced mutations, the junk DNA versus ENCODE debate, the role of drift in genome evolution, and the relationship between genome dynamics and symbiosis with microorganisms are briefly discussed. (shrink)

This paper explores whether natural selection, a putative evolutionary mechanism, and a main one at that, can be characterized on either of the two dominant conceptions of mechanism, due to Glennan and the team of Machamer, Darden, and Craver, that constitute the “new mechanistic philosophy.” The results of the analysis are that neither of the dominant conceptions of mechanism adequately captures natural selection. Nevertheless, the new mechanistic philosophy possesses the resources for an understanding of natural selection under the rubric.