The appeal to mechanisms in scientific explanation is commonplace in contemporary philosophy of science. In short, mechanists argue that an explanation of a phenomenon consists of citing the mechanism that brings the phenomenon about. In this paper, we present an argument that challenges the universality of mechanistic explanation: in explanations of the contemporary features of the eukaryotic cell, biologists appeal to its symbiogenetic origin and therefore the notion of symbiogenesis plays the main explanatory role. We defend the notion that symbiogenesis (...) is non-mechanistic in nature and that any attempt to explain some of the contemporary features of the eukaryotic cell mechanistically turns out to be at least insufficient and sometimes fails to address the question that is asked. Finally, we suggest that symbiogenesis is better understood as a pragmatic scientific law and present an alternative non-mechanistic model of scientific explanation. In the model we present, the use of scientific laws is supposed to be a minimal requirement of all scientific explanations, since the purpose of a scientific explanation is to make phenomena expectable. Therefore, this model would help to understand biologists’ appeal to the notion of symbiosis and thus is shown to be better, for the case under examination, than the mechanistic alternative. (shrink)

Issues concerning scientific explanation have been a focus of philosophical attention from Pre- Socratic times through the modern period. However, recent discussion really begins with the development of the Deductive-Nomological (DN) model. This model has had many advocates (including Popper 1935, 1959, Braithwaite 1953, Gardiner, 1959, Nagel 1961) but unquestionably the most detailed and influential statement is due to Carl Hempel (Hempel 1942, 1965, and Hempel & Oppenheim 1948). These papers and the reaction to them have structured subsequent discussion concerning (...) scientific explanation to an extraordinary degree. After some general remarks by way of background and orientation (Section 1), this entry describes the DN model and its extensions, and then turns to some well-known objections (Section 2). It next describes a variety of subsequent attempts to develop alternative models of explanation, including Wesley Salmon's Statistical Relevance (Section 3) and Causal Mechanical (Section 4) models and the Unificationist models due to Michael Friedman and Philip Kitcher (Section 5). Section 6 provides a summary and discusses directions for future work. (shrink)

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.

The most compelling extant accounts of explanation casts all explanations as causal. Yet there are sciences, theoretical population biology in particular, that explain their phenomena by appeal to statistical, non-causal properties of ensembles. I develop a generalised account of explanation. An explanation serves two functions: metaphysical and cognitive. The metaphysical function is discharged by identifying a counterfactually robust invariance relation between explanans event and explanandum. The cognitive function is discharged by providing an appropriate description of this relation. I offer examples (...) of explanations from portfolio theory and population genetics that meet this characterisation. In each case the invariance relation holds between a statistical property of an ensemble and a change in structure of the ensemble. In neither case, however, does the statistical property cause the outcome it explains. There are genuine statistical, non-causal scientific explanations. (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 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 to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts. (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)

A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this causal approach. I contend that many optimality models provide highly idealized equilibrium explanations that do not accurately represent the causes of their target system. Furthermore, in many contexts, it is in virtue of (...) their independence of causes that optimality models are able to provide a better explanation than competing causal models. Consequently, our account of explanation and modeling must expand beyond the causal approach. (shrink)

In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining the properties (...) of non-decomposable systems. Where part-whole decomposition is not possible, network science provides a much-needed alternative method of compressing information about the behavior of complex systems, and does so without succumbing to problems associated with combinatorial explosion. The article concludes with a comparison between the uses of network representation analyzed in the main discussion, and an entirely distinct use of network representation that has recently been discussed in connection with mechanistic modeling. (shrink)

Causal accounts of scientific explanation are currently broadly accepted (though not universally so). My first task in this paper is to show that, even for a causal approach to explanation, significant features of explanatory practice are not determined by settling how causal facts bear on the phenomenon to be explained. I then develop a broadly causal approach to explanation that accounts for the additional features that I argue an explanation should have. This approach to explanation makes sense of several aspects (...) of actual explanatory practice, including the widespread use of equilibrium explanations, the formulation of distinct explanations for a single event, and the tight relationship between explanations of events and explanations of causal regularities. (shrink)

The representation of a complex but stable, self-regulated and, finally, harmonious nature penetrates the whole history of Ecology, thus contradicting the core of the Darwinian evolution. Originated in the pre-Darwinian Natural History, this representation defined theoretically the various schools of early ecology and, in the context of the cybernetic synthesis of the 1950s, it assumed a typical mathematical form on account of α positive correlation between species diversity and community stability. After 1960, these two aforementioned concepts and their positive correlation (...) were proposed as environmental management tools, in the face of the ecological crisis arising at the time. In the early 1970s, and particularly after May’s evolutionary arguments, the consensus around this positive correlation collapsed for a while, only to be promptly restored for the purpose of attaching an ecological value on biodiversity. In this paper, we explore the history of the diversity–stability hypothesis and we review the successive terms that have been used to express community stability. We argue that this hypothesis has been motivated by the nodal ideological presuppositions of order and harmony and that the scientific developments in this field largely correspond to external social pressures. We conclude that the conflict about the diversity–stability relationship is in fact an ideological debate, referring mostly to the way we see nature and society rather than to an autonomous scientific question. From this point of view, we may understand why Ecology’s concepts and perceptions may decline and return again and again, forming a pluralistic scientific history. (shrink)

The concept of mechanism in biology has three distinct meanings. It may refer to a philosophical thesis about the nature of life and biology (‘mechanicism’), to the internal workings of a machine-like structure (‘machine mechanism’), or to the causal explanation of a particular phenomenon (‘causal mechanism’). In this paper I trace the conceptual evolution of ‘mechanism’ in the history of biology, and I examine how the three meanings of this term have come to be featured in the philosophy of biology, (...) situating the new ‘mechanismic program’ in this context. I argue that the leading advocates of the mechanismic program (i.e., Craver, Darden, Bechtel, etc.) inadvertently conflate the different senses of ‘mechanism’. Specifically, they all inappropriately endow causal mechanisms with the ontic status of machine mechanisms, and this invariably results in problematic accounts of the role played by mechanism-talk in scientific practice. I suggest that for effective analyses of the concept of mechanism, causal mechanisms need to be distinguished from machine mechanisms, and the new mechanismic program in the philosophy of biology needs to be demarcated from the traditional concerns of mechanistic biology. (shrink)

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

Certain scientific explanations of physical facts have recently been characterized as distinctively mathematical –that is, as mathematical in a different way from ordinary explanations that employ mathematics. This article identifies what it is that makes some scientific explanations distinctively mathematical and how such explanations work. These explanations are non-causal, but this does not mean that they fail to cite the explanandum’s causes, that they abstract away from detailed causal histories, or that they cite no natural laws. Rather, in these explanations, (...) the facts doing the explaining are modally stronger than ordinary causal laws or are understood in the why question’s context to be constitutive of the physical arrangement at issue. A distinctively mathematical explanation works by showing the explanandum to be more necessary than ordinary causal laws could render it. Distinctively mathematical explanations thus supply a kind of understanding that causal explanations cannot. 1 Introduction2 Some Distinctively Mathematical Scientific Explanations3 Are Distinctively Mathematical Explanations Set Apart by their Failure to Cite Causes? 4 Distinctively Mathematical Explanations do not Exploit Causal Powers5 How these Distinctively Mathematical Explanations Work6 Conclusion. (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 argues that in some explanations mathematics are playing an explanatory rather than a representational role, and that this feature unifies many types of non-causal or non-mechanistic explanations that some philosophers of science have been recently exploring under various names. After showing how mathematics can play either a representational or an explanatory role by considering two alternative explanations of a same biological pattern—“Bergmann’s rule”—I offer an example of an explanation where the bulk of the explanatory job is done by (...) a mathematical theorem, and where mechanisms involved in the target systems are not explanatorily relevant. Then I account for the way mathematical properties may function in an explanatory way within an explanation by arguing that some mathematical propositions involving variables non directly referring to the target system features constitute constraints to which a whole class of systems should comply, provided they are describable by a mathematical object concerned by those propositions. According to such “constraint account”, those mathematical facts are directly entailing the explanandum, as a consequence of such constraints. I call those explanations “structural”, because here properties of mathematical structures are accounting for the explanandum; various kinds of mathematical structures thereby define various types of structural explanations. (shrink)

Besides mechanistic explanations of phenomena, which have been seriously investigated in the last decade, biology and ecology also include explanations that pinpoint specific mathematical properties as explanatory of the explanandum under focus. Among these structural explanations, one finds topological explanations, and recent science pervasively relies on them. This reliance is especially due to the necessity to model large sets of data with no practical possibility to track the proper activities of all the numerous entities. The paper first defines topological explanations (...) and then explains why topological explanations and mechanisms are different in principle. Then it shows that they are pervasive both in the study of networks—whose importance has been increasingly acknowledged at each level of the biological hierarchy—and in contexts where the notion of selective neutrality is crucial; this allows me to capture the difference between mechanisms and topological explanations in terms of practical modelling practices. The rest of the paper investigates how in practice mechanisms and topologies are combined. They may be articulated in theoretical structures and explanatory strategies, first through a relation of constraint, second in interlevel theories, or they may condition each other. Finally, I explore how a particular model can integrate mechanistic informations, by focusing on the recent practice of merging networks in ecology and its consequences upon multiscale modelling. (shrink)

Philosophers of science typically associate the causal-mechanical view of scientific explanation with the work of Railton and Salmon. In this paper I shall argue that the defects of this view arise from an inadequate analysis of the concept of mechanism. I contrast Salmon's account of mechanisms in terms of the causal nexus with my own account of mechanisms, in which mechanisms are viewed as complex systems. After describing these two concepts of mechanism, I show how the complex-systems approach avoids certain (...) objections to Salmon's account of causal-mechanical explanation. I conclude by discussing how mechanistic explanations can provide understanding by unification. (shrink)

Philosophers of science typically associate the causal-mechanical view of scientific explanation with the work of Railton and Salmon. In this paper I shall argue that the defects of this view arise from an inadequate analysis of the concept of mechanism. I contrast Salmon's account of mechanisms in terms of the causal nexus with my own account of mechanisms, in which mechanisms are viewed as complex systems. After describing these two concepts of mechanism, I show how the complex-systems approach avoids certain (...) objections to Salmon's account of causal-mechanical explanation. I conclude by discussing how mechanistic explanations can provide understanding by unification. (shrink)

The goal of this paper is to present and defend an empiricist, neo-Hempelian account of scientific explanation as ampliative, specialized embedding. The proposal aims to preserve what I take to be the core of Hempel’s empiricist account, by weakening it in some respects and strengthening it in others, introducing two new conditions that solve most of Hempel’s problems without abandoning his empiricist strictures. According to this proposal, to explain a phenomenon is to make it expectable by introducing new conceptual/ontological machinery (...) and using special, and non-ad hoc, non-accidental regularities. The new conditions are elaborated making essential use of two central structuralist ideas, namely T-theoreticity and specialization. I first introduce and qualify the project, then present the new account in detail and assess it vis-à-vis its rivals, and finally discuss some possible objections, concluding that the account fares better than its monistic rivals and well enough to qualify as a promising neo-Hempelian account. Even for those unpersuaded by its monistic goals, it has the merit of calling attention to two new necessary conditions not explicitly emphasized thus far and showing how they serve to answer many of the criticisms addressed against Hempel’s account. (shrink)

Philosophers of science typically associate the causal‐mechanical view of scientific explanation with the work of Railton and Salmon. In this paper I shall argue that the defects of this view arise from an inadequate analysis of the concept of mechanism. I contrast Salmon’s account of mechanisms in terms of the causal nexus with my own account of mechanisms, in which mechanisms are viewed as complex systems. After describing these two concepts of mechanism, I show how the complex‐systems approach avoids certain (...) objections to Salmon’s account of causal‐mechanical explanation. I conclude by discussing how mechanistic explanations can provide understanding by unification. (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)

This essay analyzes and develops recent views about explanation in biology. Philosophers of biology have parted with the received deductive-nomological model of scientific explanation primarily by attempting to capture actual biological theorizing and practice. This includes an endorsement of different kinds of explanation (e.g., mathematical and causal-mechanistic), a joint study of discovery and explanation, and an abandonment of models of theory reduction in favor of accounts of explanatory reduction. Of particular current interest are philosophical accounts of complex explanations that appeal (...) to different levels of organismal organization and use contributions from different biological disciplines. The essay lays out one model that views explanatory integration across different disciplines as being structured by scientific problems. I emphasize the philosophical need to take the explanatory aims pursued by different groups of scientists into account, as explanatory aims determine whether different explanations are competing or complementary and govern the dynamics of scientific practice, including interdisciplinary research. I distinguish different kinds of pluralism that philosophers have endorsed in the context of explanation in biology, and draw several implications for science education, especially the need to teach science as an interdisciplinary and dynamic practice guided by scientific problems and explanatory aims. (shrink)

The paper works towards an account of explanatory integration in biology, using as a case study explanations of the evolutionary origin of novelties-a problem requiring the integration of several biological fields and approaches. In contrast to the idea that fields studying lower level phenomena are always more fundamental in explanations, I argue that the particular combination of disciplines and theoretical approaches needed to address a complex biological problem and which among them is explanatorily more fundamental varies with the problem pursued. (...) Solving a complex problem need not require theoretical unification or the stable synthesis of different biological fields, as items of knowledge from traditional disciplines can be related solely for the purposes of a specific problem. Apart from the development of genuine interfield theories, successful integration can be effected by smaller epistemic units (concepts, methods, explanations) being linked. Unification or integration is not an aim in itself, but needed for the aim of solving a particular scientific problem, where the problem's nature determines the kind of intellectual integration required. (shrink)

In the context of mechanistic explanation, reductionistic research pursues a decomposition of complex systems into their component parts and operations. Using research on the mechanisms responsible for circadian rhythms, I consider both the gains that have been made by discovering genes and proteins that figure in these intracellular oscillators and also highlight the increasingly recognized need to understand higher-level integration, both between cells in the central oscillator and between the central and peripheral oscillators. This history illustrates a common need to (...) complement reductionistic inquiry with investigations at higher-levels. Unlike most other accounts of reduction, the mechanistic framework accommodates this complementary relationship between reductionistic and systems approaches. (shrink)

This article argues that the basic account of mechanism and mechanistic explanation, involving sequential execution of qualitatively characterized operations, is itself insufficient to explain biological phenomena such as the capacity of living organisms to maintain themselves as systems distinct from their environment. This capacity depends on cyclic organization, including positive and negative feedback loops, which can generate complex dynamics. Understanding cyclically organized mechanisms with complex dynamics requires coordinating research directed at decomposing mechanisms into parts and operations with research using computational (...) models to recompose mechanisms and determine their dynamic behavior. This coordinated endeavor yields dynamic mechanistic explanations. (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)

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)

Many explanations in science make use of mathematics. But are there cases where the mathematical component of a scientific explanation is explanatory in its own right? This issue of mathematical explanations in science has been for the most part neglected. I argue that there are genuine mathematical explanations in science, and present in some detail an example of such an explanation, taken from evolutionary biology, involving periodical cicadas. I also indicate how the answer to my title question impacts on broader (...) issues in the philosophy of mathematics; in particular it may help platonists respond to a recent challenge by Joseph Melia concerning the force of the Indispensability Argument. (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.

This fine collection of essays by a leading philosopher of science presents a defence of integrative pluralism as the best description for the complexity of scientific inquiry today. The tendency of some scientists to unify science by reducing all theories to a few fundamental laws of the most basic particles that populate our universe is ill-suited to the biological sciences, which study multi-component, multi-level, evolved complex systems. This integrative pluralism is the most efficient way to understand the different and complex (...) processes - historical and interactive - that generate biological phenomena. This book will be of interest to students and professionals in the philosophy of science. (shrink)

The appeal to mechanisms in scientific explanation is commonplace in contemporary philosophy of science. In short, mechanists argue that an explanation of a phenomenon consists of citing the mechanism that brings the phenomenon about. In this paper, we present an argument that challenges the universality of mechanistic explanation: in explanations of the contemporary features of the eukaryotic cell, biologists appeal to its symbiogenetic origin and therefore the notion of symbiogenesis plays the main explanatory role. We defend the notion that symbiogenesis (...) is non-mechanistic in nature and that any attempt to explain some of the contemporary features of the eukaryotic cell mechanistically turns out to be at least insufficient and sometimes fails to address the question that is asked. Finally, we suggest that symbiogenesis is better understood as a pragmatic scientific law and present an alternative non-mechanistic model of scientific explanation. In the model we present, the use of scientific laws is supposed to be a minimal requirement of all scientific explanations, since the purpose of a scientific explanation is to make phenomena expectable. Therefore, this model would help to understand biologists’ appeal to the notion of symbiosis and thus is shown to be better, for the case under examination, than the mechanistic alternative. (shrink)