One of the central aims of science is explanation: scientists seek to uncover why things happen the way they do. This chapter addresses what kinds of explanations are formulated in biology, how explanatory aims influence other features of the field of biology, and the implications of all of this for biology education. Philosophical treatments of scientific explanation have been both complicated and enriched by attention to explanatory strategies in biology. Most basically, whereas traditional philosophy (...) of science based explanation on derivation from scientific laws, there are many biological explanations in which laws play little or no role. Instead, the field of biology is a natural place to turn for support for the idea that causal information is explanatory. Biology has also been used to motivate mechanistic accounts of explanation, as well as criticisms of that approach. Ultimately, the most pressing issue about explanation in biology may be how to account for the wide range of explanatory styles encountered in the field. This issue is crucial, for the aims of biological explanation influence a variety of other features of the field of biology. Explanatory aims account for the continued neglect of some central causal factors, a neglect that would otherwise be mysterious. This is linked to the persistent use of models like evolutionary game theory and population genetic models, models that are simplified to the point of unreality. These explanatory aims also offer a way to interpret many biologists’ total commitment to one or another methodological approach, and the intense disagreements that result. In my view, such debates are better understood as arising not from different theoretical commitments, but commitments to different explanatory projects. Biology education would thus be enriched by attending to approaches to biological explanation, as well as the unexpected ways that these explanatory aims influence other features of biology. I suggest five lessons for teaching about explanation in biology that follow from the considerations of this chapter. (shrink)

The new mechanists and the autonomy approach both aim to account for how biological phenomena are explained. One identifies appeals to how components of a mechanism are organized so that their activities produce a phenomenon. The other directs attention towards the whole organism and focuses on how it achieves self-maintenance. This paper discusses challenges each confronts and how each could benefit from collaboration with the other: the new mechanistic framework can gain by taking into account what happens outside individual mechanisms, (...) while the autonomy approach can ground itself in biological research into how the actual components constituting an autonomous system interact and contribute in different ways to realize and maintain the system. To press the case that these two traditions should be constructively integrated we describe how three recent developments in the autonomy tradition together provide a bridge between the two traditions: (1) a framework of work and constraints, (2) a conception of function grounded in the organization of an autonomous system, and (3) a focus on control. (shrink)

Inequalities of social goods between gender, racial, or other groups call out for explanation. Such inequalities might be explained by socialization and discrimination. But historically some have attributed these inequalities to biological differences between social groups. Such explanations are highly controversial: on the one hand, they have a very troubling racist and sexist history, but on the other hand, they are empirical claims, and so it seems inappropriate to rule them out a priori. I propose that the appropriate epistemic (...) attitude toward biological explanations of social inequalities is a general but defeasible skepticism. I then turn to the appropriate moral attitude, arguing that when such explanations are inadequately supported, they are offensive. (shrink)

This is an extended review of John Dupré's _Processes of Life_, a collection of essays. It clarifies Dupré's concepts of reductionism and anti-reductionism, and critically examines his associated discussions of downward causation, and both the context sensitivity and multiple realization of categories. It reviews his naturalistic monism, and critically distinguishes between his realism about categories and constructivism about classification. Challenges to his process ontology are presented, as are arguments for his pluralism about scientific categories. None of his main conclusions are (...) rejected; rather it is main arguments for them that are the focus. (shrink)

Walter M. Miller, Jr.’s 1959 novel A Canticle for Leibowitz is on one level a theological reflection on the human propensity to sin. Not coincidentally, the story is located in an Albertinian abbey in the former American southwest six hundred years after a nuclear holocaust, recounting three separate historical periods over the following twelve hundred years: a dark age, a scientific renaissance, and finally a time of technological achievement where a second nuclear holocaust is imminent. Miller asks the question of (...) whether humans as a species cannot avoid committing acts of sin that cumulatively and continually lead to acts of societal and technological self-destruction; his response oscillates between a fatalistic pessimism (the final extinction of the human species) and a vague optimism (the “new creature,” the mutant Rachel, or perhaps a last group of humans leaving the Earth as the curtain falls). It is my thesis in this article that theological questions about sin (as explored by Augustine and later scholars) can be fruitfully approached by analogous genetic processes; more precisely by reference to the rising science of Epigenetics, which explores how genes can express themselves differently without mutating in response to life stressors. In light of the idea of a genetic and epigenetic expression of moral rules of existence, Augustine’s thesis of original sin as something that is inherited makes new sense. In this light, it may also be possible to approach the question that the Canticle proposes: are we destined to self-destruction, or is there a way out of this destructive cycle? (shrink)

I show how archaeologists have two problems. The construction of scenarios accounting for the raw data of Archaeology, the material remains of the past, and the explanation of pre-history. Within Archaeology, there has been an ongoing debate about how to constrain speculation within both of these archaeological projects, and archaeologists have consistently looked to biological mechanisms for constraints. I demonstrate the problems of using biology, either as an analogy for cultural processes or through direct application of biological principles (...) to material remains. This is done through setting out the requirements of a Darwinian Archaeology, and then measuring various approaches against these requirements. This approach leads to the conclusion that archaeologist's explanations of the past must include within their formulations an account of human cognitive capacities within their explanatory framework. The limits of our understanding of the human past will be the limits of our understanding of Homo sapiens. (shrink)

This article presents a challenge that those philosophers who deny the causal interpretation of explanations provided by population genetics might have to address. Indeed, some philosophers, known as statisticalists, claim that the concept of natural selection is statistical in character and cannot be construed in causal terms. On the contrary, other philosophers, known as causalists, argue against the statistical view and support the causal interpretation of natural selection. The problem I am concerned with here arises for the statisticalists because the (...) debate on the nature of natural selection intersects the debate on whether mathematical explanations of empirical facts are genuine scientific explanations. I argue that if the explanations provided by population genetics are regarded by the statisticalists as non-causal explanations of that kind, then statisticalism risks being incompatible with a naturalist stance. The statisticalist faces a dilemma: either she maintains statisticalism but has to renounce naturalism; or she maintains naturalism but has to content herself with an account of the explanations provided by population genetics that she deems unsatisfactory. This challenge is relevant to the statisticalists because many of them see themselves as naturalists. (shrink)

One of biology's fundamental aims is to generate understanding of the living world around—and within—us. In this chapter, I aim to provide a relatively nonpartisan discussion of the nature of explanation in biology, grounded in widely shared philosophical views about scientific explanation. But this discussion also reflects what I think is important for philosophers and biologists alike to appreciate about successful scientific explanations, so some points will be controversial, at least among philosophers. I make three main (...) points: (1) causal relationships and broad patterns have often been granted importance to scientific explanations, and they are in fact both important; (2) some explanations in biology cite the components of or processes in systems that account for the systems’ features, whereas other explanations feature large-scale or structural causes that influence a system; and (3) there can be multiple different explanations of a given biological phenomenon, explanations that respond to different research aims and can thus be compatible with one another even when they may seem to disagree. (shrink)

A common view of how-possibly explanations in biology treats them as explanatorily incomplete. In addition to this interpretation of how-possibly explanation, I argue that there is another interpretation, one which features what I term “explanatory strategies.” This strategy-centered interpretation of how-possibly explanation centers on there being a different explanatory context within which how-possibly explanations are offered. I contend that, in conditions where this strategy context is recognized, how-possibly explanations can be understood as complete explanations. I defend this (...) alternative interpretation by analyzing the explanatory value of simple physical models the nineteenth century developmental biologist Wilhelm His constructed for animal development. (shrink)

Some moral realists claim that moral facts are a species of natural fact, amenable to scientific investigation. They argue that these moral facts are needed in the best explanations of certain phenomena and that this is evidence that they are real. In this paper I present part of a biological account of the function of morality. The account allows the identification of a plausible natural kind that could play the explanatory role that a moral kind would play in naturalist realist (...) theories. It is therefore a candidate for being the moral kind. I argue, however, that it will underdetermine the morally good, that is, identifying the kind is not sufficient to identify what is good. Hence this is not a natural moral kind. Its explanatory usefulness, however, means that we do not have to postulate any further (moral) facts to provide moral explanations. Hence there is no reason to believe that there are any natural moral kinds. (shrink)

Historical explanations in evolutionary biology are commonly characterized as narrative explanations. Examples include explanations of the evolution of particular traits and explanations of macroevolutionary transitions. In this paper I present two case studies of explanations in accounts of pathogen evolution and host-pathogen coevolution, respectively, and argue that one of them is captured well by established accounts of time-sequenced narrative explanation. The other one differs from narrative explanations in important respects, even though it shares some characteristics with them as (...) it is also a population-level historical explanation. I thus argue that the second case represents a different kind of explanation that I call historical explanation of type phenomena. The main difference between the two kinds of explanation is the conceptualization of the explanandum phenomena as particulars or type phenomena, respectively. Narrative explanations explain particulars but also deal with generalization, regularities and type phenomena. Historical explanations of type phenomena, on the other hand, explain multiply realizable phenomena but also deal with particulars. The two kinds of explanation complement each other because they explain different aspects of evolution. (shrink)

The traditional emphasis on the physics of the very small is questioned, and the suggestion made that a crucial test of contributions to the philosophy of science ought to be their applicability to areas which are more representative of the scientific enterprise. Life science is cited as just such an area. It is quantum physics, rather than biology, which nurtures anti-realism. The most respected anti-realism today is that provided by Bas C van Fraassen; and the persuasiveness of his "Constructive (...) Empiricism" is attested to by the failure of avowed realists, such as Ian Hacking, to distance themselves very far from van Fraassen's ideas. A detailed critique of van Fraassen is presented with particular attention paid to his ideas on scientific explanation - the question of whether good explanations are true explanations being one which tends to divide realist and anti-realist. The conclusion is reached that, while realism raises - and fails to answer - a number of philosophical questions, the anti-realism provided by Bas van Fraassen is no better at answering those questions. Moreover, it is argued, van Fraassen's ideas lack plausibility as a description of science and the attitudes of scientists - particularly when attention is shifted from physics to biology. Finally, a number of suggestions are made as to how the philosophical questions posed by realism might be answered in the future. (shrink)

Many biologists and philosophers have worried that importing models of reasoning from the physical sciences obscures our understanding of reasoning in the life sciences. In this paper we discuss one example that partially validates this concern: part-whole reductive explanations. Biology and physics tend to incorporate different models of temporality in part-whole reductive explanations. This results from differential emphases on compositional and causal facets of reductive explanations, which have not been distinguished reliably in prior philosophical analyses. Keeping these two facets (...) distinct facilitates the identifi cation of two further aspects of reductive explanation: intrinsicality and fundamentality. Our account provides resources for discriminating between different types of reductive explanation and suggests a new approach to comprehending similarities and differences in the explanatory reasoning found in biology and physics. (shrink)

Should we think of our universe as law-governed and “clockwork”-like or as disorderly and “soup”-like? Alternatively, should we consciously and intentionally synthesize these two extreme pictures? More concretely, how deterministic are the postulated causes and how rigid are the modeled properties of the best statistical methodologies used in the biological and behavioral sciences? The charge of this entry is to explore thinking about causation in the temporal evolution of biological and behavioral systems. Regression analysis and path analysis are simply explicated (...) with reference to a thought experiment of painting three universes (clockwork, soup, and conscious) useful for imagining our actual universe. Attention to historical, structural, and mechanistic explanatory perspectives broadens the palette of methodologies available for analyzing determinism in, and explanation of, biological and behavioral systems. Each justified methodology provides a partial perspective on complex reality. Is a total explanation of any system ever possible, and what would it require? (shrink)

Following an analysis of the state of investigations and clinical outcomes in the Alzheimer's research field, I argue that the widely-accepted 'amyloid cascade' mechanistic explanation of Alzheimer's disease appears to be fundamentally incomplete. In this context, I propose that a framework termed 'principled mechanism' (PM) can help with remedying this problem. First, using a series of five 'tests', PM systematically compares different components of a given mechanistic explanation against a paradigmatic set of criteria, and hints at various ways (...) of making the mechanistic explanation more 'complete'. These steps will be demonstrated using the amyloid explanation, and its missing or problematic mechanistic elements will be highlighted. Second, PM makes an appeal for the discovery and application of 'biological principles' (BPs), which approximate ceteris paribus laws and are operative at the level of a biological cell. As such, although thermodynamic, evolutionary, ecological and other laws or principles from chemistry and the broader life sciences could inform them, BPs should be considered ontologically unique. BPs could augment different facets of the mechanistic explanation but also allow further independent nomological explanation of the phenomenon. Whilst this overall strategy can be complementary to certain 'New Mechanist' approaches, an important distinction of the PM framework is its equal attention to the explanatory utility of biological principles. Lastly, I detail two hypothetical BPs, and show how they could each inform and improve the potentially incomplete mechanistic aspects of the amyloid explanation and also how they could provide independent explanations of the cellular features associated with Alzheimer's disease. (shrink)

This paper investigates conceptions of explanation, teleology, and analogy in the works of Immanuel Kant (1724-1804) and Georges Cuvier (1769-1832). Richards (2000, 2002) and Zammito (2006, 2012, 2018) have argued that Kant’s philosophy provided an obstacle for the project of establishing biology as a proper science around 1800. By contrast, Russell (1916), Outram (1986), and Huneman (2006, 2008) have argued, similar to suggestions from Lenoir (1989), that Kant’s philosophy influenced the influential naturalist Georges Cuvier. In this article, I (...) wish to expand on and further the work of Russell, Outram, and Huneman by adopting a novel perspective on Cuvier and considering (a) the similar conceptions of proper science and explanation of Kant and Cuvier, and (b) the similar conceptions of the role of teleology and analogy in the works of Kant and Cuvier. The similarities between Kant and Cuvier show, contrary to the interpretation of Richards and Zammito, that some of Kant’s philosophical ideas, whether they derived from him or not, were fruitfully applied by some life scientists who wished to transform life sciences into proper sciences around 1800. However, I also show that Cuvier, in contrast to Kant, had a workable strategy for transforming the life sciences into proper sciences, and that he departed from Kant’s philosophy of science in crucial respects. (shrink)

Using as case studies two early diagrams that represent mechanisms of the cell division cycle, we aim to extend prior philosophical analyses of the roles of diagrams in scientific reasoning, and specifically their role in biological reasoning. The diagrams we discuss are, in practice, integral and indispensible elements of reasoning from experimental data about the cell division cycle to mathematical models of the cycle’s molecular mechanisms. In accordance with prior analyses, the diagrams provide functional explanations of the cell cycle and (...) facilitate the construction of mathematical models of the cell cycle. But, extending beyond those analyses, we show how diagrams facilitate the construction of mathematical models, and we argue that the diagrams permit nomological explanations of the cell cycle. We further argue that what makes diagrams integral and indispensible for explanation and model construction is their nature as locality aids: they group together information that is to be used together in a way that sentential representations do not. (shrink)

A compelling idea holds that reality has a layered structure. We often disagree about what inhabits the bottom layer, but we agree that higher up we find chemical, biological, geological, psychological, sociological, economic, /etc./, entities: molecules, human beings, diamonds, mental states, cities, interest rates, and so on. How is this intuitive talk of a layered structure of entities to be understood? Traditionally, philosophers have proposed to understand layered structure in terms of either reduction or supervenience. But these traditional views face (...) well-known problems. A plausible alternative is that layered structure is to be explicated by appeal to explanations of a certain sort, termed / grounding explanations/. Grounding explanations tell us what obtains in virtue of what. Unfortunately, the use of grounding explanations to articulate the layered conception faces a problem, which I call /the collapse/. The collapse turns on the question of how to ground the facts stated by the explanations themselves. In this paper I make a suggestion about how to ground explanations that avoids the collapse. Briefly, the suggestion is that the fact stated by a grounding explanation is grounded in its /explanans/. (shrink)

The selected effects or ‘etiological’ theory of Proper function is a naturalistic and realist account of biological teleology. It is used to analyse normativity in philosophy of language, philosophy of mind, philosophy of medicine and elsewhere. The theory has been developed with a simple and intuitive view of natural selection. Traits are selected because of their positive effects on the fitness of the organisms that have them. These ‘selected effects’ are the Proper functions of the traits. Proponents argue that this (...) analysis of biological teleology has the unique advantage that the selected effect function of a trait is also a causal explanation of the trait: the trait exists because it performs this function. We show, however, that selected effect functions as currently defined explain the existence of traits only under highly restrictive assumptions about evolutionary dynamics. In many common scenarios in which traits evolve by natural selection, selected effect functions do not explain those traits. This is because definitions of selected effect function extract from any evolutionary scenario only the information that would be explanatorily relevant in the simple evolutionary scenario implicit in those definitions. When applied to more complex scenarios selected effect functions omit the key information that is explanatorily relevant in those scenarios. The assumptions required for selected effect functions to be explanatory are particularly unlikely to hold in the domain that its proponents care most about - the evolution of representation. A more adequate selected effects theory of Proper functions may be possible, but will require much greater attention to the structure of actual evolutionary explanations. (shrink)

Philippe Huneman has recently questioned the widespread application of mechanistic models of scientific explanation based on the existence of structural explanations, i.e. explanations that account for the phenomenon to be explained in virtue of the mathematical properties of the system where the phenomenon obtains, rather than in terms of the mechanisms that causally produce the phenomenon. Structural explanations are very diverse, including cases like explanations in terms of bowtie structures, in terms of the topological properties of the system, or (...) in terms of equilibrium. The role of mathematics in bowtie structured systems and in topologically constrained systems has recently been examined in different papers. However, the specific role that mathematical properties play in equilibrium explanations requires further examination, as different authors defend different interpretations, some of them closer to the new-mechanistic approach than to the structural model advocated by Huneman. In this paper, we cover this gap by investigating the explanatory role that mathematics play in Blaser and Kirschner’s nested equilibrium model of the stability of persistent long-term human-microbe associations. We argue that their model is explanatory because: i) it provides a mathematical structure in the form of a set of differential equations that together satisfy an ESS; ii) that the nested nature of the ESSs makes the explanation of host-microbe persistent associations robust to any perturbation; iii) that this is so because the properties of the ESS directly mirror the properties of the biological system in a non-causal way. The combination of these three theses make equilibrium explanations look more similar to structural explanations than to causal-mechanistic explanation. (shrink)

Cancer biology features the ascription of normal functions to parts of cancers. At least some ascriptions of function in cancer biology track local normality of parts within the global abnormality of the aberration to which those parts belong. That is, cancer biologists identify as functions activities that, in some sense, parts of cancers are supposed to perform, despite cancers themselves having no purpose. The present paper provides a theory to accommodate these normal function ascriptions—I call it the Modeling (...) Account of Normal Function (MA). MA comprises two claims. First, normal functions are activities whose performance by the function-bearing part contributes to the self-maintenance of the whole system and, thereby, results in the continued presence of that part. Second, MA holds that models of system-level activities that are (partly) constitutive of self-maintenance are improved by including a representation of the relevant function-bearing part and by making reference to the activity/activities that part performs which contribute(s) to those system-level activities. I contrast MA with two other accounts that seek to explicate the ascription of normal functions in biology, namely, the organizational account and the selected effects account. Both struggle to extend to cancer biology. However, I offer ecumenical readings which allow them to recover some ascriptions of normal function to parts of cancers. So, though I contend that MA excels in this respect, the purpose of this paper is served if it provides materials for bridging the gap between cancer biology, philosophy of cancer, and the literature on function. (shrink)

This paper adds to the philosophical literature on mechanistic explanation by elaborating two related explanatory functions of idealisation in mechanistic models. The first function involves explaining the presence of structural/organizational features of mechanisms by reference to their role as difference-makers for performance requirements. The second involves tracking counterfactual dependency relations between features of mechanisms and features of mechanistic explanandum phenomena. To make these functions salient, we relate our discussion to an exemplar from systems biological research on the mechanism for (...) countering heat shock—the heat shock response system—in Escherichia coli bacteria. This research also reinforces a more general lesson: ontic constraint accounts in the literature on mechanistic explanation provide insufficiently informative normative appraisals of mechanistic models. We close by outlining an alternative view on the explanatory norms governing mechanistic representation. (shrink)

We argue that genuine biological autonomy, or described at human level as free will, requires taking into account quantum vacuum processes in the context of biological teleology. One faces at least three basic problems of genuine biological autonomy: (1) if biological autonomy is not physical, where does it come from? (2) Is there a room for biological causes? And (3) how to obtain a workable model of biological teleology? It is shown here that the solution of all these three problems (...) is related to the quantum vacuum. We present a short review of how this basic aspect of the fundamentals of quantum theory, although it had not been addressed for nearly 100 years, actually it was suggested by Bohr, Heisenberg, and others. Realizing that the quantum mechanical measurement problem associated with the “collapse” of the wave function is related, in the Copenhagen Interpretation of quantum mechanics, to a process between self-consciousness and the external physical environment, we are extending the issue for an explanation of the different processes occurring between living organisms and their internal environment. Definitions of genuine biological autonomy, biological aim, and biological spontaneity are presented. We propose to improve the popular two-stage model of decisions with a biological model suitable to obtain a deeper look at the nature of the mind-body problem. In the newly emerging picture biological autonomy emerges as a new, fundamental and inevitable element of the scientific worldview. (shrink)

Unless one embraces activities as foundational, understanding activities in mechanisms requires an account of the means by which entities in biological mechanisms engage in their activities—an account that does not merely explain activities in terms of more basic entities and activities. Recent biological research on molecular motors exemplifies such an account, one that explains activities in terms of free energy and constraints. After describing the characteristic “stepping” activities of these molecules and mapping the stages of those steps onto the stages (...) of the motors’ hydrolytic cycles, researchers pieced together from images of the molecules in different hydrolyzation states accounts of how the chemical energy in ATP is transformed in the constrained environments of the motors into the characteristic activities of the motors. We argue that New Mechanism’s standard set of analytic categories—entities, activities, and organization—should be expanded to include constraints and energetics. Not only is such an expansion required descriptively to capture research on molecular motors but, more importantly from a philosophical point of view, it enables a non-regressive account of activities in mechanisms. In other words, this expansion enables a philosophical account of mechanistic explanation that avoids a regress of entities and activities “all the way down.” Rather, mechanistic explanation bottoms out in constraints and energetics. (shrink)

Convincing disputes about explanatory reductionism in the philosophy of biology require a clear and precise understanding of what a reductive explanation in biology is. The central aim of this book is to provide such an account by revealing the features that determine the reductive character of a biological explanation. Chapters I-IV provide the ground, on which I can then, in Chapter V, develop my own account of explanatory reduction in biology: Chapter I reveals the meta-philosophical (...) assumptions that underlie my analysis of reduction, Chapter II introduces the previous debate about reduction in the philosophy of biology by pointing out the most crucial lessons one should learn from this debate, Chapter III critically discusses the two perspectives on explanatory reduction that have been proposed in the philosophy of biology so far, and Chapter IV clarifies in what ways the issue of reduction is entangled with the issue of explanation. In Chapter V I present my own account of explanatory reduction. My main claim is that reductive explanations in the biological sciences possess three main characteristics: they explain the behavior of a biological system S, first, exclusively in terms of factors that are located on a lower level of organization than S, second, by focusing on factors that are internal to S , and third, by describing the genuine parts of S only as “parts in isolation”. This account is ontic because it traces the reductivity of an explanation back to certain relations that exist in the world. (shrink)

There are two fundamentally distinct kinds of biological theorizing. "Formal biology" focuses on the relations, captured in formal laws, among mathematically abstracted properties of abstract objects. Population genetics and theoretical mathematical ecology, which are cases of formal biology, thus share methods and goals with theoretical physics. "Compositional biology," on the other hand, is concerned with articulating the concrete structure, mechanisms, and function, through developmental and evolutionary time, of material parts and wholes. Molecular genetics, biochemistry, developmental biology, (...) and physiology, which are examples of compositional biology, are in serious need of philosophical attention. For example, the very concept of a "part" is understudied in both philosophy of biology and philosophy of science. ;My dissertation is an attempt to clarify the distinction between formal biology and compositional biology and, in so doing, provide a clear philosophical analysis, with case studies, of compositional biology. Given the social, economic, and medical importance of compositional biology, understanding it is urgent. For my investigation, I draw on the philosophical fields of metaphysics and epistemology, as well as philosophy of biology and philosophy of science. I suggest new ways of thinking about some classic philosophy of science issues, such as modeling, laws of nature, abstraction, explanation, and confirmation. I hint at the relevance of my study of two kinds of biological theorizing to debates concerning the disunity of science. (shrink)

In this paper, I present a general theory of topological explanations, and illustrate its fruitfulness by showing how it accounts for explanatory asymmetry. My argument is developed in three steps. In the first step, I show what it is for some topological property A to explain some physical or dynamical property B. Based on that, I derive three key criteria of successful topological explanations: a criterion concerning the facticity of topological explanations, i.e. what makes it true of a particular system; (...) a criterion for describing counterfactual dependencies in two explanatory modes, i.e. the vertical and the horizontal; and, finally, a third perspectival one that tells us when to use the vertical and when to use the horizontal mode. In the second step, I show how this general theory of topological explanations accounts for explanatory asymmetry in both the vertical and horizontal explanatory modes. Finally, in the third step, I argue that this theory is universally applicable across biological sciences, which helps to unify essential concepts of biological networks. (shrink)

My contribution to this Symposium focuses on the links between sexuality and reproduction from the evolutionary point of view.' The relation between women's sexuality and reproduction is particularly importantb ecause of a vital intersectionb etweenp olitics and biology feminists have noticed, for more than a century, that women's identity is often defined in terms of her reproductive capacity. More recently, in the second wave of the feminist movement in the United States, debates about women'si dentityh ave explicitlyi ncludeds exuality;m (...) uch feminist argument in the late 1960's and early 1970's involved an attempt to separate out an autonomous female sexuality from women's reproductive functions. It is especially relevant, then, to examine biological arguments, particularlye volutionarya rgumentst, o see what they say about whether and how women's sexuality is related to reproduction. We shall find that many evolutionarya rgumentss eem to supportt he direct linkingo f female sexualitya nd reproductionY. et I will argue that this supporti s not well-groundedI. n fact, I think evolutionarye xplanationso f female sexuality exemplify how social beliefs and social agendas can influence very basic biological explanations of fundamental physiological processes. In this paper, I shall spend some time spelling out a few examples in which assumptions about the close link between reproduction and sexuality yield misleading results, then I shall conclude with a discussion of the consequences of this case study for issues in the philosophy of science. (shrink)

The interventionist account of causal explanation, in the version presented by Jim Woodward, has been recently claimed capable of buttressing the widely felt—though poorly understood—hunch that high-level, relatively abstract explanations, of the sort provided by sciences like biology, psychology and economics, are in some cases explanatorily optimal. It is the aim of this paper to show that this is mistaken. Due to a lack of effective constraints on the causal variables at the heart of the interventionist causal-explanatory scheme, (...) as presently formulated it is either unable to prefer high-level explanations to low, or systematically overshoots, recommending explanations at so high of a level as to be virtually vacuous. (shrink)

Much has been made of how Darwinian thinking destroyed proofs for the existence of God from ‘design’ in the universe. I challenge that prevailing view by looking closely at classical ‘teleological’ arguments for the existence of God. One version championed by Aristotle and Thomas Aquinas stems from how chance is not a sufficient kind of ultimate explanation of the universe. In the course of constructing this argument, I argue that the classical understanding of teleology is no less necessary in (...) modern Darwinian biology than it was in Aristotle's time. In fact, modern biology strengthens the claims that teleological arguments make by vindicating many of their key features. As a consequence, I show how Aristotle and Aquinas' teleological argument for an intelligent First Cause remains valid. (shrink)

This paper aims to provide a philosophical and theoretical account of biological communication grounded in the notion of organisation. The organisational approach characterises living systems as organised in such a way that they are capable to self-produce and self-maintain while in constant interaction with the environment. To apply this theoretical framework to the study of biological communication, we focus on a specific approach, based on the notion of influence, according to which communication takes place when a signal emitted by a (...) sender triggers a change in the behaviour of the receiver that is functional for the sender itself. We critically analyse the current formulations of this account, that interpret what is functional for the sender in terms of evolutionary adaptations. Specifically, the adoption of this etiological functional framework may lead to the exclusion of several phenomena usually studied as instances of communication, and possibly even of entire fields of investigation such as synthetic biology. As an alternative, we reframe the influence approach in organisational terms, characterising functions in terms of contributions to the current organisation of a biological system. We develop a theoretical account of biological communication in which communicative functions are distinguished from other types of biological functions described by the organisational account (e.g. metabolic, ecological, etc.). The resulting organisational-influence approach allows to carry out causal analyses of current instances of phenomena of communication, without the need to provide etiological explanations. In such a way it makes it possible to understand in terms of communication those phenomena which realise interactive patterns typical of signalling interactions – and are usually studied as such in scientific practice – despite not being the result of evolutionary adaptations. Moreover, this approach provides operational tools to design and study communicative interactions in experimental fields such as synthetic biology. (shrink)

Biological modalities, i.e., biologically possible, impossible, or necessary states of affairs have not received much attention from philosophers. Yet, it is widely agreed that there are biological constraints on physically possible states of affairs, such that not everything that is physically possible is also biologically possible, even if everything that is biologically possible is also physically possible. Furthermore, biologists use concepts that appear to be modal in nature, such as the concept of evolvability in evolutionary developmental biology, or “evo-devo.” (...) The present chapter investigates what kind of modality underlies the concept of evolvability. This concept tries to capture the capacity of an organism or a lineage to sustain genetic changes that enable it to evolve or to evolve adaptively. The basic idea of the proposed approach is to construe evolvability as a kind of accessibility in a modal space. The difficult part is to specify this modal space and the relevant accessibility relation. While there may not be a general way of defining such a relation, there exist model systems for which it is possible, e.g., evolving small RNAs. The modal space in such cases turns out to be quite distinct from those constructed by philosophers, e.g., David Lewis’s similarity metric for possible worlds. Even though the biological case examined here is quite special, attending to the way in which biological possibilities are modeled in this case harbors some general lessons about biological modalities, in particular their dependence on the explanatory goals of the models modeling modality. (shrink)

This paper examines the adequacy of causal graph theory as a tool for modeling biological phenomena and formalizing biological explanations. I point out that the causal graph approach reaches it limits when it comes to modeling biological phenomena that involve complex spatial and structural relations. Using a case study from molecular biology, DNA-binding and -recognition of proteins, I argue that causal graph models fail to adequately represent and explain causal phenomena in this field. The inadequacy of these models is (...) due to their failure to include relevant spatial and structural information in a way that does not render the model non-explanatory, unmanageable, or inconsistent with basic assumptions of causal graph theory. (shrink)

The philosophical conception of mechanistic explanation is grounded on a limited number of canonical examples. These examples provide an overly narrow view of contemporary scientific practice, because they do not reflect the extent to which the heuristic strategies and descriptive practices that contribute to mechanistic explanation have evolved beyond the well-known methods of decomposition, localization, and pictorial representation. Recent examples from evolutionary robotics and network approaches to biology and neuroscience demonstrate the increasingly important role played by computer (...) simulations and mathematical representations in the epistemic practices of mechanism discovery and mechanism description. These examples also indicate that the scope of mechanistic explanation must be re-examined: With new and increasingly powerful methods of discovery and description comes the possibility of describing mechanisms far more complex than traditionally assumed. (shrink)

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the (...) definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies. (shrink)

This MPhil dissertation presents a novel account of teleological explanations in biology. I outline the “shorthand approach” to such explanations, on which they are taken to convey implicit evolutionary explanations. “Selected effects” accounts of teleological explanation dominate recent literature, but they struggle to accommodate teleological explanations of complex traits built through cumulative selection. I articulate the general notion of a landscape explanation, which, applied to biology, explains the evolution of complex features in a population by citing (...) salient features of the population’s fitness landscape. I show that such explanations lend themselves to a teleological shorthand. I close with remarks concerning when a teleological explanation of a trait is legitimate, and why teleological language strikes us as appropriate when we give evolutionary explanations. (shrink)

According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects of (...) scientific explanation. In C. G. Hempel (Ed.), Aspects of scientific explanation (pp. 331–496). New York: Free Press; Kitcher (1989); Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25). My concern is with the minimal suggestion that an adequate philosophical theory of scientific explanation can limit its attention to the format or structure with which theories are represented. The representational subsumption view is a plausible hypothesis about the psychology of understanding. It is also a plausible claim about how scientists present their knowledge to the world. However, one cannot address the central questions for a philosophical theory of scientific explanation without turning one’s attention from the structure of representations to the basic commitments about the worldly structures that plausibly count as explanatory. A philosophical theory of scientific explanation should achieve two goals. The first is explanatory demarcation. It should show how explanation relates with other scientific achievements, such as control, description, measurement, prediction, and taxonomy. The second is explanatory normativity. It should say when putative explanations succeed and fail. One cannot achieve these goals without undertaking commitments about the kinds of ontic structures that plausibly count as explanatory. Representations convey explanatory information about a phenomenon when and only when they describe the ontic explanations for those phenomena. (shrink)

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, yet (...) it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems. (shrink)

This paper sketches a causal account of scientific explanation designed to sustain the judgment that high-level, detail-sparse explanations—particularly those offered in biology—can be at least as explanatorily valuable as lower-level counterparts. The motivating idea is that complete explanations maximize causal economy: they cite those aspects of an event’s causal run-up that offer the biggest-bang-for-your-buck, by costing less (in virtue of being abstract) and delivering more (in virtue making the event stable or robust).

Culture or Biology? The question can seem deep and important. Yet, I argue in this chapter, if you are enthralled by questions about our biological differences, then you are probably confused. My goal is to diagnose the confusion. In debates about the role of biology in the social world it is easy to ask the wrong questions, and it is easy to misinterpret the scientific research. We are intuitively attracted to what is called psychological essentialism, and therefore interpret (...) what is biological as what can be traced to “essences”. On this interpretation, it would be deep and important to know what about, say, the differences between the genders is biological: it would correspond to what is essential to being a man or being a woman, and be opposed to what is a mere accidental feature that some women or some men have. Yet, the psychological essentialist understanding of ‘biological differences’ is deeply mistaken about biology. It has the wrong conception of biological kinds, of biological heritability, and of how genes and hormones work. Those who argue for an important role of ‘biology’ in the explanation of human differences often see ‘the science’ on their side. But this is false – on the interpretation of ‘biological differences’ that is most intuitive and that makes the question appear to be most interesting. Defenders of ‘biology’ often have the science against them. What is often called ‘biology’ is a myth: a myth created by an intuitive tendency that grotesquely distorts real biological research. (shrink)

The term “Complex Systems Biology” was introduced a few years ago [Kaneko, 2006] and, although not yet of widespread use, it seems particularly well suited to indicate an approach to biology which is well rooted in complex systems science. Although broad generalizations are always dangerous, it is safe to state that mainstream biology has been largely dominated by a gene-centric view in the last decades, due to the success of molecular biology. So the one gene - (...) one trait approch, which has often proved to be effective, has been extended to cover even complex traits. This simplifying view has been appropriately criticized, and the movement called systems biology has taken off. Systems biology [Noble, 2006] emphasizes the presence of several feedback loops in biological systems, which severely limit the range of validity of explanations based upon linear causal chains (e.g. gene → behaviour). Mathematical modelling is one the favourite tools of systems biologists to analyze the possible effects of competing negative and positive feedback loops which can be observed at several levels (from molecules to organelles, cells, tissues, organs, organisms, ecosystems). Systems biology is by now a well-established field, as it can be inferred by the rapid growth in number of conferences and journals devoted to it, as well as by the existence of several grants and funded projects.Systems biology is mainly focused upon the description of specific biological items, like for example specific organisms, or specific organs in a class of animals, or specific genetic-metabolic circuits. It therefore leaves open the issue of the search for general principles of biological organization, which apply to all living beings or to at least to broad classes. We know indeed that there are some principles of this kind, biological evolution being the most famous one. The theory of cellular organization also qualifies as a general principle. But the main focus of biological research has been that of studying specific cases, with some reluctance to accept (and perhaps a limited interest for) broad generalizations. This may however change, and this is indeed the challenge of complex systems biology: looking for general principles in biological systems, in the spirit of complex systems science which searches for similar features and behaviours in various kinds of systems. The hope to find such general principles appears well founded, and I will show in Section 2 that there are indeed data which provide support to this claim. Besides data, there are also general ideas and models concerning the way in which biological systems work. The strategy, in this case, is that of introducing simplified models of biological organisms or processes, and to look for their generic properties: this term, borrowed from statistical physics, is used for those properties which are shared by a wide class of systems. In order to model these properties, the most effective approach has been so far that of using ensembles of systems, where each member can be different from another one, and to look for those properties which are widespread. This approach was introduced many years ago [Kauffman, 1971] in modelling gene regulatory networks. At that time one had very few information about the way in which the expression of a given gene affects that of other genes, apart from the fact that this influence is real and can be studied in few selected cases (like e.g. the lactose metabolism in E. coli). Today, after many years of triumphs of molecular biology, much more has been discovered, however the possibility of describing a complete map of gene-gene interactions in a moderately complex organism is still out of reach. Therefore the goal of fully describing a network of interacting genes in a real organism could not be (and still cannot be) achieved. But a different approach has proven very fruitful, that of asking what are the typical properties of such a set of interacting genes. Making some plausible hypotheses and introducing some simplifying assumptions, Kauffman was able to address some important problems. In particular, he drew attention to the fact that a dynamical system of interacting genes displays selforganizing properties which explain some key aspects of life, most notably the existence of a limited number of cellular types in every multicellular organism (these numbers are of the order of a few hundreds, while the number of theoretically possible types, absent interactions, would be much much larger than the number of protons in the universe). In section 3 I will describe the ensemble based approach in the context of gene regulatory networks, and I will show that it can describe some important experimental data. Finally, in section 4 I will discuss some methodological aspects. (shrink)

Unlike in physics, the category of thought experiment is not very common in biology. At least there are no classic examples that are as important and as well-known as the most famous thought experiments in physics, such as Galileo’s, Maxwell’s or Einstein’s. The reasons for this are far from obvious; maybe it has to do with the fact that modern biology for the most part sees itself as a thoroughly empirical discipline that engages either in real natural history (...) or in experimenting on real organisms rather than fictive ones. While theoretical biology does exist and is recognized as part of biology, its role within biology appears to be more marginal than the role of theoretical physics within physics. It could be that this marginality of theory also affects thought experiments as sources of theoretical knowledge. Of course, none of this provides a sufficient reason for thinking that thought experiments are really unimportant in biology. It is quite possible that the common perception of this matter is wrong and that there are important theoretical considerations in biology, past or present, that deserve the title of thought experiment just as much as the standard examples from physics. Some such considerations may even be widely known and considered to be important, but were not recognized as thought experiments. In fact, as we shall see, there are reasons for thinking that what is arguably the single most important biological work ever, Charles Darwin’s On the Origin of Species, contains a number of thought experiments. There are also more recent examples both in evolutionary and non-evolutionary biology, as we will show. Part of the problem in identifying positive examples in the history of biology is the lack of agreement as to what exactly a thought experiment is. Even worse, there may not be more than a family resemblance that unifies this epistemic category. We take it that classical thought experiments show the following characteristics: They serve directly or indirectly in the non-empirical epistemic evaluation of theoretical propositions, explanations or hypotheses. Thought experiments somehow appeal to the imagination. They involve hypothetical scenarios, which may or may not be fictive. In other words, thought experiments suppose that certain states of affairs hold and then try to intuit what would happen in a world where these suppositions are true. We want to examine in the following sections if there are episodes in the history of biology that satisfy these criteria. As we will show, there are a few episodes that might satisfy all three of these criteria, and many more if the imagination criterion is dropped or understood in a lose sense. In any case, this criterion is somewhat vague in the first place, unless a specific account of the imagination is presupposed. There will also be issues as to what exactly “non-empirical” means. In general, for the sake of discussion we propose to understand the term “thought experiment” here in a broad rather than a narrow sense here. We would rather be guilty of having too wide a conception of thought experiment than of missing a whole range of really interesting examples. (shrink)

The biological sciences study (bio)complex living systems. Research directed at the mechanistic explanation of the "live" state truly requires a pluralist research program, i.e. BioComplexity research. The program should apply multiple intra-level and inter-level theories and methodologies. We substantiate this thesis with analysis of BioComplexity: metabolic and modular control analysis of metabolic pathways, emergence of oscillations, and the analysis of the functioning of glycolysis.

This collection of essays explores the metaphysical thesis that the living world is not made up of substantial particles or things, as has often been assumed, but is rather constituted by processes. The biological domain is organised as an interdependent hierarchy of processes, which are stabilised and actively maintained at different timescales. Even entities that intuitively appear to be paradigms of things, such as organisms, are actually better understood as processes. Unlike previous attempts to articulate processual views of biology, (...) which have tended to use Alfred North Whitehead’s panpsychist metaphysics as a foundation, this book takes a naturalistic approach to metaphysics. It submits that the main motivations for replacing an ontology of substances with one of processes are to be found in the empirical findings of science. Biology provides compelling reasons for thinking that the living realm is fundamentally dynamic, and that the existence of things is always conditional on the existence of processes. The phenomenon of life cries out for theories that prioritise processes over things, and it suggests that the central explanandum of biology is not change but rather stability, or more precisely, stability attained through constant change. This edited volume brings together philosophers of science and metaphysicians interested in exploring the consequences of a processual philosophy of biology. The contributors draw on an extremely wide range of biological case studies, and employ a process perspective to cast new light on a number of traditional philosophical problems, such as identity, persistence, and individuality. (shrink)

Since Aristotle the central question in biology was the origin of organic form; a question put in the backyard by neo-Darwinism that considers organic form as a side effect of the interactions between genes and their products. On the other hand, the fashionable notion of self-organization also fails to provide a true causal explanation for organic form. For Aristotle form is both a cause and the principle of intelligibility and this coupled to the classical concepts of potentiality and (...) actuality provides a rational framework,currently embodied in the contemporary concept of ‘attractor’, for understanding the origin of organic form. (shrink)

In the last couple of years, a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the questions what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences (i.e., explanations that don’t cite causes in the explanans) sometimes take a form of the question of what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and in (...) virtue of what they are explanatory. These questions raise further issues about counterfactuals, modality, and explanatory asymmetries: i.e., do mathematical and non-causal explanations support counterfactuals, and how ought we to understand explanatory asymmetries in non-causal explanations? Even though these are very common issues in the philosophy of physics and mathematics, they can be found in different guises in the philosophy of biology where there is the statistical interpretation of the Modern Synthesis theory of evolution, according to which the post-Darwinian theory of natural selection explains evolutionary change by citing statistical properties of populations and not the causes of changes. These questions also arise in philosophy of ecology or neuroscience in regard to the nature of topological explanations. The question here is can the mathematical (or more precisely topological) properties in network models in biology, ecology, neuroscience, and computer science be explanatory of physical phenomena, or are they just different ways to represent causal structures. The aim of this special issue is to unify all these debates around several overlapping questions. These questions are: are there genuinely or distinctively mathematical and non-causal explanations?; are all distinctively mathematical explanations also non-causal; in virtue of what they are explanatory; does the instantiation, implementation, or in general, applicability of mathematical structures to a variety of phenomena and systems play any explanatory role? This special issue provides a platform for unifying the debates around several key issues and thus opens up avenues for better understanding of mathematical and non-causal explanations in general, but also, it will enable even better understanding of key issues within each of the debates. (shrink)

CONTENT 1. Misconceptions of Darwin's Theory of Evolution 2. Darwinism against Essentialism and the Concept of Species 3. Function and Biological Explanation 4. The Gene 목차 1. 다윈의 진화론에 대한 오해들 2. 본질주의에 대한 진화론의 반대와 종(Species)의 개념 3. 기능(function)과 생명과학적 설명 4. 유전자 맺음말.

Among the factors necessary for the occurrence of some event, which of these are selectively highlighted in its explanation and labeled as causes-and which are explanatorily omitted, or relegated to the status of background conditions? Following J. S. Mill, most have thought that only a pragmatic answer to this question was possible. In this paper I suggest we understand this ‘causal selection problem’ in causal-explanatory terms, and propose that explanatory trade-offs between abstraction and stability can provide a principled solution (...) to it. After sketching that solution, it is applied to a few biological examples. (shrink)

We characterize a type of functional explanation that addresses why a homologous trait originating deep in the evolutionary history of a group remains widespread and largely unchanged across the group’s lineages. We argue that biologists regularly provide this type of explanation when they attribute conserved functions to phenotypic and genetic traits. The concept of conserved function applies broadly to many biological domains, and we illustrate its importance using examples of molecular sequence alignments at the intersection of evolution and (...) cell biology. We use these examples to show how the study of conserved functions can integrate knowledge of a trait’s causal effects on fitness and its history of natural selection without invoking adaptation. We also show how conserved function provides a novel basis for addressing objections against evolutionary functions raised by Robert Cummins. (shrink)