Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical models, models now come (...) in three forms: in vivo, in vitro and in silico. Each has been investigated separately, and many specific problems they raised have been laid out. Another relevant distinction is disciplinary: do models differ in significant ways according to the discipline involved—medicine or biology, evolutionary biology or earth science? Focusing on either this threefold distinction or the disciplinary boundaries reveals that they might not be sufficient from a philosophical perspective. On the contrary, focusing on the interaction between these various kinds of models, some interesting forms of explanation come to the fore, as is exemplified by the papers included in this issue. On the other hand, a focus on the use of models, rather than on their content, shows that the distinction between biological and medical models is theoretically sound. (shrink)

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018.

Reduction and reductionism have been central philosophical topics in analytic philosophy of science for more than six decades. Together they encompass a diversity of issues from metaphysics and epistemology. This article provides an introduction to the topic that illuminates how contemporary epistemological discussions took their shape historically and limns the contours of concrete cases of reduction in specific natural sciences. The unity of science and the impulse to accomplish compositional reduction in accord with a layer-cake vision of the sciences, the (...) seminal contributions of Ernest Nagel on theory reduction and how they strongly conditioned subsequent philosophical discussions, and the detailed issues pertaining to different accounts of reduction that arise in both physical and biological science (e.g., limit-case and part-whole reduction in physics, the difference-making principle in genetics, and mechanisms in molecular biology) are explored. The conclusion argues that the epistemological heterogeneity and patchwork organization of the natural sciences encourages a pluralist stance about reduction. (shrink)

Accounts of mechanistic explanation have emphasized the importance of looking down—decomposing a mechanism into its parts and operations. Using research on visual processing as an exemplar, I illustrate how productive such research has been. But once multiple components of a mechanism have been identified, researchers also need to figure out how it is organized—they must look around and determine how to recompose the mechanism. Although researchers often begin by trying to recompose the mechanism in terms of sequential operations, they frequently (...) find that the components of a mechanism interact in complex ways involving positive and negative feedback and that the organization often exhibits highly interactive local networks linked by a few long-range connections (small-worlds organization) and power law distributions of connections. The mechanisms are themselves active systems that are perturbed by inputs but not set in motion by them. Researchers also need to look up —situate a mechanism in its context, which may be a larger mechanism that modulates its behavior. When looking down is combined with looking around and up, mechanistic research results in an integrated, multi-level perspective. (shrink)

This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions (...) related to the general practice of computer simulations. (shrink)

Schaffner’s model of theory reduction has played an important role in philosophy of science and philosophy of biology. Here, the model is found to be problematic because of an internal tension. Indeed, standard antireductionist external criticisms concerning reduction functions and laws in biology do not provide a full picture of the limits of Schaffner’s model. However, despite the internal tension, his model usefully highlights the importance of regulative ideals associated with the search for derivational, and embedding, deductive relations among mathematical (...) structures in theoretical biology. A reconstructed Schaffnerian model could therefore shed light on mathematical theory development in the biological sciences and on the epistemology of mathematical practices more generally. *Received November 2006; revised March 2009. †To contact the author, please write to: Philosophy Department, University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064; e‐mail: [email protected]. (shrink)

Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli, and the origin of eukaryotic cells through endosymbiosis.

John Searle has argued that functions owe their existence to the value that we put into life and survival. In this paper, I will provide a critique of Searle’s argument concerning the ontology of functions. I rely on a standard analysis of functional predicates as relating not only a biological entity, an activity that constitutes the function of this entity and a type of system but also a goal state. A functional attribution without specification of such a goal state has (...) no truth-value. But if completed with a goal state, functional attributions understood as four-place relations attain a truth-value. The truth conditions of all attributions of function involve a dependence claim of the goal state on the function bearer’s activity. The nature of this dependence may differ; I consider five different possibilities: causality, mechanistic constitution, mereology, supervenience and metaphysical grounding. If these dependency relations are objective, Searle’s central ontological thesis fails. What he ought to have said is that our valuing survival or other goal states may be the reason why biology seeks functional knowledge, but this has nothing to do with ontology. I will show further that Searle also raised an interesting challenge concerning the relationship of functional and causal truths, but it does not threaten the objectivity of functions either. At best, it could show that functional vocabulary is eliminable. However, I will show that functional vocabulary is not so eliminable. (shrink)

Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in for a physically (...) different kind of process. I discuss the advantages of this approach in the context of evolutionary biology. (shrink)

The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. Therefore, neural representations are as real as neurons, action potentials, or any other well-established entities in our ontology.

This article describes contrasting ideas for a set of topics in epidemiological thinking. The premise underlying this contribution to the special edition is that researchers develop their epidemiological thinking over time through interactions with other researchers who have a variety of in-practice commitments, such as to kinds of cases and methods of analysis, and not simply to a philosophical framework for explanation. I encourage discussants from philosophy and epidemiology to draw purposefully from across a range of topics and contrasting positions, (...) and thereby pursue critical thinking in the sense of understanding ideas and practices better when we examine them in relation to alternatives. After an initial topic concerning practices for developing epidemiological literacy, a number of conceptual steps follow—the characterization of the very phenomena we might be concerned with, the scope and challenges of the field of epidemiology, the formulation of categories—before linking associations, predictions, causes and interventions and examining the confounding of purported links. Building on that conceptual basis, the remaining topics consist of issues or angles of analysis related to the complexities of inequalities within and between populations, context, and changes over the life course. The organization of topics derives from a graduate course that I teach that aims for epidemiological literacy, not technical ability in statistical formulas and data analysis, and shares the underlying premise and critical thinking goals of this article. During the topic-by-topic description, some assertions about explanation and intervention emerge, notably, that epidemiological–philosophical discussion about causality often leaves unclear or unexamined whether a modifiable factor shown to have been associated with a difference in the data from past observations should be thought of as factor that, when modified, would generate that difference going forward. The article concludes with a “Limitations of this Study” section that teases out different kinds of description–prescription relationship that are implied in undertaking philosophy of epidemiology and identifies some other considerations that are implied but not emphasized by this article. (shrink)

Consensus conferences are social techniques which involve bringing together a group of scientific experts, and sometimes also non-experts, in order to increase the public role in science and related policy, to amalgamate diverse and often contradictory evidence for a hypothesis of interest, and to achieve scientific consensus or at least the appearance of consensus among scientists. For consensus conferences that set out to amalgamate evidence, I propose three desiderata: Inclusivity, Constraint, and Evidential Complexity. Two examples suggest that consensus conferences can (...) readily satisfy Inclusivity and Evidential Complexity, but consensus conferences do not as easily satisfy Constraint. I end by discussing the relation between social inclusivity and the three desiderata. (shrink)

Developmental Systems Theory (DST) emphasises the importance of non-genetic factors in development and their relevance to evolution. A common, deflationary reaction is that it has long been appreciated that non-genetic factors are causally indispensable. This paper argues that DST can be reformulated to make a more substantive claim: that the special role played by genes is also played by some (but not all) non-genetic resources. That special role is to transmit inherited representations, in the sense of Shea (2007: Biology and (...) Philosophy, 22, 313-331). Formulating DST as the claim that there are non-genetic inherited representations turns it into a striking, empirically-testable hypothesis, driving the sort of investigations that are only now beginning to appear in the scientific literature. DST’s characteristic rejection of a gene vs. environment dichotomy is preserved, but without dissolving all potentially explanatory distinctions into an interactionist causal soup, as some have alleged. (shrink)

This article examines a metaphilosophical issue, namely existing disagreements in philosophy of science about the significance of using multiple means of determination in scientific practice. We argue that this disagreement can, in part, be resolved by separating different questions that can be asked about the use of multiple means of determination, including the following: what can be concluded from the convergence of data or the convergence of claims about phenomena? Are the conclusions drawn from the convergence of data and of (...) statements about phenomena of special importance to the debate about realism and antirealism? Do inferences based on multiple means of determination have stronger epistemic force than inferences that are secured in other ways? Is the epistemic goal of deploying multiple means of determination well entrenched within the scientific community? Most of these questions can be discussed both in a formal and in an empirical perspective. If the differences in perspective are taken into account, some disagreements can be easily resolved. In part, however, the disagreements reflect historiographical challenges that are very difficult, if not impossible to meet. (shrink)

We investigate the context of discovery of two significant achievements of twentieth century biochemistry: the chemiosmotic mechanism of oxidative phosphorylation and the dark reaction of photosynthesis. The pursuit of these problems involved discovery strategies such as the transfer, recombination and reversal of previous causal and mechanistic knowledge in biochemistry. We study the operation and scope of these strategies by careful historical analysis, reaching a number of systematic conclusions: even basic strategies can illuminate “hard cases” of scientific discovery that go far (...) beyond simple extrapolation or analogy; the causal–mechanistic approach to discovery permits a middle course between the extremes of a completely substrate-neutral and a completely domain-specific view of scientific discovery; the existing literature on mechanism discovery underemphasizes the role of combinatorial approaches in defining and exploring search spaces of possible problem solutions; there is a subtle interplay between a fine-grained mechanistic and a more coarse-grained causal level of analysis, and both are needed to make discovery processes intelligible. (shrink)

We investigate the context of discovery of two significant achievements of twentieth century biochemistry: the chemiosmotic mechanism of oxidative phosphorylation and the dark reaction of photosynthesis. The pursuit of these problems involved discovery strategies such as the transfer, recombination and reversal of previous causal and mechanistic knowledge in biochemistry. We study the operation and scope of these strategies by careful historical analysis, reaching a number of systematic conclusions: even basic strategies can illuminate “hard cases” of scientific discovery that go far (...) beyond simple extrapolation or analogy; the causal–mechanistic approach to discovery permits a middle course between the extremes of a completely substrate-neutral and a completely domain-specific view of scientific discovery; the existing literature on mechanism discovery underemphasizes the role of combinatorial approaches in defining and exploring search spaces of possible problem solutions; there is a subtle interplay between a fine-grained mechanistic and a more coarse-grained causal level of analysis, and both are needed to make discovery processes intelligible. (shrink)

Louis Pasteur’s defeat of belief in spontaneous generation has been a classical rationalist example of how the experimental approach of modern science can reveal superstition. Farley and Geison told a counter-story of how Pasteur’s success was due to political and ideological support rather than superior experimental science. They claimed that Pasteur violated proper norms of scientific method, and that the French Academy of Science did not see this, or did not want to. Farley and Geison argued that Pouchet’s experiments were (...) as valid as those of Pasteur. In this paper I argue that the core of the scientific debate was not general theories for or against spontaneous generation but the outcome of specific experiments. It was on the conduct of these experiments that the Academy made judgements favorable to Pasteur. Claude Bernard was a colleague of Pasteur, supportive and sometimes critical. I argue that Bernard’s fact-oriented methodology of “experimental medicine” is a better guide to explaining the controversy than the hypothetic-deductive view of scientific method typical of logical empiricism. (shrink)

This paper looks at an appeal to the authority of biomedical research that has recently been used by empirical economists to motivate and justify their methods. I argue that those who make this appeal mistake the nature of biomedical research. Randomised trials, which are said to have revolutionised biomedical research, are a central methodology, but according to only one paradigm. There is another paradigm at work in biomedical research, the inferentialist paradigm, in which randomised trials play no special role. I (...) outline the inferentialist alternative in broad strokes, apply it to a recent controversy in econometrics and draw some general conclusions concerning econometric methodology. (shrink)

Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this article, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ are new types of material manipulations and are important not because of their explanatory force, but because of the interventionist strategies they afford. This (...) is a distinction between aspects of the same model. In molecular biology, models may be treated either as ‘models of’ or as ‘models for’. By analysing the discovery and characterization of restriction–modification systems and their exploitation for DNA cloning and mapping, I identify the differences between treating a model as a ‘model of’ or as a ‘model for’. These lie in the cognitive disposition of the modeller towards the model: a modeller will look at a model as a ‘model of’ if interested in its explanatory force, or as a ‘model for’ if interested in the material manipulations it can possibly afford. (shrink)

This paper is about conceptual engineering. Specifically, it discusses a common objection to CE, which I call the Discontinuity Objection. According to the Discontinuity Objection, CE leads to problematic discontinuities in subject and/or inquiry – making it philosophically uninteresting or irrelevant. I argue that a conceptual engineer can dismiss the Discontinuity Objection by showing that the pre-engineering concept persists through the proposed changes. In other words, the Discontinuity Objection does not apply if the proposal involves identity-preserving changes. Two existing views (...) on identity-preserving changes are considered and rejected. I then argue that an identity-preserving conceptual change is one that allows the concept to continue to perform its function. A concept’s function is its job, its point and purpose, its role in a conceptual repertoire. In a slogan: Preserve a concept’s function, and you preserve the concept itself; preserve the concept, and you preserve the subject. The paper concludes by discussing some implications of this view. (shrink)

Which domains of biology do philosophers of biology primarily study? The fact that philosophy of biology has been dominated by an interest for evolutionary biology is widely admitted, but it has not been strictly demonstrated. Here I analyse the topics of all the papers published in Biology & Philosophy, just as the journal celebrates its thirtieth anniversary. I then compare the distribution of biological topics in Biology & Philosophy with that of the scientific journal Proceedings of the National Academy of (...) Science of the USA, focusing on the recent period 2003–2015. This comparison reveals a significant mismatch between the distributions of these topics. I examine plausible explanations for that mismatch. Finally, I argue that many biological topics underrepresented in philosophy of biology raise important philosophical issues and should therefore play a more central role in future philosophy of biology. (shrink)

Experiments are commonly thought to have epistemic privilege over simulations. Two ideas underpin this belief: first, experiments generate greater inferential power than simulations, and second, simulations cannot surprise us the way experiments can. In this article I argue that neither of these claims is true of experiments versus simulations in general. We should give up the common practice of resting in-principle judgments about the epistemic value of cases of scientific inquiry on whether we classify those cases as experiments or simulations, (...) per se. To the extent that either methodology puts researchers in a privileged epistemic position, this is context sensitive. (shrink)

This article compares the epistemic roles of theoretical models and model organisms in science, and specifically the role of non-human animal models in biomedicine. Much of the previous literature on this topic shares an assumption that animal models and theoretical models have a broadly similar epistemic role—that of indirect representation of a target through the study of a surrogate system. Recently, Levy and Currie have argued that model organism research and theoretical modelling differ in the justification of model-to-target inferences, such (...) that a unified account based on the widely accepted idea of modelling as indirect representation does not similarly apply to both. I defend a similar conclusion, but argue that the distinction between animal models and theoretical models does not always track a difference in the justification of model-to-target inferences. Case studies of the use of animal models in biomedicine are presented to illustrate this. However, Levy and Currie’s point can be argued for in a different way. I argue for the following distinction. Model organisms function as surrogate sources of evidence, from which results are transferred to their targets by empirical extrapolation. By contrast, theoretical modelling does not involve such an inductive step. Rather, theoretical models are used for drawing conclusions from what is already known or assumed about the target system. Codifying assumptions about the causal structure of the target in external representational media allows one to apply explicit inferential rules to reach conclusions that could not be reached with unaided cognition alone. (shrink)

It is customary to distinguish experimental from purely observational sciences. The former include physics and molecular biology, the latter astronomy and palaeontology. Experiments involve actively intervening in the course of nature, as opposed to observing events that would have happened anyway. When a molecular biologist inserts viral DNA into a bacterium in his laboratory, this is an experiment; but when an astronomer points his telescope at the heavens, this is an observation. Without the biologist’s handiwork the bacterium would never have (...) contained foreign DNA; but the planets would have continued orbiting the sun whether or not the astronomer had directed his telescope skyward. The observational/experimental distinction would probably be difficult to make precise 1, as the notion of an ‘intervention’ is not easily defined, but it is intuitively fairly clear, and is frequently invoked by scientists and historians of science. Experimentation, or ‘putting questions to nature’, is often cited as a hallmark of the modern scientific method, something that permitted the enormous advances of the last 350 years. And it is sometimes said that the social sciences lag behind the natural because controlled experiments cannot be done so readily in the former. Moreover in certain sciences, e.g. epidemiology, students are explicitly taught that experimental data is preferable to observational data, particularly for doing causal inference. So the distinction between observational and experimental science has quite wide currency, and is often regarded as methodologically significant. Surprisingly, mainstream philosophy of science has had rather little to say about the observational/experimental distinction. 2 For example, discussions of confirmation usually invoke a notion of ‘evidence’, to be contrasted with ‘theory’ or ‘hypothesis’; the aim is to understand how the evidence bears on the hypothesis. But whether this ‘evidence’ comes from observation or experiment generally plays no role in the discussion; this is true …. (shrink)

In this article we examine the “phenotype” concept in light of recent technological advances in Genome-Wide Association Studies . By observing the technology and its presuppositions, we put forward the thesis that at least in this case genotype and phenotype are effectively coidentifled one by means of the other. We suggest that the coidentiflcation of genotype-phenotype couples in expression-based GWAS also indicates a conceptual dependence, which we call “co-deñnition.” We note that viewing these terms as codeflned runs against possible expectations, (...) viz., that genotypes and phenotypes could ultimately be expressed independently from one another. In addition, the co-definition of genotypes and phenotypes in this context emphasizes the correlative character of both genotypes and phenotypes in GWAS. (shrink)

We characterize access to empirical objects in biology from a theoretical perspective. Unlike objects in current physical theories, biological objects are the result of a history and their variations continue to generate a history. This property is the starting point of our concept of measurement. We argue that biological measurement is relative to a natural history which is shared by the different objects subjected to the measurement and is more or less constrained by biologists. We call symmetrization the theoretical and (...) often concrete operation which leads to considering biological objects as equivalent in a measurement. Last, we use our notion of measurement to analyze research strategies. Some strategies aim to bring biology closer to the epistemology of physical theories, by studying objects as similar as possible, while others build on biological diversity. (shrink)

In 1966 the population biologist Richard Levins gave a forceful and in?uential defence of a method called “robustness analysis” (RA). RA is a way of assessing the result of a model by showing that different but related models give the same result. As Levins put it, “our truth is the intersection of independent lies” (1966, 423). Steven Orzack and Elliott Sober (1993) responded with an equally forceful critique of this method, concluding that the idea of robustness “lacks proper de?nition and (...) that its bearing on the question of whether a proposition is true is highly problematic” (533). Replies to Orzack and Sober, from Levins (1993) and Weisberg (2006b), have rejected the idea that RA, on its own, can con?rm the results of models. I argue that these replies have not properly addressed Orzack and Sober’s real criticisms, which focus not on the role of empirical data in RA but on the problem of ensuring that the models used in RA are independent. By drawing on accounts of RA in experimental science, I argue that there is in fact a fallible but viable form of RA that can both con?rm the results of models and incorporate empirical data. However, for reasons other than those of Orzack and Sober, I conclude that RA may be of limited use in model-based science, as the example of ?shery biology can show. (shrink)

In addition to his experiments on selection in pure lines, Wilhelm Johannsen performed less well-known hybridisation experiments with beans. This article describes these experiments and discusses Johannsen’s motivations and interpretations, in the context of developments in early genetics. I will show that Johannsen first presented the hybridisation experiments as an additional control for his selection experiments. The latter were dedicated to investigating heredity with respect to debates concerning the significance of natural selection of continuous variation for evolution. In the course (...) of the establishment of a Mendelian research program after 1900, the study of heredity gained increasing independence from questions of evolution, and focused more on the modes and mechanisms of heredity. Further to their role as control experiments, Johannsen also saw his hybridisation experiments as contributing to the Mendelian program, by extending the scope of the principles of Mendelian inheritance to quantitative characters. Towards the end of the first decade of genetics, Johannsen revisited his experiments to illustrate the many–many relationship between genes and characters, at a time when that relationship appeared increasingly complex, and the unit-character concept, accordingly, became inadequate. For the philosophy of science, the example shows that experiments can have multiple roles in a research programme, and can be interpreted in the light of questions other than those that motivated the experiments in the first place. (shrink)

In this article, I introduce the notion of horizon for scientific practice (HSP), representing limits or boundaries within which scientists ply their trade, to facilitate analysis of scientific discovery and progress. The notion includes not only constraints that delimit scientific practice, e.g. of bringing experimentation to a temporary conclusion, but also possibilities that open up scientific practice to additional scientific discovery and to further scientific progress. Importantly, it represents scientific practice as a dynamic and developmental integration of activities to investigate (...) and analyze the natural world. I use the discovery of the clotting factor, thrombin, and the experiments conducted by the Johns Hopkins physiologist, William Howell, on the enzymatic nature of thrombin to illustrate the notion of HSP. In a concluding section, I compare the notion of HSP to other notions for scientific practice proposed in the history and philosophy of science literature. (shrink)

“Functional homology” appears regularly in different areas of biological research and yet it is apparently a contradiction in terms—homology concerns identity of structure regardless of form and function. I argue that despite this conceptual tension there is a legitimate conception of ‘homology of function’, which can be recovered by utilizing a distinction from pre-Darwinian physiology (use versus activity) to identify an appropriate meaning of ‘function’. This account is directly applicable to molecular developmental biology and shares a connection to the theme (...) of hierarchy in homology. I situate ‘homology of function’ within existing definitions and criteria for structural assessments of homology, and introduce a criterion of ‘organization’ for judging function homologues, which focuses on hierarchically interconnected interdependencies (similar to relative position and connection for skeletal elements in structural homology). This analysis of biological concepts has at least three broad philosophical consequences: (1) it provides the grounds for the study of behavior and psychological categories as homologues; (2) it demonstrates that philosophers who take selected effect function as primary effectively ignore large portions of comparative, structural, and experimental research, thereby misconstruing biological reasoning and knowledge; and, (3) it underwrites causal generalizations, which illuminates inferences made from model organisms in experimental biology. (shrink)

Intertheoretic relations are an important topic in the philosophy of science. However, since their classical discussion by Ernest Nagel, such relations have mostly been restricted to relations between pairs of theories in the natural sciences. This paper presents a case study of a new type of intertheoretic relation that is inspired by Montague’s analysis of the linguistic syntax-semantics relation. The paper develops a simple model of this relation. To motivate the adoption of our new model, we show that this model (...) extends the scope of application of the Nagelian models and that it shares the epistemological advantages of the Nagelian model. The latter is achieved in a Bayesian framework. (shrink)

Minimal genetic pre-formationism is defended, in that primacy is ascribed to DNA in the structuring of molecules through molecular codes. This together with the importance of such codes for stability and variation in living systems makes DNA categorically different from other causal factors. It is argued that post-transcriptional and post-translational processing in protein synthesis does not rob DNA of this structuring role. Notions of structuring causal powers that may vary in degree, of arbitrary molecular codes that are more or less (...) realized, of partial templating and of genetic information as a subspecies of mechanistic information are brought in to support this and to rival causal and semantic notions of information. It is concluded that the primacy of genes in their structuring of molecules goes together with parity between genes and non-genetic causal factors in regulation of living systems. This is seen to hold independently of the radical reconceptualization of organism cum environment that has been suggested in developmental systems theory. (shrink)

The inapplicability of variations on theory reduction in the context of genetics and their irrelevance to ongoing research has led to an anti-reductionist consensus in philosophy of biology. One response to this situation is to focus on forms of reductive explanation that better correspond to actual scientific reasoning (e.g. part–whole relations). Working from this perspective, we explore three different aspects (intrinsicality, fundamentality, and temporality) that arise from distinct facets of reductive explanation: composition and causation. Concentrating on these aspects generates new (...) forms of reductive explanation and conditions for their success or failure in biology and other sciences. This analysis is illustrated using the case of protein folding in molecular biology, which demonstrates its applicability and relevance, as well as illuminating the complexity of reductive reasoning in a specific biological context. (shrink)

In this article, I reply to the preceding articles by Naomi Oreskes, Chrysostomos Mantzavinos, Brad Wray, Sarah Green, Alexander Bird, and Timothy Lyons. These articles contain a number of objections and suggestions concerning systematicity theory, as developed in my book ystematicity: The Nature of Science.

Laudan famously argued that neither truth nor approximate truth can be part of an explanation of a scientific theory's predictive success because in the history of science there were theories that enjoyed some limited success but now are considered outright false. The power of his argument lay in the many historic examples he listed . Realists have disputed that all theories on Laudan's list can be regarded as predictively successful but let's suppose momentarily that at least some exist that support (...) his point. In this case, there are historic false theories that made some true predictions. We are aiming for a general explanation of a theory's predictive success and hence these false theories necessarily are on board. Now, Laudan also argues that historic false theories – the downright false ones and the ones we nowadays judge as strictly false, but approximately true – do not in any principled sense differ from the ones we presently accept. These theories seem to present a good basis for inductively inferring that any presently accepted scientific theory is actually false. Many presently accepted theories certainly are predictively successful to a great extent and certainly their success should be covered by an acceptable explanation. Since there seems to be inductive support for the conclusion that …. (shrink)

El presente escrito se propone presentar el modelo de tres niveles del conocimiento científico (datos, fenómenos y teorías) desde los recientes resultados de la filosofía de la ciencia obtenidos por dos perspectivas filosóficas particulares: el nuevo experimentalismo y el enfoque semántico de las teorías. Para ello contrasta los planteamientos principales de estas dos perspectivas con el modelo de dos niveles del conocimiento científico (observación y teoría) del positivismo lógico, que dominó el panorama de la filosofía de la ciencia en las (...) primeras seis décadas del siglo XX. (shrink)

Martin Jones has criticized my account of the methodology of experimental economics on three points: the impossibility of testing external validity claims in the laboratory, my reconstruction of external validity inferences as analogical arguments, and the distinction between laboratory and non-laboratory sciences. I defend my account here and try to eliminate some misunderstandings that may have prompted Jones’s criticism.

Philosophical discussions of biological classification have failed to recognise the central role of homology in the classification of biological parts and processes. One reason for this is a misunderstanding of the relationship between judgments of homology and the core explanatory theories of biology. The textbook characterisation of homology as identity by descent is commonly regarded as a definition. I suggest instead that it is one of several attempts to explain the phenomena of homology. Twenty years ago the ‘new experimentalist’ movement (...) in philosophy of science drew attention to the fact that many experimental phenomena have a ‘life of their own’: the conviction that they are real is not dependent on the theories used to characterise and explain them. I suggest that something similar can be true of descriptive phenomena, and that many homologies are phenomena of this kind. As a result the descriptive biology of form and function has a life of its own—a degree of epistemological independence from the theories that explain form and function. I also suggest that the two major ‘homology concepts’ in contemporary biology, usually seen as two competing definitions, are in reality complementary elements of the biological explanation of homology. (shrink)

Dobzhansky argued that biology only makes sense if life on earth has a shared history. But his dictum is often reinterpreted to mean that biology only makes sense in the light of adaptation. Some philosophers of science have argued in this spirit that all work in ‘proximal’ biosciences such as anatomy, physiology and molecular biology must be framed, at least implicitly, by the selection histories of the organisms under study. Others have denied this and have proposed non-evolutionary ways in which (...) biologists can frame these investigations. This paper argues that an evolutionary perspective is indeed necessary, but that it must be a forward-looking perspective informed by a general understanding of the evolutionary process, not a backward-looking perspective informed by the specific evolutionary history of the species being studied. Interestingly, it turns out that there are aspects of proximal biology that even a creationist cannot study except in the light of a theory of their effect on future evolution. (shrink)

We outline three very different concepts of the gene—instrumental, nominal, and postgenomic. The instrumental gene has a critical role in the construction and interpretation of experiments in which the relationship between genotype and phenotype is explored via hybridization between organisms or directly between nucleic acid molecules. It also plays an important theoretical role in the foundations of disciplines such as quantitative genetics and population genetics. The nominal gene is a critical practical tool, allowing stable communication between bioscientists in a wide (...) range of fields grounded in well-defined sequences of nucleotides, but this concept does not embody major theoretical insights into genome structure or function. The post-genomic gene embodies the continuing project of understanding how genome structure supports genome function, but with a deflationary picture of the gene as a structural unit. This final concept of the gene poses a significant challenge to conventional assumptions about the relationship between genome structure and function, and between genotype and phenotype. (shrink)

I defend the view that many biological categories are defined by homology against a series of arguments designed to show that all biological categories are defined, at least in part, by selected function. I show that categories of homology are `abnormality inclusive'—something often alleged to be unique to selected function categories. I show that classifications by selected function are logically dependent on classifications by homology, but not vice-versa. Finally, I reject the view that biologists must use considerations of selected function (...) to abstract away from variation and pathology to form a canonical description of a class of biological systems. (shrink)

This paper argues that scale-dependence of physical and biological processes offers resistance to reductionism and has implications that support a specific kind of downward causation. I demonstrate how insights from multiscale modeling can provide a concrete mathematical interpretation of downward causation as boundary conditions for models used to represent processes at lower scales. The autonomy and role of macroscale parameters and higher-level constraints are illustrated through examples of multiscale modeling in physics, developmental biology, and systems biology. Drawing on these examples, (...) I defend the explanatory importance of constraining relations for understanding the behavior of biological systems. (shrink)