Completeness is an important but misunderstood norm of explanation. It has recently been argued that mechanistic accounts of scientific explanation are committed to the thesis that models are complete only if they describe everything about a mechanism and, as a corollary, that incomplete models are always improved by adding more details. If so, mechanistic accounts are at odds with the obvious and important role of abstraction in scientific modelling. We respond to this characterization of the mechanist’s views about abstraction and (...) articulate norms of completeness for mechanistic explanations that have no such unwanted implications. _1_ Introduction _2_ A Balancing Act: When Do Details Matter? _3_ The Norms of Causal Explanation _4_ The Norms of Constitutive Explanation _5_ Salmon-Completeness _6_ From More Details to More Relevant Details _7_ Non-explanatory Virtues of Abstraction _8_ From Explanatory Models to Explanatory Knowledge _9_ Mechanistic Completeness Reconsidered _10_ Conclusion. (shrink)

The success of a piece of behaviour is often explained by its being caused by a true representation (similarly, failure falsity). In some simple organisms, success is just survival and reproduction. Scientists explain why a piece of behaviour helped the organism to survive and reproduce by adverting to the behaviour’s having been caused by a true representation. That usage should, if possible, be vindicated by an adequate naturalistic theory of content. Teleosemantics cannot do so, when it is applied to simple (...) representing systems (Godfrey-Smith 1996). Here it is argued that the teleosemantic approach to content should therefore be modified, not abandoned, at least for simple representing systems. The new ‘infotel-semantics’ adds an input condition to the output condition offered by teleosemantics, recognising that it is constitutive of content in a simple representing system that the tokening of a representation should correlate probabilistically with the obtaining of its specific evolutionary success condition. (shrink)

We reconstruct genetic determinism as a reductionist thesis to the effect that the molecular properties of cells can be accounted for to a great extent by their genetic outfit. The non-reductionist arguments offered at this molecular level often use the relationship between structure and function as their point of departure. By contrast, we develop a non-reductionist argument that is confined to the structural characteristics of biomolecules; no appeal to functions is made. We raise two kinds of objections against the reducibility (...) claim underlying genetic determinism. First, some conceptual distinctions at the protein level cannot be captured on a genetic basis. A one-to-many relationship between DNA sequences and proteins emerges from them. Second, the relationship between genes and proteins is characterized by explanatory loops or reciprocal explanatory dependence. The presence of proteins is explained by the transcription from corresponding DNA sequences, and the latter is in turn accounted for by the action of proteins. By contrast, a reductive account requires a unidirectional explanatory dependence. (shrink)

http://dx.doi.org/10.5007/1808-1711.2012v16n1p53 Ratifying Haeckel and contradicting Kant’s negative prophesy, in this paper I try to show that Darwin was, really, the Newton of the blade of grass . Darwin showed how the configurations according to goals of the living beings, could be explained from a naturalistic point of view, without having to postulate the existence of an intentional agent that had arranged or prearranged then. This achievement, nevertheless, was obtained by a way that Kant could not foresee and that Haeckel could (...) not understand: Darwin came there showing that there was more natural science than that Newton, Kant and Haeckel could conceive. (shrink)

The discussion presents a framework of concepts that is intended to account for the rationality of semantic change and variation, suggesting that each scientific concept consists of three components of content: 1) reference, 2) inferential role, and 3) the epistemic goal pursued with the concept’s use. I argue that in the course of history a concept can change in any of these components, and that change in the concept’s inferential role and reference can be accounted for as being rational relative (...) to the third component, the concept’s epistemic goal. This framework is illustrated and defended by application to the history of the gene concept. It is explained how the molecular gene concept grew rationally out of the classical gene concept despite a change in reference, and why the use and reference of the contemporary molecular gene concept may legitimately vary from context to context. (shrink)

Whereas an inference (deductive as well as inductive) is usually viewed as being valid in virtue of its argument form, the present paper argues that scientific reasoning is material inference, i.e., justified in virtue of its content. A material inference is licensed by the empirical content embodied in the concepts contained in the premises and conclusion. Understanding scientific reasoning as material inference has the advantage of combining different aspects of scientific reasoning, such as confirmation, discovery, and explanation. This approach explains (...) why these different aspects (including discovery) can be rational without conforming to formal schemes, and why scientific reasoning is local, i.e., justified only in certain domains and contingent on particular empirical facts. The notion of material inference also fruitfully interacts with accounts of conceptual change and psychological theories of concepts. (shrink)

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

This essay discusses Elliott Sober’s Evidence and Evolution: The Logic Behind the Science. Valuable to both philosophers and biologists, Sober analyzes the testing of different kinds of evolutionary hypotheses about natural selection or phylogenetic history, including a thorough critique of intelligent design. Not at least because of a discussion of different schools of hypothesis testing (Bayesianism, likelihoodism, and frequentism), with Sober favoring a pluralism where different inference methods are appropriate in different empirical contexts, the book has lessons for philosophy of (...) science beyond its evolutionary focus. I criticize Sober for not including epistemic values and social aspects of scientific practice in his epistemological framework. (shrink)

David Chalmers and Frank Jackson have promoted a strong program of conceptual analysis, which accords a significant philosophical role to the a priori analysis of concepts. They found this methodological program on an account of concepts using two-dimensional semantics. This paper argues that Chalmers and Jackson’s account of concepts, and the related approach by David Braddon-Mitchell, is inadequate for natural kind concepts as found in biology. Two-dimensional semantics is metaphysically faulty as an account of the nature of concepts and concept (...) possession. It is also methodologically flawed as a guideline for how to study scientific concepts. Proponents of two-dimensional semantics are criticized for not taking seriously semantic variation between persons and for failing to adequately account for the rationality of semantic change. I suggest a more pragmatic approach to natural kind term meaning, arguing that the epistemic goal pursued by a term’s use is an additional semantic property. (shrink)

Computer simulations and experiments share many important features. One way of explaining the similarities is to say that computer simulations just are experiments. This claim is quite popular in the literature. The aim of this paper is to argue against the claim and to develop an alternative explanation of why computer simulations resemble experiments. To this purpose, experiment is characterized in terms of an intervention on a system and of the observation of the reaction. Thus, if computer simulations are experiments, (...) either the computer hardware or the target system must be intervened on and observed. I argue against the first option using the non-observation argument, among others. The second option is excluded by e.g. the over-control argument, which stresses epistemological differences between experiments and simulations. To account for the similarities between experiments and computer simulations, I propose to say that computer simulations can model possible experiments and do in fact often do so. (shrink)

Abstract Although noting the importance of organization in mechanisms, the new mechanistic philosophers of science have followed most biologists in focusing primarily on only the simplest mode of organization in which operations are envisaged as occurring sequentially. Increasingly, though, biologists are recognizing that the mechanisms they confront are non-sequential and the operations nonlinear. To understand how such mechanisms function through time, they are turning to computational models and tools of dynamical systems theory. Recent research on circadian rhythms addressing both intracellular (...) mechanisms and the intercellular networks in which these mechanisms are synchronized illuminates this point. This and other recent research in biology shows that the new mechanistic philosophers of science must expand their account of mechanistic explanation to incorporate computational modeling, yielding dynamical mechanistic explanations. Developing such explanations, however, is a challenge for both the scientists and the philosophers as there are serious tensions between mechanistic and dynamical approaches to science, and there are important opportunities for philosophers of science to contribute to surmounting these tensions. Content Type Journal Article Category Original paper in Philosophy of Science Pages 1-16 DOI 10.1007/s13194-012-0046-x Authors William Bechtel, Department of Philosophy, Center for Chronobiology, and Science Studies Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0119, USA Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912. (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)

Waters’s (2007) actual difference making and Weber’s (2013, 2017) biological normality approaches to causal selection have received many criticisms, some of which miss their target. Disagreement about whether Waters’s and Weber’s views succeed in providing criteria that uniquely singles out the gene as explanatorily significant in biology has led philosophers to overlook a prior problem. Before one can address whether Waters’s and Weber’s views successfully account for the explanatory significance of genes, one must ask whether either view satisfactorily meets the (...) necessary conditions for causal selection in the first place. An adequate defense of causal selection must meet two desiderata. First, there must be an explanatory property that sets some causes apart from others. Second, the property identified must be one that is recognized by biologists as relevant to their domain(s) of inquiry. I argue that both fall short of meeting the second condition. I demonstrate this by showing how many of the biological technologies crucial to experimentation do not fit either view very well. I offer a more adequate proposal that accommodates non-actual and artificial causal variables. A consequence of my view is the following: When analyzing the causal selection practices of biologists, philosophers should consider the explanatory targets relevant to a research program – including ones whose explanans must appeal to biological technologies. I then explain how this proposal can inform the existing debate between Weber (2017) and Griffiths et al. (2015). (shrink)

Kenneth Waters and Marcel Weber argue that the joint use of distinct gene concepts and the transfer of knowledge between classical and molecular analyses in contemporary scientific practice is possible because classical and molecular concepts of the gene refer to overlapping chromosomal segments and the DNA sequences associated with these segments. However, while pointing in the direction of coreference, both authors also agree that there is a considerable divergence between the actual sequences that count as genes in classical genetics and (...) molecular biology. The thesis advanced in this paper is that the referents of classical and molecular gene concepts are coextensive to a higher degree than admitted by Waters and Weber, and therefore coreference can provide a satisfactory account of the high level of integration between classical genetics and molecular biology. In particular, I argue that the functional units/cistrons identified by classical techniques overlap with functional elements entering the composition of molecular transcription units, and that the precision of this overlap can be improved by conducting further experimentation. (shrink)

Multidisciplinary models aggregating ‘lower-level’ biological and ‘higher-level’ psychological and social determinants of a phenomenon raise a puzzle. How is the interaction between the physical, the psychological and the social conceptualized and explained? Using biopsychosocial models of pain as an illustration, I argue that these models are in fact level-neutral compilations of empirical findings about correlated and causally relevant factors, and as such they neither assume, nor entail a conceptual or ontological stratification into levels of description, explanation or reality. If inter-level (...) causation is deemed problematic or if debates about the superiority of a particular level of description or explanation arise, these issues are fueled by considerations other than empirical findings. (shrink)

Examples from the sciences showing that mechanisms do not always succeed in producing the phenomena for which they are responsible have led some authors to conclude that the regularity requirement can be eliminated from characterizations of mechanisms. In this article, I challenge this conclusion and argue that a minimal form of regularity is inextricably embedded in examples of elucidated mechanisms that have been shown to be causally responsible for phenomena. Examples of mechanistic explanations from the sciences involve mechanisms that have (...) been shown to produce phenomena with a reproducible rate of success. By contrast, if phenomena are infrequent to the point that they amount to irreproducible observations and experimental results, they are indistinguishable from the background noise of accidental happenings. The inability to detect or measure the phenomenon of interest against the background noise of accidental correlations makes it impossible to elucidate a mechanism by experimental means, to demonstrate that a proposed mechanism actually produces the phenomenon, and ultimately to justify why a hypothetical scenario involving an irregular mechanism should be preferred over attributing irreproducible happenings to chance. (shrink)

ArgumentWe examine the criteria used to validate the use of nonhuman organisms in North-American alcohol addiction research from the 1950s to the present day. We argue that this field, where the similarities between behaviors in humans and non-humans are particularly difficult to assess, has addressed questions of model validity by transforming the situatedness of non-human organisms into an experimental tool. We demonstrate that model validity does not hinge on the standardization of one type of organism in isolation, as often the (...) case with genetic model organisms. Rather, organisms are viewed as necessarily situated: they cannot be understood as a model for human behavior in isolation from their environmental conditions. Hence the environment itself is standardized as part of the modeling process; and model validity is assessed with reference to the environmental conditions under which organisms are studied. (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)

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)