The aim of this work is to elucidate John F. W. Herschel’s distinctive contribution to nineteenth-century British inductivism by exploring his understanding of experimental methods. Drawing on both his explicit discussion of experiment in his Preliminary Discourse on Natural Philosophy and his published account of experiments he conducted in the domain of electromagnetism, I argue that the most basic principle underlying Herschel’s epistemology of experiment is that experiment enables a particular kind of lower-level experimental understanding of phenomena. Experimental practices provide (...) knowledge of a particular phenomenon as a genuine effect produced under precise material conditions whose connections with other phenomena can be traced by variations in experimental parameters. The orienting concern of experimental inquiry seems to be the production of a secure understanding of phenomena even if it has no direct theoretical significance. Insofar as one can generate this lower-level understanding, it can function both as a fertile source for explanatory principles about phenomena and as a body of evidence against which one can test the adequacy of an explanatory hypothesis. This project provides evidence of Herschel’s inductivism not merely as a philosophical approach to understanding the sciences but as a methodological commitment in scientific practice. (shrink)

Big data biology—bioinformatics, computational biology, systems biology (including ‘omics’), and synthetic biology—raises a number of issues for the philosophy of science. This article deals with several such: Is data-intensive biology a new kind of science, presumably post-reductionistic? To what extent is big data biology data-driven? Can data ‘speak for themselves?’ I discuss these issues by way of a reflection on Carl Woese’s worry that “a society that permits biology to become an engineering discipline, that allows that science to slip into (...) the role of changing the living world without trying to understand it, is a danger to itself.” And I argue that scientific perspectivism, a philosophical stance represented prominently by Giere, Van Fraassen, and Wimsatt, according to which science cannot as a matter of principle transcend our human perspective, provides the best resources currently at our disposal to tackle many of the philosophical issues implied in the modeling of complex, multilevel/multiscale phenomena. (shrink)

Although “theory” has been the prevalent unit of analysis in the meta-study of science throughout most of the twentieth century, the concept remains elusive. I further explore the leitmotiv of several authors in this issue: that we should deal with theorizing (rather than theory) in biology as a cognitive activity that is to be investigated naturalistically. I first contrast how philosophers and biologists have tended to think about theory in the last century or so, and consider recent calls to upgrade (...) the role of theory in the life sciences against the background of the recent “data deluge” in molecular biology, systems biology, etc. I then review thinking about theory in biology in relation to physical theory as a positive or negative exemplar. I conclude by discussing various aspects of a positive program for “naturalizing theorizing.”. (shrink)

This is the second of two articles in which I reflect on “generalized Darwinism” as currently discussed in evolutionary economics. In the companion article (Callebaut, Biol Theory 6. doi: 10.1007/s13752-013-0086-2, 2011, this issue) I approached evolutionary economics from the naturalistic perspectives of evolutionary epistemology and the philosophy of biology, contrasted evolutionary economists’ cautious generalizations of Darwinism with “imperialistic” proposals to unify the behavioral sciences, and discussed the continued resistance to biological ideas in the social sciences. Here I assess Generalized Darwinism (...) as propounded by Geoffrey Hodgson, Thorbjørn Knudsen, and others, concentrating on the roles of theory and model building in science (and the roles of analogy and metaphor therein), generative replication, and the relation between selection and self-organization. I then point to advances in current biology that promise to be more fruitful as sources of inspiration for evolutionary economics than the project to generalize Darwinism in its current, “hardened Modern Synthesis” form; and I draw some conclusions. (shrink)

In recent months months the hashtag #overlyhonestmethods has steadily been gaining popularity. Posts under this hashtag—presumably by scientists—detail aspects of daily scientific research that differ considerably from the idealized interpretation of scientific experimentation as standardized, objective and reproducible. Over and above its entertainment value, the popularity of this hashtag raises two important points for those who study both science and scientists. Firstly, the posts highlight that the generation of data through experimentation is often far less standardized than is commonly assumed. (...) Secondly, the popularity of the hashtag together with its relatively blasé reception by the scientific community reveal that the actions reported in the tweets are far from shocking and indeed may be considered just “part of scientific research”. Such observations give considerable pause for thought, and suggest that current conceptions of data might be limited by failing to recognize this “inherent variability” within the actions of generation—and thus within data themselves. Is it possible, we must ask, that epistemic virtues such as standardization, consistency, reportability and reproducibility need to be reevaluated? Such considerations are, of course, of particular importance to data sharing discussions and the Open Data movement. This paper suggests that the notion of a “moral professionalism” for data generation and sharing needs to be considered in more detail if the inherent variability of data are to be addressed in any meaningful manner. (shrink)

In the fall of 2018 Jiankui He shocked the international community with the following announcement: two female babies, “Lulu” and “Nana,” whose germlines had been modified by the cutting edge, yet profoundly unsafe CRISPR-Cas9 technology had been born. This event galvanized policy makers and scientists to advocate for more explicit and firm regulation of human germline gene editing. Recent policy proposals attempt to integrate safety considerations and public input to identify specific types of diseases that may be safe targets for (...) human GGE. This paper argues these policy proposals are inadequate in different ways. While Sarkar intends to incorporate input from the disability community for the purpose of deciding the value of human GGE, I argue that his strategy for doing so is inadequate. I’ll argue that an iterative, deliberative process is a more appropriate framework for allowing the disability community to inform policy on human GGE. Further policy proposals have been framed in terms of monogenetic or single-gene diseases. I argue that this way of conceptualizing disease is not what matters for deciding which disorders are viable candidates for human GGE. Instead, what matters is that the disease in question must have genes that have a high degree of causal control with respect to the disease and alternative nucleic acid sequences variants that are likely to produce traits deemed desirable must be identified. Previous policy proposals leave unspecified. What conditions must be met for satisfying condition should not be left to individual scientists to decide for themselves. The present proposal offers some guidance on this issue. (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)

The discovery of RNA interference in 1998 has made a lasting impact on biological research. Identifying the regulatory role of small RNAs changed the modes of molecular biological inquiry as well as biologists' understanding of genetic regulation. This article examines the early years of small RNA biology's success story. I query which factors had to come together so that small RNA research came into life in the blink of an eye. I primarily look at scientific repertoires as facilitators of rapid (...) scientific change. I show that for a short period of time, between the years 1998 and 2002, different model organism communities, investigative strategies, technological innovations, and research interests could be successfully aligned to take small RNA research off the ground. I discuss how the keystone discoveries were situated in specific experimental traditions and what strategies were employed to establish these discoveries as more general phenomena. Providing thus a practice-based approach of rapid scientific change, I ask how to relate the change in propositional bits of scientific knowledge with changes in scientific practice. (shrink)

We introduce a new type of pluralism about biological function that, in contrast to existing, demonstrates a practical integration among the term’s different meanings. In particular, we show how to generalize Sandra Mitchell’s notion of integrative pluralism to circumstances where multiple epistemic tools of the same type are jointly necessary to solve scientific problems. We argue that the multiple definitions of biological function operate jointly in this way based on how biologists explain the evolution of protein function. To clarify how (...) our account relates to existing views, we introduce a general typology for monist and pluralist accounts along with standardized criteria for judging which is best supported by evidence. (shrink)

It is now well-appreciated by philosophers that contemporary large-scale ‘-omics’ studies in biology stand in non-trivial relationships to more orthodox hypothesis-driven approaches. These relationships have been clarified by Ratti (2015); however, there remains much more to be said regarding how an important field of genomics cited in that work—‘genome-wide association studies’ (GWAS)—fits into this framework. In the present article, we propose a revision to Ratti’s framework more suited to studies such as GWAS. In the process of doing so, we introduce (...) to the philosophical literature novel exploratory experiments in (phospho)proteomics, and demonstrate how these experiments interplay with the above considerations. (shrink)

The philosophical debate around the impact of machine learning in science is often framed in terms of a choice between AI and classical methods as mutually exclusive alternatives involving difficult epistemological trade-offs. A common worry regarding machine learning methods specifically is that they lead to opaque models that make predictions but do not lead to explanation or understanding. Focusing on the field of molecular biology, I argue that in practice machine learning is often used with explanatory aims. More specifically, I (...) argue that machine learning can be tightly integrated with other, more traditional, research methods and in a clear sense can contribute to insight into the causal processes underlying phenomena of interest to biologists. One could even say that machine learning is not the end of theory in important areas of biology, as has been argued, but rather a new beginning. I support these claims with a detailed discussion of a case study involving gene regulation by microRNAs. (shrink)

Philosophers of science diverge on the question what drives the growth of scientific knowledge. Most of the twentieth century was dominated by the notion that theories propel that growth whereas experiments play secondary roles of operating within the theoretical framework or testing theoretical predictions. New experimentalism, a school of thought pioneered by Ian Hacking in the early 1980s, challenged this view by arguing that theory-free exploratory experimentation may in many cases effectively probe nature and potentially spawn higher evidence-based theories. Because (...) theories are often powerless to envisage workings of complex biological systems, theory-independent experimentation is common in the life sciences. Some such experiments are triggered by compelling observation, others are prompted by innovative techniques or instruments, whereas different investigations query big data to identify regularities and underlying organizing principles. A distinct fourth type of experiments is motivated by a major question. Here I describe two question-guided experimental discoveries in biochemistry: the cyclic adenosine monophosphate mediator of hormone action and the ubiquitin-mediated system of protein degradation. Lacking underlying theories, antecedent data bases, or new techniques, the sole guides of the two discoveries were respective substantial questions. Both research projects were similarly instigated by theory-free exploratory experimentation and continued in alternating phases of results-based interim working hypotheses, their examination by experiment, provisional hypotheses again, and so on. These two cases designate theory-free, question-guided, stepwise biochemical investigations as a distinct subtype of the new experimentalism mode of scientific enquiry. (shrink)

Both scientists and philosophers of science have recently emphasized the importance of promoting transparency in science. For scientists, transparency is a way to promote reproducibility, progress, and trust in research. For philosophers of science, transparency can help address the value-ladenness of scientific research in a responsible way. Nevertheless, the concept of transparency is a complex one. Scientists can be transparent about many different things, for many different reasons, on behalf of many different stakeholders. This paper proposes a taxonomy that clarifies (...) the major dimensions along which approaches to transparency can vary. By doing so, it provides several insights that philosophers and other science studies scholars can pursue. In particular, it helps address common objections to pursuing transparency in science, it clarifies major forms of transparency, and it suggests avenues for further research on this topic. (shrink)

Explanation is a perennially hot topic in philosophy of science. Yet philosophers have exhibited a curious blind spot to the questions of how explanatory projects develop over time, as well as what processes are involved in generating their developmental trajectories. This paper examines these questions using research into the end-Permian mass extinction as a case study. It takes as its jumping-off point the observation that explanations of historical events tend to grow more complex over time, but it goes beyond this (...) observation by scrutinizing the processes responsible for generating this pattern. Surveying several decades of research into the end-Permian extinction, I suggest that the principal “driver” of explanation in geohistory is non-explanatory work: work that is undertaken to increase our descriptive understanding of a phenomenon, not to test a particular explanatory claim. Non-explanatory work drives explanation by imposing or eliminating demands on explanation, and by furnishing new resources for constructing explanatory models. Explanations grow more complex because the demands on explanation tend to increase with ongoing characterization of the target phenomenon, and characterization tends grows the roster of explanatory resources. However, the fact that non-explanatory work sometimes eliminates demands on explanation means that this trend is not irreversible. I suggest that to achieve a more rounded view of the dynamics of explanation, philosophers should ask how research into complex phenomena is organized, as well as how explanatory progress depends upon the coordination of different kinds of material and epistemic resources. (shrink)