Scientific and medical practice both relate to and differ from each other, as do discussions of how to handle decisions under uncertainty in the laboratory and clinic respectively. While studies of science have pointed out that scientific practice is more complex and messier than dominant conceptions suggest, medical practice has looked to the rigour of scientific and statistical methods to address clinical uncertainty. In this article, we turn to epistemological studies of the laboratory to highlight how clinical practice already has (...) strategies for dealing with messiness. We draw on Hans-Jo ̈rg Rheinberger’s Toward a History of Epistemic Things, in which he invokes the metaphor of a spider’s web to explain the role of tacit practices in experimental biochemistry for helping practitioners manage messiness. We argue that diagnostic practices in clinical medicine employ similar, albeit codified, procedures to evaluate epistemic significance, ensure sensitivity to the unforeseen, and allow focused grounds for action. We consider three practices: (a) the pre-set structure of medical records, ensuring broad coverage in initial anamnesis, (b) the use of lists of differential diagnoses and ongoing ‘anchoring and adjusting’ as inquiry progresses, and (c) shared decision-making as an occasion to synthesize empirical evidence and reopen inquiry for potential missed information. We end by suggesting that while philosophy of medicine may learn from laboratory epistemology, the sciences may learn something from medical practice. (shrink)

Much has been written on Newton’s concept of matter, as well as Newton’s laws. Meanwhile, the metaphysical and epistemological relationships between these two principal features of Newtonian philosophy are relatively unexplored. Among the existing accounts of the relationship between bodies and laws, two are especially compelling: the “law-constitutive” approach from Katherine Brading and the “formal-cause” approach from Zvi Biener and Eric Schliesser. Both accounts argue that Newton’s bodies are (at least partially) metaphysically dependent on the laws. That is, according to (...) Brading, Biener, and Schliesser, Newtonian laws are ontologically prior to the bodies they govern. In this article, I reply to Brading, Biener, and Schliesser. I develop negative arguments against their approaches by closely examining three features of Newton’s ontology—forms, particles, and active principles—and their relationship to the laws. I also offer an explanation as to why “law-first” approaches have (understandably) dominated recent Newtonian exegesis. Finally, I present a positive argument for what I believe to be a superior alternative: Newton’s law-first approach is purely pragmatic. It is a feature of his experimental philosophy that we should begin our inquiry into the metaphysical constituents of the universe with the laws that arise from them. This does not entail, however, that Newton views laws as constituting or forming physical bodies. Newton’s metaphysics should be interpreted as “matter-first,” with bodies giving rise to laws, whereas his epistemology should be interpreted as “law-first,” with knowledge of laws giving rise to knowledge of bodies. (shrink)

Scientific research is constrained by limited resources, so it is imperative that it be conducted efficiently. This paper introduces the notion of epistemic expression, a kind of representation that expedites the solution of research problems. Epistemic expressions are representations that (i) contain information in a way that enables more reliable information to place the most stringent constraints on possible solutions and (ii) make new information readily extractible by biasing the search through that space. I illustrate these conditions using historical and (...) contemporary examples of biomolecular structure determination. Then, I argue that the notion of epistemic expression parts ways with pragmatic accounts of scientific representation and an understanding of models as artifacts, neither of which require models to accurately represent. Explicating epistemic expression thus fills a gap in our understanding of scientific practice, extending Morrison and Morgan's (1999) conception of models as investigative instruments. (shrink)

Dilthey frequently recognizes anthropology as a foundational science of human nature and as a cornerstone in the system of the human sciences. While much has been written about Dilthey’s “philosophical anthropology,” relatively little attention has been paid to his views on the emerging empirical science of anthropology. This paper examines Dilthey’s relation to the new discipline by focusing on his reception of its leading German representatives. Using his book reviews, essays, and drafts for Introduction to the Human Sciences from the (...) 1860s-70s, it highlights the influence of the new anthropology on his earliest attempts to elaborate the foundations of the Geisteswissenschaften. It argues that anthropology was a key source for some of the naturalistic features of Dilthey’s philosophy, and that it pulled him in a direction contrary to the historicist hermeneutics of his teachers. (shrink)

According to contemporary computational neuroscience the mental is associated with computations implemented in the brain. We analyze in physical terms based on recent results in the foundations of statistical mechanics two well-known (independent) problems that arise for this approach: the problem of multiple-computations and the problem of multiple-realization. We show that within the computational theory of the mind the two problems are insoluble by the physics of the brain. We further show that attempts to solve the problems by the interactions (...) of the systems implementing the computations with an environment (in or outside the brain) must introduce non- physical factors, and therefore fail on physical grounds. We also show that the problems are endemic and pertain to other forms of functional theories of the mind, most notably, causal functionalism. Finally, we propose a physicalist reductive identity theory, which is a generalization of statistical mechanics for all the special sciences, and show that only a theory of this kind can provide physical solutions to the above two problems in computational neuroscience. We conclude that functionalism in the theory of mind must be replaced with a reductive identity theory. This result has far-reaching implications with respect to the research programs in brain science. (shrink)

In this paper I extend the case for a necessitation account of particular laws in Kant's philosophy of science by examining the relation between reason's hypothetical use in the Appendix to the Transcendental Dialectic and the legitimate hypotheses identified in the Doctrine of Method. Building on normative accounts of reason's ideas, I argue that reason's hypothetical use does not describe the connections between objects and their grounds, which lie beyond the reach of the understanding, but merely prescribes the relations between (...) appearances and their conditions, for which the understanding must seek. A legitimate hypothesis, I suggest, is a proposition we hold to be true that fills in one or several of those relations. The problematic character of hypotheses requires that we evaluate our reasons for holding them to be true. While natural modality is grounded in the nature of things, which cannot be fully known, our reasons for assent can and must be grounded on features of objects that are epistemically available to us. (shrink)

We discuss two influential views of unification: mutual information unification (MIU) and common origin unification (COU). We propose a simple probabilistic measure for COU and compare it with Myrvold’s (2003, 2017) probabilistic measure for MIU. We then explore how well these two measures perform in simple causal settings. After highlighting several deficiencies, we propose causal constraints for both measures. A comparison with explanatory power shows that the causal version of COU is one step ahead in simple causal settings. However, slightly (...) increasing the complexity of the underlying causal structure shows that both measures can easily disagree with explanatory power. The upshot of this is that even sophisticated causally constrained measures for unification ultimately fail to track explanatory relevance. This shows that unification and explanation are not as closely related as many philosophers thought. (shrink)

We argue that the asymmetry between diverging and converging electromagnetic waves is just one of many asymmetries in observed phenomena that can be explained by a past hypothesis and statistical postulate (together assigning probabilities to different states of matter and field in the early universe). The arrow of electromagnetic radiation is thus absorbed into a broader account of temporal asymmetries in nature. We give an accessible introduction to the problem of explaining the arrow of radiation and compare our preferred strategy (...) for explaining the arrow to three alternatives: (i) modifying the laws of electromagnetism by adding a radiation condition requiring that electromagnetic fields always be attributable to past sources, (ii) removing electromagnetic fields and having particles interact directly with one another through retarded action-at-a-distance, (iii) adopting the Wheeler-Feynman approach and having particles interact directly through half-retarded half-advanced action-at-a-distance. In addition to the asymmetry between diverging and converging waves, we also consider the related asymmetry of radiation reaction. (shrink)

A central goal of the scientific endeavor is to explain phenomena. Scientists often attempt to explain a phenomenon by way of representing it in some manner—such as with mathematical equations, models, or theory—which allows for an explanation of the phenomenon under investigation. However, in developing scientific representations, scientists typically deploy simplifications and idealizations. As a result, scientific representations provide only partial, and often distorted, accounts of the phenomenon in question. Philosophers of science have analyzed the nature and function of how (...) scientists construct representations, deploy idealizations, and provide explanations. As such, our aim in this special issue is to bring these three pillars of research into closer contact with the contributions to it focusing on three main themes. -/- The first set of papers, Alan Baker (2021) and Marc Lange (2021), address mathematical explanations in science. Baker (2021), a proponent of mathematical Platonism, examines its capacity to evade the critique that the so-called Enhanced Indispensability Argument is circular. Lange (2021) examines distinctively mathematical explanations, arguing that neither Platonism nor representationalism are successful paths, and instead argues in favor of Aristotelian realism. A second theme emerging from the papers in this special issue is the impact that various conceptualizations of idealization have on our abilities to offer scientific explanations, to pro- duce an analysis of what explanations are or should be, and to understand scientific representation. Peter Tan (2021) suggests amending inferentialist accounts of scientific representation to account for inconsistent idealizations. Michael Strevens (2021) advocates a view wherein the introduction of idealizations into a model is legitimate so long as it pertains to non-difference-making factors, arguing for a logical reading of the notion of difference-making. Natalia Carrillo and Tarja Knuuttila (2022) offer an alternative account to the idealization-as-distortion view, emphasizing instead the holistic nature of idealization. -/- Finally, contributions by Carrillo and Knuuttila (2022), Terzian (2021), Valente (2021), and Rodriguez (2021) illustrate how issues regarding idealization, representation, and explanation are applied to specific contexts and across various sciences. Carrillo and Knuuttila (2022) examine conceptions of idealization in the context of models of the nerve impulse. Giulia Terzian (2021) extends the discussion of idealizations to the context of generative linguistics. Giovanni Valente (2021) examines how idealizations and evaluations of accurate representation impact capacities to explain phenomena in the context of statistical thermodynamics. Quentin Rodriguez (2021) examines the role of idealizations and analogies in various strategies to explain critical phenomena. -/- In what follows, we offer a brief overview of important philosophical issues connected to representation, idealization, and explanation in science. We then provide short summaries of the eight papers in this special issue. (shrink)

‘History will be kind to me, for I intend to write it,’ Winston Churchill is famously said to have quipped. That he never seems to have actually made this comment is beside the point, since the message is important: past events never speak for themselves. Facts do not settle like rocks in a dry river, but are moved, displaced, and replaced by waters that continue to gush. The currents and their temperates are sensetative to mores, signs of their times. And (...) the keepers of the waters, more often than not, are historians. This special issue is devoted to new directions in the historiography of genetics, a field that has seen particularly lively drift, swirl, and surge in recent decades. (shrink)

Scientific models typically contain idealizations, or assumptions that are known not to be true. Philosophers have long questioned the nature of idealizations: Are they heuristic tools that will be abandoned? Or rather fictional representations of reality? And how can we reconcile them with realism about knowledge of nature? Immanuel Kant developed an account of scientific investigation that can inspire a new approach to the contemporary debate. Kant argued that scientific investigation is possible only if guided by ideal assumptions—what he calls (...) “regulative ideas”. These ideas are not true of objects of nature, and yet they are not heuristic tools or fictional represen- tations. They are necessary rules governing the construction and assessment of scientific explanations. In this paper, I suggest that some idealizations can be interpreted as having necessary regulative value and as being compatible with scientific realism. I first analyze the puzzle of the nature of idealization and present the main approaches to this topic in the literature. Second, I reconsider the puzzle vis-a-vis a restricted, Kantian definition of idealization and a novel characterization of the relation between idealization and truth. Finally, I discuss in detail an example of idealization (the Hardy-Weinberg equilibrium) along the suggested Kantian lines. (shrink)

Biologists often study particular biological systems as models of a phenomenon of interest even if they already know that the phenomenon is produced by diverse mechanisms and hence none of those systems alone can sufficiently represent it. To understand this modeling practice, the present paper provides an account of how multiple model systems can be used to study a phenomenon that is produced by diverse mechanisms. Even if generalizability of results from a single model system is significantly limited, generalizations concerning (...) specific aspects of mechanisms often hold across certain ranges of biological systems, which enables multiple model systems to jointly represent such a phenomenon. Comparing mechanisms that operate in different biological systems as examples of the same phenomenon also facilitates characterization and investigation of individual mechanisms. I also compare my account with two existing accounts of the use of multiple model systems and argue that my account is distinct from and complementary to them. (shrink)

While the pursuitworthiness of philosophical ideas has changed over time, philosophical practice and methodology have not kept pace. The worthiness of a philosophical pursuit includes not only the ideas and objectives one pursues but also the methods with which one pursues them. In this paper, we articulate how empirical approaches benefit philosophy of science, particularly advocating for the use of qualitative methods for understanding the social and normative aspects of scientific inquiry. After situating qualitative methods within empirical philosophy of science, (...) we discuss how to adapt these traditionally sociological methods to empirically inform philosophical questions. Our aim is to normalize and legitimize qualitative methods for philosophical purposes and discuss how they can elucidate descriptive and normative components of scientific practice in a more generalizable non-idealized manner. We contend that qualitative methods are particularly well suited to philosophical interest in the social norms of science, their achievability, and their mutability. Furthermore, unlike more historical case studies in philosophy, qualitative methods enable more confidence in generalizability, albeit limited, from a concrete sample to a larger class. We conclude by addressing anxieties about the distinctness of empirical philosophy of science from social epistemology and from sociology of science. (shrink)

This article adopts a bottom-up approach to theory interpretation, following the slogan “meaning is use”, and applies it to quantum mechanics. I argue that it fits very well with the Consistent Histories formulation of quantum mechanics, interpreted in a particular way that is not the interpretation favoured by original proponents of the formulation. I examine the difficulties and advantages of this interpretation.

Recently, Dewar (2019) has suggested that one can apply the strategy of 'sophistication' - as exemplified by sophisticated substantivalism as a response to the diffeomorphism invariance of General Relativity - to gauge theories such as electrodynamics. This requires a shift to the formalism of fibre bundles. In this paper, I develop and defend this suggestion. Where my approach differs from previous discussions is that I focus on the metaphysical picture underlying the fibre bundle formalism. In particular, I aim to affirm (...) the physical reality of gauge properties. I argue that this allows for a local and separable explanation of the Aharonov-Bohm effect. Its puzzling features are explained by a form of holism inherent to fibre bundles. (shrink)

The aim of this virtual special issue is to bring together philosophical and historical perspectives to address long-standing issues in the interpretation, utility, and impacts of quantitative genetics methods and findings. Methodological approaches and the underlying scientific understanding of genetics and heredity have transformed since the field's inception. These advances have brought with them new philosophical issues regarding the interpretation and understanding of quantitative genetic results. The contributions in this issue demonstrate that there is still work to be done integrating (...) old and new methodological and conceptual frameworks. In some cases, new results are interpreted using assumptions based on old concepts and methodologies that need to be explicitly recognised and updated. In other cases, new philosophical tools can be employed to synthesise historical quantitative genetics work with modern methodologies and findings. This introductory article surveys three general themes that have dominated philosophical discussion of quantitative genetics throughout history: (1) how methodologies have changed and transformed our knowledge and interpretations; (2) whether or not quantitative genetics can offer explanations relating to causation and prediction; and (3) the importance of defining the phenotypes under study. We situate the contributions in this virtual special issue within a historical framework addressing these three themes. (shrink)

The description of how individual models in families of models are related to each other is crucial for the general philosophical understanding of model-based scientific practice. We focus on the Capital Asset Pricing Models (CAPM) family, a cornerstone in financial economics, to provide a descriptive analysis of model relations within a family. We introduce the concepts of theoretical and empirical complementarity to characterise model relations. Our complementarity analysis of model relations has two types of payoff. Specifically regarding the CAPM, our (...) analysis reveals why this model family, which has been empirically contested, has yet remained popular and important: the different models that have been added over time have made new empirically and theoretically complementary contributions to the model family. More generally, our analysis reveals the dynamic character of model-based scientific practice. Characterising relations between models as theoretically and/or empirically complementary yields three hitherto underappreciated lessons: (i) actual modelling purposes are not always primarily epistemic, (ii) a model's individual import is relative, not absolute, and (iii) there is an important interplay between theory and data models. Faithfully characterising scientific modelling in this way facilitates subsequent analysis of models' epistemic import. (shrink)