The periodic table of the elements is one of the most powerful icons in science: a single document that captures the essence of chemistry in an elegant pattern. Indeed, nothing quite like it exists in biology or physics, or any other branch of science, for that matter. One sees periodic tables everywhere: in industrial labs, workshops, academic labs, and of course, lecture halls. It is sometimes said that chemistry has no deep ideas, unlike physics, which can boast quantum mechanics and (...) relativity, and biology, which has produced the theory of evolution. This view is mistaken, however, since there are in fact two big ideas in chemistry. They are chemical periodicity and chemical bonding, and they are deeply interconnected. The observation that certain elements prefer to combine with specific kinds of elements prompted early chemists to classify the elements in tables of chemical affinity. Later these tables would lead, somewhat indirectly, to the discovery of the periodic system, perhaps the biggest idea in the whole of chemistry. Indeed, periodic tables arose partly through the attempts by Dimitri Mendeleev and numerous others to make sense of the way in which particular elements enter into chemical bonding. (shrink)

This volume launches a new series of contemporary conversations about scientific classification. Most philosophical conversations about kinds have focused centrally or solely on natural kinds, that is, kinds whose existence is not dependent on the scientific process of synthesis. This volume refocuses conversations about classification on unnatural, or synthetic, kinds via extensive study of three paradigm cases of unnatural kinds: nanomaterials, stem cells, and synthetic biology.

In the spirit of explanatory pluralism, this chapter argues that causal and noncausal explanations of a phenomenon are compatible, each being useful for bringing out different sorts of insights. After reviewing a model-based account of scientific explanation, which can accommodate causal and noncausal explanations alike, an important core conception of noncausal explanation is identified. This noncausal form of model-based explanation is illustrated using the example of how Earth scientists in a subfield known as aeolian geomorphology are explaining the formation of (...) regularlyspaced sand ripples. The chapter concludes that even when it comes to everyday "medium-sized dry goods" such as sand ripples, where there is a complete causal story to be told, one can find examples of noncausal scientific explanations. (shrink)

This paper addresses issues surrounding the concept of geometric phase or "anholonomy". Certain physical phenomena apparently require for their explanation and understanding, reference to toplogocial/geometric features of some abstract space of parameters. These issues are related to the question of how gauge structures are to be interpreted and whether or not the debate over their "reality" is really going to be fruitful.

Homology is a natural kind concept, but one that has been notoriously elusive to pin down. There has been sustained debate over the nature of correspondence and the units of comparison. But this continued debate over its meaning has focused on defining homology rather than on its use in practice. The aim of this chapter is to concentrate on the practices of homologizing. I define “homologizing” to be a concept-in-use. Practices of homologizing are kinds of rule following, the satisfaction of (...) which demarcates a category—that of being a homologue. Identifying, explaining, discovering, and understanding are exchanges that connect practice to concept through the performance of a rule by practitioners. These practices are constitutive of natural kinding activities. If homologizing is a kind of kinding, consideration of these practices of discovery, tracking, and identification not only clarifies the meaning, use, and progression of the concept of homology, but provides further understanding of the processes and progression of natural kinds and kinding practices in general. (shrink)

This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story (...) explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems. (shrink)

This book presents the concept of “complementary science” which contributes to scientific knowledge through historical and philosophical investigations. It emphasizes the fact that many simple items of knowledge that we take for granted were actually spectacular achievements obtained only after a great deal of innovative thinking, painstaking experiments, bold conjectures, and serious controversies. Each chapter in the book consists of two parts: a narrative part that states the philosophical puzzle and gives a problem-centred narrative on the historical attempts to solve (...) the puzzle; and the analysis part which provides in-depth analyses of certain scientific, historical, and philosophical aspects of the story. (shrink)

Mesoscale modeling is often considered merely as a practical strategy used when information on lower-scale details is lacking, or when there is a need to make models cognitively or computationally tractable. Without dismissing the importance of practical constraints for modeling choices, we argue that mesoscale models should not just be considered as abbreviations or placeholders for more “complete” models. Because many systems exhibit different behaviors at various spatial and temporal scales, bottom-up approaches are almost always doomed to fail. Mesoscale models (...) capture aspects of multi-scale systems that cannot be parameterized by simple averaging of lower-scale details. To understand the behavior of multi-scale systems, it is essential to identify mesoscale parameters that “code for” lower-scale details in a way that relate phenomena intermediate between microscopic and macroscopic features. We illustrate this point using examples of modeling of multi-scale systems in materials science and biology, where identification of material parameters such as stiffness or strain is a central step. The examples illustrate important aspects of a so-called “middle-out” modeling strategy. Rather than attempting to model the system bottom-up, one starts at intermediate scales where systems exhibit behaviors distinct from those at the atomic and continuum scales. One then seeks to upscale and downscale to gain a more complete understanding of the multi-scale system. The cases highlight how parameterization of lower-scale details not only enables tractable modeling but is also central to understanding functional and organizational features of multi-scale systems. (shrink)

A common reductionist assumption is that macro-scale behaviors can be described "bottom-up" if only sufficient details about lower-scale processes are available. The view that an "ideal" or "fundamental" physics would be sufficient to explain all macro-scale phenomena has been met with criticism from philosophers of biology. Specifically, scholars have pointed to the impossibility of deducing biological explanations from physical ones, and to the irreducible nature of distinctively biological processes such as gene regulation and evolution. This paper takes a step back (...) in asking whether bottom-up modeling is feasible even when modeling simple physical systems across scales. By comparing examples of multi-scale modeling in physics and biology, we argue that the “tyranny of scales” problem presents a challenge to reductive explanations in both physics and biology. The problem refers to the scale-dependency of physical and biological behaviors that forces researchers to combine different models relying on different scale-specific mathematical strategies and boundary conditions. Analyzing the ways in which different models are combined in multi-scale modeling also has implications for the relation between physics and biology. Contrary to the assumption that physical science approaches provide reductive explanations in biology, we exemplify how inputs from physics often reveal the importance of macro-scale models and explanations. We illustrate this through an examination of the role of biomechanics modeling in developmental biology. In such contexts, the relation between models at different scales and from different disciplines is neither reductive nor completely autonomous, but interdependent. (shrink)

A significant portion of the scholarship in analytic philosophy of psychiatry has been devoted to the problem of what kind of kind psychiatric disorders are. Efforts have included descriptive projects, which aim to identify what psychiatrists in fact refer to when they diagnose, and prescriptive ones, which argue over that to which diagnostic categories should refer. In other words, philosophers have occupied themselves with what I call “diagnostic kinds”. However, the pride of place traditionally given to diagnostic kinds in psychiatric (...) research has recently come under attack, most notably by a recent initiative of the National Institute of Mental Health, the Research Domain Criteria Project, that seeks to exclude diagnostic categories from experimental designs and focus on other sorts of psychiatric kinds. I argue that philosophical accounts privileging diagnostic kinds must respond to this new line of criticism, and conclude that philosophers need to either counter psychiatrists’ growing suspicion about the hegemony of diagnostic categories in the clinic and the laboratory, or join in redirecting their efforts toward the development of robust accounts of other sorts of psychiatric objects and processes. (shrink)

This paper examines a fundamental problem in applied mathematics. How can one model the behavior of materials that display radically different, dominant behaviors at different length scales. Although we have good models for material behaviors at small and large scales, it is often hard to relate these scale-based models to one another. Macroscale models represent the integrated effects of very subtle factors that are practically invisible at the smallest, atomic, scales. For this reason it has been notoriously difficult to model (...) realistic materials with a simple bottom-up-from-the-atoms strategy. The widespread failure of that strategy forced physicists interested in overall macro-behavior of materials toward completely top-down modeling strategies familiar from traditional continuum mechanics. The problem of the ``tyranny of scales'' asks whether we can exploit our rather rich knowledge of intermediate micro- scale behaviors in a manner that would allow us to bridge between these two dominant methodologies. Macroscopic scale behaviors often fall into large common classes of behaviors such as the class of isotropic elastic solids, characterized by two phenomenological parameters---so-called elastic coefficients. Can we employ knowledge of lower scale behaviors to understand this universality---to determine the coefficients and to group the systems into classes exhibiting similar behavior? (shrink)

Acidity provides an interesting example of an everyday concept that developed fully into a scientific one; it is one of the oldest concepts in chemistry and remains an important one. However, up to now there has been no unity to it. Currently two standard theoretical definitions coexist ; the standard laboratory measure of acidity, namely the pH, only corresponds directly to the Br⊘nsted-Lowry concept. The lasting identity of the acidity concept in modern chemistry is based on the persistence of the (...) quotidian concept. This is suggestive for considerations of other scientific concepts. (shrink)

Robert Batterman examines a form of scientific reasoning called asymptotic reasoning, arguing that it has important consequences for our understanding of the scientific process as a whole. He maintains that asymptotic reasoning is essential for explaining what physicists call universal behavior. With clarity and rigor, he simplifies complex questions about universal behavior, demonstrating a profound understanding of the underlying structures that ground them. This book introduces a valuable new method that is certain to fill explanatory gaps across disciplines.

Viruses have been virtually absent from philosophy of biology. In this editorial introduction, we explain why we think viruses are philosophically important. We focus on six issues, and we show how they relate to classic questions of philosophy of biology and even general philosophy.

Examining stem cell biology from a philosophy of science perspective, this book clarifies the field's central concept, the stem cell, as well as its aims, methods, models, explanations and evidential challenges. The first chapters discuss what stem cells are, how experiments identify them, and why these two issues cannot be completely separated. The basic concepts, methods and structure of the field are set out, as well as key limitations and challenges. The second part of the book shows how rigorous explanations (...) emerge from stem cell experiments, and compares these to other kinds of scientific explanation. Model organisms, the role of genes, and the significance of collaboration are also discussed. The last part of the book considers relations to systems biology and clinical medicine, arguing that both the mathematical models of the former, and ethical principles of the latter, are necessary for stem cell biology to deliver on its promises. (shrink)