Charles H. Pence

The idea of ‘reversion’ or ‘atavism’ has a peculiar history. For many authors in the latenineteenth and early-twentieth centuries – including Darwin, Galton, Pearson, Weismann, and Spencer, among others – reversion was one of the central phenomena which a theory of heredity ought to explain. By only a few decades later, however, Fisher and others could look back upon reversion as a historical curiosity, a non-problem, or even an impediment to clear theorizing. I explore various reasons that reversion might have appeared to be a central problem for this first group of figures, focusing on their commitment to a variety of conceptual features of evolutionary theory; discuss why reversion might have then ceased to be an interesting phenomenon; and, finally, close with some more general thoughts about the death of scientific problems.

The Rise of Chance in Evolutionary Theory: A Pompous Parade of Arithmetic explores a pivotal conceptual moment in the history of evolutionary theory: the development of its extensive reliance on a wide array of concepts of chance. It tells the history of a methodological and conceptual development that reshaped our approach to natural selection over a century, ranging from Darwin’s earliest notebooks in the 1830s to the early years of the Modern Synthesis in the 1930s. Far from being a “pompous parade of arithmetic,” as one early critic argued, evolution transformed during this period to make these conceptual and technical tools indispensable. This book charts the role of chance in evolutionary theory from its beginnings to the earliest days of modern evolutionary theory, making it an ideal resource for evolutionary biologists, historians, philosophers, and researchers in science studies or biological statistics.

Recent arguments concerning the nature of causation in evolutionary theory, now often known as the debate between the 'causalist' and 'statisticalist' positions, have involved answers to a variety of independent questions – definitions of key evolutionary concepts like natural selection, fitness, and genetic drift; causation in multi-level systems; or the nature of evolutionary explanations, among others. This Element offers a way to disentangle one set of these questions surrounding the causal structure of natural selection. Doing so allows us to clearly reconstruct the approach that some of these major competing interpretations of evolutionary theory have to this causal structure, highlighting particular features of philosophical interest within each. Further, those features concern problems not exclusive to the philosophy of biology. Connections between them and, in two case studies, contemporary metaphysics and philosophy of physics demonstrate the potential value of broader collaboration in the understanding of evolution.

A recent debate over the causal foundations of evolutionary theory pits those who believe that natural selection causally explains long-term, adaptive population change against those who do not. In this paper, I argue that this debate – far from being an invention of several articles in 2002 – dates from our very first engagements with evolution as a quantified, statistical science. Further, when we analyze that history, we see that a pivotal figure in the early use of statistical methodology in evolutionary theory, W.F.R. Weldon, changes his mind about precisely the central claim at issue. I close by drawing some morals which I think the case can offer for the contemporary debate going forward.

Empirical philosophers of science aim to base their philosophical theories on observations of scientific practice. But since there is far too much science to observe it all, how can we form and test hypotheses about science that are sufficiently rigorous and broad in scope, while avoiding the pitfalls of bias and subjectivity in our methods? Part of the answer, we claim, lies in the computational tools of the digital humanities, which allow us to analyze large volumes of scientific literature. Here we advocate for the use of these methods by addressing a number of large-scale, justificatory concerns—specifically, about the epistemic value of journal articles as evidence for what happens elsewhere in science, and about the ability of DH tools to extract this evidence. Far from ignoring the gap between scientific literature and the rest of scientific practice, effective use of DH tools requires critical reflection about these relationships.

Philosophers of biology have worked extensively on how we ought best to interpret the probabilities which arise throughout evolutionary theory. In spite of this substantial work, however, much of the debate has remained persistently intractable. I offer the example of Bayesian models of divergence time estimation as a case study in how we might bring further resources from the biological literature to bear on these debates. These models offer us an example in which a number of different sources of uncertainty are combined to produce an estimate for a complex, unobservable quantity. These models have been carefully analyzed in recent biological work, which has determined the relationship between these sources of uncertainty, both quantitatively and qualitatively. I suggest here that this case shows us the limitations of univocal analyses of probability in evolution, as well as the simple dichotomy between “subjective” and “objective” probabilities, and I conclude by gesturing toward ways in which we might introduce more sophisticated interpretive taxonomies of probability as a path toward advancing debates on probability in the life sciences.

We argue that the economy of nature constitutes an invocation of structure in the biological sciences, one largely missed by philosophers of biology despite the turn in recent years toward structural explanations throughout the philosophy of science. We trace a portion of the history of this concept, beginning with the theologically and economically grounded work of Linnaeus, moving through Darwin’s adaptation of the economy of nature and its reconstitution in genetic terms during the first decades of the Modern Synthesis. What this historical case study reveals, we argue, is a window into the shifting landscape of the explanatory and ontic uses of structural concepts. In Linnaeus, the economy of nature has both ontic and explanatory import; in Darwin the ontic and explanatory aspects start to come apart ; and finally, in the Modern Synthesis, the economy of nature is replaced by the conceptual toolkit of population genetics, the structural elements of which are nearly entirely explanatory. Having traced a historical trajectory of structural concepts that moves from an ontic formulation to an increasingly explanatory one, we conclude by outlining some insights for structural realism.

Philosophy of science is expanding via the introduction of new digital data and tools for their analysis. The data comprise digitized published books and journal articles, as well as heretofore unpublished material such as images, archival text, notebooks, meeting notes, and programs. The growth in available data is matched by the extensive development of automated analysis tools. The variety of data sources and tools can be overwhelming. In this article, we survey the state of digital work in the philosophy of science, showing what kinds of questions can be answered and how one can go about answering them.

There are a bewildering variety of claims connecting Darwin to nineteenth-century philosophy of science—including to Herschel, Whewell, Lyell, German Romanticism, Comte, and others. I argue here that Herschel’s influence on Darwin is undeniable. The form of this influence, however, is often misunderstood. Darwin was not merely taking the concept of “analogy” from Herschel, nor was he combining such an analogy with a consilience as argued for by Whewell. On the contrary, Darwin’s Origin is written in precisely the manner that one would expect were Darwin attempting to model his work on the precepts found in Herschel’s Preliminary Discourse on Natural Science. While Hodge has worked out a careful interpretation of both Darwin and Herschel, drawing similar conclusions, his interpretation misreads Herschel’s use of the vera causa principle and the verification of hypotheses. The new reading that I present here resolves this trouble, combining Hodge’s careful treatment of the structure of the Origin with a more cautious understanding of Herschel’s philosophy of science. This interpretation lets us understand why Darwin laid out the Origin in the way that he did and also why Herschel so strongly disagreed, including in Herschel’s heretofore unanalyzed marginalia in his copy of Darwin’s book.

Fitness plays many roles throughout evolutionary theory, from a measure of populations in the wild to a central element in abstract theoretical presentations of natural selection. It has thus been the subject of an extensive philosophical literature, which has primarily centered on the way to understand the relationship between fitness values and reproductive outcomes. If fitness is a probabilistic or statistical quantity, how is it to be defined in general theoretical contexts? How can it be measured? Can a single conceptual model for fitness be offered that applies in all biological cases, or must fitness measures be case-specific? Philosophers have explored these questions over the last several decades, largely in the context of an influential definition of fitness proposed in the late 1970s: the propensity interpretation. This interpretation as first described undeniably suffers from significant difficulties, and debate regarding the tenability of amendments and alternatives to it remains unsettled.

The propensity interpretation of fitness (PIF) is commonly taken to be subject to a set of simple counterexamples. We argue that three of the most important of these are not counterexamples to the PIF itself, but only to the traditional mathematical model of this propensity: fitness as expected number of offspring. They fail to demonstrate that a new mathematical model of the PIF could not succeed where this older model fails. We then propose a new formalization of the PIF that avoids these (and other) counterexamples. By producing a counterexample-free model of the PIF, we call into question one of the primary motivations for adopting the statisticalist interpretation of fitness. In addition, this new model has the benefit of being more closely allied with contemporary mathematical biology than the traditional model of the PIF.

Work throughout the history and philosophy of biology frequently employs ‘chance’, ‘unpredictability’, ‘probability’, and many similar terms. One common way of understanding how these concepts were introduced in evolution focuses on two central issues: the first use of statistical methods in evolution (Galton), and the first use of the concept of “objective chance” in evolution (Wright). I argue that while this approach has merit, it fails to fully capture interesting philosophical reflections on the role of chance expounded by two of Galton's students, Karl Pearson and W.F.R. Weldon. Considering a question more familiar from contemporary philosophy of biology—the relationship between our statistical theories of evolution and the processes in the world those theories describe—is, I claim, a more fruitful way to approach both these two historical actors and the broader development of chance in evolution.

Mehlman and Li offer a framework for approaching the bioethical issues raised by the military use of genomics that is compellingly grounded in both the contemporary civilian and military ethics of medical research, arguing that military commanders must be bound by the two principles of paternal- ism and proportionality. I agree fully. But I argue here that this is a much higher bar than we may fully realize. Just as the principle of proportionality relies upon a thorough assessment of harms caused and military advantage gained, the use of genomic research, on Mehlman and Li’s view, will require an accurate understanding of the connection between genotypes and phenotypes – accurate enough to ameliorate the risk undertaken by our armed forces in being subject to such research. Recent conceptual work in evolutionary theory and the philosophy of biology, however, renders it doubtful that such knowledge is forthcoming. The complexity of the relationship between genotypic factors and realized traits (the so-called ‘G→P map’) makes the estimation of potential military advantage, as well as potential harm to our troops, incredibly challenging. Such fundamental conceptual challenges call into question our ability to ever satisfactorily satisfy the demands of a sufficiently rigorous ethical standard.

Fitness is a central theoretical concept in evolutionary theory. Despite its importance, much debate has occurred over how to conceptualize and formalize fitness. One point of debate concerns the roles of organismic and trait fitness. In a recent addition to this debate, Elliott Sober argues that trait fitness is the central fitness concept, and that organismic fitness is of little value. In this paper, by contrast, we argue that it is organismic fitness that lies at the bases of both the conceptual role of fitness, as well as its role as a measure of evolutionary dynamics.

Evolutionary biology since Darwin has seen a dramatic entrenchment and elaboration of the role of chance in evolution. It is nearly impossible to discuss contemporary evolutionary theory in any depth at all without making reference to at least some concept of “chance” or “randomness.” Many processes are described as chancy, outcomes are characterized as random, and many evolutionary phenomena are thought to be best described by stochastic or probabilistic models. Chance is taken by various authors to be central to the understanding of fitness, genetic drift, macroevolution, mutation, foraging theory, and environmental variation, to take but a few examples. And for each of these notions, there are yet more stories to tell. Each weaves itself into the various branches of evolutionary theory in myriad different ways, with a wide variety of effects on the history and current state of life on Earth. Each is grounded in a particular trajectory in the history of philosophy and the history of biology, and has inspired a variety of responses throughout science and culture. This book endeavors to offer a cross-section of biological, historical, philosophical, and theological approaches to understanding chance in evolutionary theory.

Despite his position as one of the first philosophers to write in the “post- Darwinian” world, the critique of Darwin by Friedrich Nietzsche is often ignored for a host of unsatisfactory reasons. I argue that Nietzsche’s critique of Darwin is important to the study of both Nietzsche’s and Darwin’s impact on philosophy. Further, I show that the central claims of Nietzsche’s critique have been broadly misunderstood. I then present a new reading of Nietzsche’s core criticism of Darwin. An important part of Nietzsche’s response can best be understood as an aesthetic critique of Darwin, reacting to what he saw as Darwin having drained life of an essential component of objective aesthetic value. For Nietzsche, Darwin’s theory is false because it is too intellectual, because it searches for rules, regulations, and uniformity in a realm where none of these are to be found – and, moreover, where they should not be found. Such a reading goes furthest toward making Nietzsche’s criticism substantive and relevant. Finally, I attempt to relate this novel explanation of Nietzsche’s critique to topics in contemporary philosophy of biology, particularly work on the evolutionary explanation of culture.

We introduce here evoText, a new tool for automated analysis of the literature in the biological sciences. evoText contains a database of hundreds of thousands of journal articles and an array of analysis tools for generating quantitative data on the nature and history of life science, especially ecology and evolutionary biology. This article describes the features of evoText, presents a variety of examples of the kinds of analyses that evoText can run, and offers a brief tutorial describing how to use it.

There are two motivations commonly ascribed to historical actors for taking up statistics: to reduce complicated data to a mean value (e.g., Quetelet), and to take account of diversity (e.g., Galton). Different motivations will, it is assumed, lead to different methodological decisions in the practice of the statistical sciences. Karl Pearson and W. F. R. Weldon are generally seen as following directly in Galton’s footsteps. I argue for two related theses in light of this standard interpretation, based on a reading of several sources in which Weldon, independently of Pearson, reflects on his own motivations. First, while Pearson does approach statistics from this "Galtonian" perspective, he is, consistent with his positivist philosophy of science, utilizing statistics to simplify the highly variable data of biology. Weldon, on the other hand, is brought to statistics by a rich empiricism and a desire to preserve the diversity of biological data. Secondly, we have here a counterexample to the claim that divergence in motivation will lead to a corresponding separation in methodology. Pearson and Weldon, despite embracing biometry for different reasons, settled on precisely the same set of statistical tools for the investigation of evolution.

Evolutionary applications of game theory present one of the most pedagogically accessible varieties of genuine, contemporary theoretical biology. We present here Oyun (OY-oon, http://charlespence.net/oyun), a program designed to run iterated prisoner’s dilemma tournaments, competitions between prisoner’s dilemma strategies developed by the students themselves. Using this software, students are able to readily design and tweak their own strategies, and to see how they fare both in round-robin tournaments and in “evolutionary” tournaments, where the scores in a given “generation” directly determine contribution to the population in the next generation. Oyun is freely available, runs on Windows, Mac, and Linux computers, and the process of creating new prisoner’s dilemma strategies is both easy to teach and easy for students to grasp. We illustrate with two interesting examples taken from actual use of Oyun in the classroom.