Charles H. Pence

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.

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.