Charles H. Pence

A New Foundation for the Propensity Interpretation of Fitness

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. 1 Introduction   1.1 The ‘Generality Problem’   1.2 Counterexamples to the PIF    1.2.1 The moments problem    1.2.2 The delayed selection problem    1.2.3 Timing of reproduction   1.3 The need for a new model 2 A New Formalization   2.1 The new model and biological theory 3 Possible Objections to   3.1 Objection 1: Natural selection is short term   3.2 Objection 2: Descendants are only minimally related to ancestors   3.3 Objection 3: Evolutionary time scale is pragmatically determined   3.4 Objection 4: Long-term fitness is lineage fitness   3.5 Objection 5: The theory of evolution by natural selection fundamentally concerns trait fitness, not individual fitness 4 Response to Counterexamples   4.1 Timing of reproduction   4.2 Delayed selection   4.3 The moments problem 5 Conclusion 1 Introduction   1.1 The ‘Generality Problem’   1.2 Counterexamples to the PIF    1.2.1 The moments problem    1.2.2 The delayed selection problem    1.2.3 Timing of reproduction   1.3 The need for a new model   1.1 The ‘Generality Problem’   1.2 Counterexamples to the PIF    1.2.1 The moments problem    1.2.2 The delayed selection problem    1.2.3 Timing of reproduction    1.2.1 The moments problem    1.2.2 The delayed selection problem    1.2.3 Timing of reproduction   1.3 The need for a new model 2 A New Formalization   2.1 The new model and biological theory   2.1 The new model and biological theory 3 Possible Objections to   3.1 Objection 1: Natural selection is short term   3.2 Objection 2: Descendants are only minimally related to ancestors   3.3 Objection 3: Evolutionary time scale is pragmatically determined   3.4 Objection 4: Long-term fitness is lineage fitness   3.5 Objection 5: The theory of evolution by natural selection fundamentally concerns trait fitness, not individual fitness   3.1 Objection 1: Natural selection is short term   3.2 Objection 2: Descendants are only minimally related to ancestors   3.3 Objection 3: Evolutionary time scale is pragmatically determined   3.4 Objection 4: Long-term fitness is lineage fitness   3.5 Objection 5: The theory of evolution by natural selection fundamentally concerns trait fitness, not individual fitness 4 Response to Counterexamples   4.1 Timing of reproduction   4.2 Delayed selection   4.3 The moments problem   4.1 Timing of reproduction   4.2 Delayed selection   4.3 The moments problem 5 Conclusion

Is genetic drift a force

One hotly debated philosophical question in the analysis of evolutionary theory concerns whether or not evolution and the various factors which constitute it (selection, drift, mutation, and so on) may profitably be considered as analogous to “forces” in the traditional, Newtonian sense. Several compelling arguments assert that the force picture is incoherent, due to the peculiar nature of genetic drift. I consider two of those arguments here—that drift lacks a predictable direction, and that drift is constitutive of evolutionary systems—and show that they both fail to demonstrate that a view of genetic drift as a force is untenable. I go on to diagnose the reasons for the stubborn persistence of this problem, considering two open philosophical issues and offering some preliminary arguments in support of the force metaphor.

Oyun: A New, Free Program for Iterated Prisoner's Dilemma Tournaments in the Classroom

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 prisoners dilemma tournaments, competitions between prisoners 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 prisoners 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.

RLetters: A Web-Based Application for Text Analysis of Journal Articles.

While textual analysis of the journal literature is a burgeoning field, there is still a profound lack of user-friendly software for accomplishing this task. RLetters is a free, open-source web application which provides researchers with an environment in which they can select sets of journal articles and analyze them with cutting-edge textual analysis tools. RLetters allows users without prior expertise in textual analysis to analyze word frequency, collocations, cooccurrences, term networks, and more. It is implemented in Ruby and scripts are provided to automate deployment.

MTBindingSim: simulate protein binding to microtubules

SUMMARY Many protein-protein interactions are more complex than can be accounted for by 1:1 binding models. However, biochemists have few tools available to help them recognize and predict the behaviors of these more complicated systems, making it difficult to design experiments that distinguish between possible binding models. MTBindingSim provides researchers with an environment in which they can rapidly compare different models of binding for a given scenario. It is written specifically with microtubule polymers in mind, but many of its models apply equally well to any polymer or any protein-protein interaction. MTBindingSim can thus both help in training intuition about binding models and with experimental design. AVAILABILITY AND IMPLEMENTATION MTBindingSim is implemented in MATLAB and runs either within MATLAB (on Windows, Mac or Linux) or as a binary without MATLAB (on Windows or Mac). The source code (licensed under the GNU General Public License) and binaries are freely available at http://mtbindingsim.googlecode.com. CONTACT jphilip@nd.edu; cpence@nd.edu.

evoText: A new tool for analyzing the biological sciences

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.

Is Organismic Fitness at the Basis of Evolutionary Theory

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 and its role as a measure of evolutionary dynamics.

How to do digital philosophy of science

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.

Military genomic testing: proportionality, expected benefits, and the connection between genotypes and phenotypes

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 paternalism 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 Lis 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.

Busting myths about “species”

No abstract.