Online research in philosophy

In a small handful of papers in theoretical population genetics, John Gillespie argues that a new stochastic process he calls "genetic draft" is evolutionarily more significant than genetic drift. This case study of chance in evolution explores Gillespie's proposed stochastic evolutionary force and sketches the implications of Gillespie's argument for philosophers' explorations of genetic drift.

The explanation of the suppression of Modus Ponens inferences within the framework of linguistic pragmatics and of plausible reasoning (i.e., deduction from uncertain premises) is defended. First, this approach is expounded, and then it is shown that the results of the first experiment of Byrne, Espino, and Santamar a (1999) support the uncertainty explanation but fail to support their counterexample explanation. Second, two experiments are presented. In the first one, aimed to refute one objection regarding the conclusions observed, the additional conditional premise ( if N, C ) was replaced with a statement of uncertainty ( it is not certain that N ); the answers produced by the participants remained qualitatively and quantitatively similar in both conditions. In the second experiment, a fine-grained analysis of the responses to and justifications for an evaluation task was performed. The results of both experiments strongly supported the uncertainty explanation.

1. Drift and selection can be distinguished conceptually. 2. Selection and drift are physical, biological phenomena. 3. Drift and selection can occur simultaneously in a population. 4. Selection and drift should be characterized as processes (see #1), not outcomes. 5. Distinguishing between selection and drift empirically is difficult, but is (sometimes) not impossible. 6. Selection and drift are population-level causal processes.

Among philosophers, controversy over the notion of drift in population genetics is ongoing. This is at least partly because the notion of drift has an ambiguous usage among population geneticists. My goal in this paper is to explicate the causal dimension of drift, to say what causal influences are responsible for the stochasticity in population genetics models. It is commonplace for population genetics to oppose the influence of selection to that of drift, and to consider how the dynamics of populations are altered when each has greater or lesser influence. I define the causes that are referred to as drift when researchers speak this way.

In “Two Ways of Thinking About Fitness and Natural Selection” (Matthen and Ariew [2002]; henceforth “Two Ways”), we asked how one should think of the relationship between the various factors invoked to explain evolutionary change – selection, drift, genetic constraints, and so on. We suggested that these factors are not related to one another as “forces” are in classical mechanics. We think it incoherent, for instance, to think of natural selection and drift as separate and opposed “forces” in evolutionary change – that it makes sense to say, for instance, that selection contributed 80% to the actual evolutionary history of the human eye, and drift only 20%. We proposed instead a statistical view of the Theory of Evolution, a view in which fitness is not a cause of evolution, but rather a measure of growth. We also argued for a “hierarchical realization model” for thinking about the relationship between evolutionary factors such as those mentioned above, and suggested that in a “fully specified model”, as we call it below, there is no distinction between natural selection and evolution.

The statistical interpretation of the Theory of Natural Selection claims that natural selection and drift are statistical features of mathematical aggregates of individual-level events. Natural selection and drift are not themselves causes. The statistical interpretation is motivated by a metaphysical conception of individual priority. Recently, Millstein, Skipper, and Dietrich (2009) have argued (a) that natural selection and drift are physical processes, and (b) that the statistical interpretation rests on a misconception of the role of mathematics in biology. Both theses are contested.

Among philosophers, controversy over the notion of drift in population genetics is ongoing. This is at least partly because the notion of drift has an ambiguous usage among population geneticists. My goal in this paper is to explicate the causal dimension of drift, to say what causal influences are responsible for the stochasticity in population genetics models. It is commonplace for population genetics to oppose the influence of selection to that of drift, and to consider how the dynamics of populations are altered when each has greater or lesser influence. I define the causes that are referred to as drift when researchers speak this way. Introduction Populations and Variant Types The Cause–Effect Ambiguity of Drift Non-directional Factors in Population Genetics How N ev Is Used in Population Genetics Causal Conceptions of Drift 6.1 The Millstein/Beatty conception of drift 6.2 Rosenberg and Bouchard: Drift as initial conditions NINPICs 7.1 Why drift is instituted by NINPICs 7.2 How NINPICS work 7.3 NINPICs and random sampling 7.4 Independent sampling and effective population size 7.5 Variance in progeny number 7.6 Population effects of NINPICs NINPICs and the Stochastic Character of Selection Theory Conclusion Appendix CiteULike Connotea Del.icio.us What's this?

One controversy about the existence of so called evolutionary forces such as natural selection and random genetic drift concerns the sense in which such “forces” can be said to interact. In this paper I explain how natural selection and random drift can interact. In particular, I show how population-level probabilities can be derived from individual-level probabilities, and explain the sense in which natural selection and drift are embodied in these population-level probabilities. I argue that whatever causal character the individual-level probabilities have is then shared by the population-level probabilities, and that natural selection and random drift then have that same causal character. Moreover, natural selection and drift can then be viewed as two aspects of probability distributions over frequencies in populations of organisms. My characterization of population-level probabilities is largely neutral about what interpretation of probability is required, allowing my approach to support various positions on biological probabilities, including those which give biological probabilities one or another sort of causal character. ‡This paper has benefited from feedback on and discussions of this and earlier work. I want to thank André Ariew, Matt Barker, Lindley Darden, Patrick Forber, Nancy Hall, Mohan Matthen, Samir Okasha, Jeremy Pober, Robert Richardson, Alex Rosenberg, Eric Seidel, Denis Walsh, and Bill Wimsatt. †To contact the author, please write to: Department of Philosophy, University of Alabama at Birmingham, HB 414A, 900 13th Street South, Birmingham, AL 35294-1260; e-mail: mabrams@uab.edu.

Although prediction has been largely absent from discussions of explanation for the past 40 years, theories of explanation can gain much from a reintroduction. I review the history that divorced prediction from explanation, examine the proliferation of models of explanation that followed, and argue that accounts of explanation have been impoverished by the neglect of prediction. Instead of a revival of the symmetry thesis, I suggest that explanation should be understood as a cognitive tool that assists us in generating new predictions. This view of explanation and prediction clarifies what makes an explanation scientific and why inference to the best explanation makes sense in science. *Received August 2009; revised September 2009. †To contact the author, please write to: Department of Philosophy, University of Tennessee, 801 McClung Tower, Knoxville, TN 37920‐0480; e‐mail: hdouglas@utk.edu.

Scientific explanation in terms of laws and initial conditions (or better, in terms of objects with powers and liabilities) is contrasted with personal explanation in terms of agents with powers and purposes. In each case the factors involved in explanation may themselves be explained, and infinite regress of explanation is logically possible. There can be no absolute explanation of phenomena, which is explanation in terms of the logically necessary; but there can be ultimate explanation which is explanation in terms of factors which themselves have no explanation. Our normal criteria of explanation suggest that the explanation of the universe lies in the action of God.