Lewis sender-receiver games illustrate how a meaningful term language might evolve from initially meaningless random signals (Lewis 1969; Skyrms 2006). Here we consider how a meaningful language with a primitive grammar might evolve in a somewhat more subtle sort of game. The evolution of such a language involves the co-evolution of partitions of the physical world into what may seem, at least from the perspective of someone using the language, to correspond to canonical natural kinds. While the evolved language may allow for the sort of precise representation that is required for successful coordinated action and prediction, the apparent natural kinds reflected in its structure may be purely conventional. This has both positive and negative implications for the limits of naturalized metaphysics.

This paper develops a stronger version of ‘inference-to-the-best explanation’ scientific realism. I argue against three standard assumptions of current realists: (1) realism is confirmed if it provides the best explanation of theories’ predictive success; (2) the realist claim that successful theories are always approximately true provides the best explanation of their success; and (3) realists are committed to giving the same sort of truth-based explanation of superseded theories’ success that they give to explain our best current theories’ success. On the positive side, I argue that (1) the confirmation of realism requires explaining theories’ explanatory success, not just their predictive success; (2) in turn this task requires a richer realist model of explanation that brings into the explanans both (a) successful theories’ epistemic virtues (e.g., unification and simplicity) and (b) the standards governing these virtues, as well as truth; (3) this richer realist model is further confirmed because it can better explain the success of theories in gaining wide acceptance among scientists; and (4) the model is further supported because it is superior to ‘preservative realism’ in providing a plausible rebuttal of the pessimistic meta-induction from the many past successful-but-false theories to the likelihood that our best current theories are likewise false.

The most recent resurgence of philosophical attention to the so-called ‘functional talk’ in the sciences can be summarized in terms of the following questions: (Q1) What kind of restrictions, and in particular, what kind of evolutionary restrictions as well as to what extent, is involved in functional ascriptions? (Q2) How can we account for the explanatory import of function-ascribing statements? This paper addresses these questions through a modified version of Cummins’ functional analysis. The modification in question is concerned with phylogenetical restrictions on causal role functions, and it stems from an analysis of some primary areas in molecular biology. I examine how evolutionary consideration affects the so-called ‘function-analytical explanatory strategy’ (Cummins [1975] 1998, 2002). Finally, I argue that the neo-functional analysis here proposed accounts for a certain convergence between the main rival theories of biological function.

Accounts of the concepts of function and dysfunction have not adequately explained what factors determine the line between low-normal function and dysfunction. I call the challenge of doing so the line-drawing problem. Previous approaches emphasize facts involving the action of natural selection (Wakefield 1992a, 1999a, 1999b) or the statistical distribution of levels of functioning in the current population (Boorse 1977, 1997). I point out limitations of these two approaches and present a solution to the line-drawing problem that builds on the second one.

Epistemologists and philosophers of science have often attempted to express formally the impact of a piece of evidence on the credibility of a hypothesis. In this paper we will focus on the Bayesian approach to evidential support. We will propose a new formal treatment of the notion of degree of confirmation and we will argue that it overcomes some limitations of the currently available approaches on two grounds: (i) a theoretical analysis of the confirmation relation seen as an extension of logical deduction and (ii) an empirical comparison of competing measures in an experimental inquiry concerning inductive reasoning in a probabilistic setting.

This paper characterizes the increase in ‘scientific understanding’ that resulted from the Ingold Revolution in organic chemistry. By describing both the sorts of explanations facilitated by Ingold's Revolution and the sense in which organic chemistry was ‘unified’ by adopting these approaches to explanation, one can appreciate how this revolution led to a dramatic qualitative improvement in organic chemists’ understanding of the phenomena that they study. The explanatory unification responsible for this transformation in organic chemistry is contrasted with contemporary philosophical accounts of unification and its relationship to both scientific understanding and explanation.

In a recent issue of this journal, M. Frisch claims to have proven that classical electrodynamics is an inconsistent physical theory. We argue that he has applied classical electrodynamics inconsistently. Frisch also claims that all other classical theories of electromagnetic phenomena, when consistent and in some sense an approximation of classical electrodynamics, are haunted by “serious conceptual problems” that defy resolution. We argue that this claim is based on a partisan if not misleading presentation of theoretical research in classical electrodynamics.

Do evolutionary processes such as selection and random drift cause evolutionary change, or are they merely convenient ways of describing or summarizing it? Philosophers have lined up on both sides of this question. One recent defense (Reisman and Forber 2005) of the causal status of selection and drift appeals to a manipulability theory of causation. Yet, even if one accepts manipulability, there are still reasons to doubt that genetic drift, in particular, is genuinely causal. We will address two challenges to treating drift as causal within a manipulation framework. We will argue that both challenges ultimately fail, but that they raise interesting and subtle issues about the nature of causation and the differences between selection and drift.

In this essay I first provide an analysis of various community concepts. Second, I evaluate two of the most serious challenges to the existence of communities—gradient and paleoecological analysis respectively—arguing that, properly understood, neither threatens the existence of communities construed interactively. Finally, I apply the same interactive approach to ecosystem ecology, arguing that ecosystems may exist robustly as well.

Robots are being extensively used for the purpose of discovering and testing empirical hypotheses about biological sensorimotor mechanisms. We examine here methodological problems that have to be addressed in order to design and perform “good” experiments with these machine models. These problems notably concern the mapping of biological mechanism descriptions into robotic mechanism descriptions; the distinction between theoretically unconstrained “implementation details” and robotic features that carry a modeling weight; the role of preliminary calibration experiments; the monitoring of experimental environments for disturbing factors that affect both modeling features and theoretically unconstrained implementation details of robots. Various assumptions that are gradually introduced in the process of setting up and performing these robotic experiments become integral parts of the background hypotheses that are needed to bring experimental observations to bear on biological mechanism descriptions.

Robert MacArthur's mathematical ecology is often regarded as ahistorical and has been criticized by historically oriented ecologists and philosophers for ignoring the importance of history. I clarify and defend his approach, especially his use of simple mathematical models to explain patterns in data and to generate predictions that stimulate empirical research. First I argue that it is misleading to call his approach ahistorical because it is not against historical explanation. Next I distinguish three kinds of criticism of his approach and argue that his approach is compatible with the first two of them. Finally, I argue that the third kind of criticism, advanced by Kim Sterelny and Paul Griffiths, is largely irrelevant to MacArthur's approach.

This paper brings needed clarity to the influential view that species are cohesive entities held together by gene flow, and then develops an empirical argument against that view: Neglected data suggest gene flow is neither necessary nor sufficient for species cohesion. Implications are discussed.

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.

The optimality approach to modeling natural selection has been criticized by many biologists and philosophers of biology. For instance, Lewontin (1979) argues that the optimality approach is a shortcut that will be replaced by models incorporating genetic information, if and when such models become available. In contrast, I think that optimality models have a permanent role in evolutionary study. I base my argument for this claim on what I think it takes to best explain an event. In certain contexts, optimality and game-theoretic models best explain some central types of evolutionary phenomena.

In their paper, “Vexing Expectations,” Nover and Hájek (2004) present an allegedly paradoxical betting scenario which they call the Pasadena Game (PG). They argue that the silence of standard decision theory concerning the value of playing PG poses a serious problem. This paper provides a threefold response. First, I argue that the real problem is not that decision theory is “silent” concerning PG, but that it delivers multiple conflicting verdicts. Second, I offer a diagnosis of the problem based on the insight that standard decision theory is, rightly, sensitive to order. Third, I describe a new betting scenario—the Alternating St. Petersburg Game—which is genuinely paradoxical. Standard decision theory is silent on the value of playing this game even if restrictions are placed on the order in which the various alternative payoffs are summed.