A common presupposition in the concepts literature is that concepts constitute a singular natural kind. If, on the contrary, concepts split into more than one kind, this literature needs to be recast in terms of other kinds of mental representation. We offer two new arguments that concepts, in fact, divide into different kinds: (a) concepts split because different kinds of mental representation, processed independently, must be posited to explain different sets of relevant phenomena; (b) concepts split because different kinds of mental representation, processed independently, must be posited to explain responses to different kinds of category. Whether these arguments are sound remains an open empirical question, to be resolved by future empirical and theoretical work.

Over the last two decades, a fundamental outline of a theory of causal inference has emerged. However, this theory does not consider the following problem. Sometimes two or more measured variables are deterministic functions of one another, not deliberately, but because of redundant measurements. In these cases, manipulation of an observed defined variable may actually be an ambiguous description of a manipulation of some underlying variables, although the manipulator does not know that this is the case. In this article we revisit the question of precisely characterizing conditions and assumptions under which reliable inference about the effects of manipulations is possible, even when the possibility of “ambiguous manipulations” is allowed.

In his “Discussion” (1984), Elliott Sober offers some criticisms of the view about species—pluralistic realism—advocated in my 1984. Sober's comments divide into three parts. He attempts to show that species are not sets; he responds to my critique of David Hull's thesis that species are individuals; and he offers some arguments for the claim that species are “chunks of the genealogical nexus.” I consider each of these objections in turn, arguing that each of them fails. I attempt to use Sober's insightful critique to explain and defend pluralistic realism more fully.

Dutch Book arguments have been presented for static belief systems and for belief change by conditionalization. An argument is given here that a rule for belief change which under certain conditions violates probability kinematics will leave the agent open to a Dutch Book.

An important contemporary version of Boltzmannian statistical mechanics explains the approach to equilibrium in terms of typicality. The problem with this approach is that it comes in different versions, which are, however, not recognized as such and not clearly distinguished. This article identifies three different versions of typicality-based explanations of thermodynamic-like behavior and evaluates their respective successes. The conclusion is that the first two are unsuccessful because they fail to take the system's dynamics into account. The third, however, is promising. I give a precise formulation of the proposal and present an argument in support of its central contention.

In reworking a variety of biological concepts, Developmental Systems Theory (DST) has made frequent use of parity of reasoning. We have done this to show, for instance, that factors that have similar sorts of impact on a developing organism tend nevertheless to be invested with quite different causal importance. We have made similar arguments about evolutionary processes. Together, these analyses have allowed DST not only to cut through some age-old muddles about the nature of development, but also to effect a long-delayed reintegration of development into evolutionary theory.

Our penchant for causal symmetry, however (or ‘causal democracy’, as it has recently been termed), has sometimes been misunderstood. This paper shows that causal symmetry is neither a platitude about multiple influences nor a denial of useful distinctions, but a powerful way of exposing hidden assumptions and opening up traditional formulations to fruitful change.

Advances in molecular biology have generally been taken to support the claim that biology is reducible to chemistry. I argue against that claim by looking in detail at a number of central results from molecular biology and showing that none of them supports reduction because (1) their basic predicates have multiple realizations, (2) their chemical realization is context-sensitive and (3) their explanations often presuppose biological facts rather than eliminate them. I then consider the heuristic and confirmational implications of irreducibility and argue that purely biochemical approaches are likely to be unsound and to be unable to confirm an important range of statements. I conclude by sketching criteria for scientific unity that do not entail reducibility and yet leave an important place for identifying underlying mechanisms. Molecular biology, properly understood, provides an excellent paradigm of non-reductive unity between different explanatory levels.

Kaplan and Craver claim that all explanations in neuroscience appeal to mechanisms. They extend this view to the use of mathematical models in neuroscience and propose a constraint such models must meet in order to be explanatory. I analyze a mathematical model used to provide explanations in dynamical systems neuroscience and indicate how this explanation cannot be accommodated by the mechanist framework. I argue that this explanation is well characterized by Batterman’s account of minimal model explanations and that it demonstrates how relationships between explanatory models in neuroscience and the systems they represent is more complex than has been appreciated.

Germund Hesslow has argued recently [2] that a probabilistic theory of causality as advocated by Patrick Suppes [4] has two problems that a deterministic theory avoids. In this paper, I argue that Suppes' probabilistic causal calculus is free of each of these problems and, moreover, that several broader issues raised by Hesslow's discussion tend to support a probabilistic conception of causes.

It is widely agreed among philosophers of science today that no formal pattern can possibly be found in the origins of scientific theory. There is no such thing as a “logic of discovery,” insists this view—a scientific hypothesis is susceptible to methodological critique only in its relation to empirical consequences derived after the hypothesis itself has emerged through a spontaneous creative inspiration. Yet confronted with the tautly directed thrust of theory-building as actually practiced at the cutting edge of scientific research, this romantic denial of method in the genesis of ideas takes on the appearance of myth.

It is the contention of this article that as empirical data ramify in logical complexity, they deposit a hard sediment of theory according to a standard inductive pattern so primitively compelling that it must be recognized as one of the primary forms of inferential thought. This process, here called “ontological induction,” is a distillation out of unwieldly observed regularities of more conceptually tractable states hypothesized to underlie them, and is the wellspring of our beliefs in theoretical entities. Previous failure to recognize this pattern of induction has undoubtedly been in substantial measure a result of inadequate attention to the structural details of scientific propositions; for in order to exhibit the nature of ontological induction clearly, it is first necessary to make extended forays through sparsely explored methodological terrain—notably, the nature of scientific “variables,” the logical form of “laws,” and the type-hierarchy of scientific concepts.

Highly idealized models, such as the Hawk-Dove game, are pervasive in biological theorizing. We argue that the process and motivation that leads to the introduction of various idealizations into these models is not adequately captured by Michael Weisberg’s taxonomy of three kinds of idealization. Consequently, a fourth kind of idealization is required, which we call hypothetical pattern idealization. This kind of idealization is used to construct models that aim to be explanatory but do not aim to be explanations.

In this paper I argue against Sober's claim that natural selection does not explain the traits of individuals. Sober argues that natural selection only explains the distribution of traits in a population. My point is that the explanation of an individual's traits involves us in a description of the individual's ancestry, and in an explanation of the distribution of traits in that ancestral population. Thus Sober is wrong, natural selection is part of the explanation of the traits of individuals.

Recent work in the Philosophy of Mind has suggested that alternatives to reduction are required in order to explain the relationship between psychology and biology or physics. Emergence has been proposed as one such alternative. In this paper, I propose a precise definition of emergence, and I argue that chaotic systems provide concrete examples of properties that meet this definition. In particular, I suggest that being in the basin of attraction of a strange attractor is an emergent property of any chaotic nonlinear dynamical system. This shows that non-reductive accounts of inter-theoretic relations are necessary, and that non-reductive accounts of the mental are possible. Moreover, this work provides a foundation for future work investigating the nature of explanation, prediction, and scientific understanding of non-reductive phenomena.

A precise formulation of the structure of modern evolutionary theory has proved elusive. In this paper, I introduce and develop a formal approach to the structure of population genetics, evolutionary theory's most developed sub-theory. Under the semantic approach, used as a framework in this paper, presenting a theory consists in presenting a related family of models. I offer general guidelines and examples for the classification of population genetics models; the defining features of the models are taken to be their state spaces, parameters, and laws. The suggestions regarding the various aspects of the characterization of population genetics models provide an outline for further detailed research.

Men think in terms of models. Their sense organs abstract the events which touch them; their memories store traces of these events as coded symbols; and they may recall them according to patterns which they learned earlier, or recombine them in patterns that are new. In all this, we may think of our thought as consisting of symbols which are put in relations or sequences according to operating rules. Both symbols and operating rules are acquired, in part directly from interaction with the outside world, and in part from elaboration of this material through internal recombination. Together, a set of symbols and a set of rules may constitute what we may call a calculus, a logic, a game or a model. Whatever we call it, it will have some structure, i.e., some pattern of distribution of relative discontinuities, and some “laws” of operation.

More powerful methods for studying and integrating the historical track record of scientific episodes and scientific judgment, or what Faust and Meehl describe as a program of meta-science and meta-scientific studies, can supplement and extend more commonly used case study methods. We describe the basic premises of meta-science, overview methodological considerations, and provide examples of meta-scientific studies. Meta-science can help to clarify or resolve long-standing questions in the history and philosophy of science and provide practical help to the working scientist.

This paper explores varieties of scientific structuralism. Central to our investigation is the notion of ‘shared structure’. We begin with a description of mathematical structuralism and use this to point out analogies and disanalogies with scientific structuralism. Our particular focus is the semantic structuralist's attempt to use the notion of shared structure to account for the theory-world connection, this use being crucially important to both the contemporary structural empiricist and realist. We show why minimal scientific structuralism is, at the very least, a powerful methodological standpoint. Our investigation also makes explicit what more must be added to this minimal structuralist position in order to address the theory-world connection, namely, an account of representation.

Philosophers have ever declared that their problems lie at a different level from the problems of the empirical sciences. Perhaps one may agree with this assertion; the question is, however, where should one seek this level. The metaphysicians wish to seek their object behind the objects of empirical science; they wish to enquire after the essence, the ultimate cause of things. But the logical analysis of the pretended propositions of metaphysics has shown that they are not propositions at all, but empty word arrays, which on account of notional and emotional connections arouse the false appearance of being propositions. This conception that the “propositions” of metaphysics, including those of ethics, have no theoretical content, is to be sure still disputed. We shall not, however, enter here on its demonstration, but, under its guidance, will limit ourselves to non-metaphysical and non-ethical (non-evaluating) philosophical problems.

We provide an explicit taxonomy of legitimate kinds of abstraction within constitutive explanation. We argue that abstraction is an inherent aspect of adequate mechanistic explanation. Mechanistic explanations—even ideally complete ones—typically involve many kinds of abstraction and therefore do not require maximal detail. Some kinds of abstraction play the ontic role of identifying the specific complex components, subsets of causal powers, and organizational relations that produce a suitably general phenomenon. Therefore, abstract constitutive explanations are both legitimate and mechanistic.

Richard Jeffrey espoused an antifoundationalist variant of Bayesian thinking that he termed ‘Radical Probabilism’. Radical Probabilism denies both the existence of an ideal, unbiased starting point for our attempts to learn about the world and the dogma of classical Bayesianism that the only justified change of belief is one based on the learning of certainties. Probabilistic judgment is basic and irreducible. Bayesian conditioning is appropriate when interaction with the environment yields new certainty of belief in some proposition but leaves one's conditional beliefs untouched (the ‘Rigidity’ condition). Although Richard Jeffrey denied the general applicability of this condition, one of his main contributions to probabilistic thinking is a form of belief updating—now typically called ‘Jeffrey conditioning’ or ‘probability kinematics’—that is appropriate in circumstances in which Rigidity is satisfied, but where the interaction causes one to reevaluate one's probability judgments over some partition of the possibility space without conferring certainty on any particular element. The most familiar occasion for Jeffrey conditioning is receipt of uncertain evidence: things partially perceived or remembered. But it also serves to illuminate belief updating occasioned by a change in one's degrees of conditional belief, a kind of belief change largely ignored by classical Bayesianism. I argue that such changes in conditional belief can also be basic (in the sense of not being analyzable as a consequence of conditioning on factual information) and offer a kinematical model for a particular kind change in conditional belief. Both are applied to changes in preference. Finally, I argue that Rigidity can fail when changes of belief give inferential grounds for changes in conditional belief (and vice versa). These failures show that conditioning methods are properly regarded, not as valid rules of inference, but as tools in the ‘art of judgment’.