Using an example of a computer simulation of the convective structure of a red giant star, this paper argues that simulation is a rich inferential process, and not simply a “number crunching” technique. The scientific practice of simulation, moreover, poses some interesting and challenging epistemological and methodological issues for the philosophy of science. I will also argue that these challenges would be best addressed by a philosophy of science that places less emphasis on the representational capacity of theories (and ascribes that capacity instead to models) and more emphasis on the role of theory in guiding (rather than determining) the construction of models.

We offer an account of empathetic pain that preserves the distinctions among standard pain, contagious pain, empathetic pain, sympathy for pain, and standard pain ascription. Vicarious experiences of both contagious and empathetic pain resemble to some extent experiences of standard pain. But there are also crucial dissimilarities. As neuroscientific results show, standard pain involves a sensorimotor and an affective component. According to our account, contagious pain consists in imagining the former, whereas empathetic pain consists in imagining the latter. We further argue that awareness of another's standard pain is part of empathetic pain, but empathetic awareness of another's standard pain differs from believing that another is in standard pain.

The aim of this paper is to state the single most powerful argument for use of a non-classical logic in quantum mechanics. In outline the argument is the following. The working logic of a science is the logic of the events and propositions to which probabilities are assigned. A probability should be assigned to every element of the algebra of events. In the case of quantum mechanics probabilities may be assigned to events but not, without restriction, to the conjunction of two events. The conclusion is that the working logic of quantum mechanics is not classical. The nature of the logic that is appropriate for quantum mechanics is examined.

Contemplating imaginary scenarios that evoke certain sorts of quasi-sensory intuitions may bring us to new beliefs about contingent features of the natural world. These beliefs may be produced quasi-observationally; the presence of a mental image may play a crucial cognitive role in the formation of the belief in question. And this albeit fallible quasi-observational belief-forming mechanism may, in certain contexts, be sufficiently reliable to count as a source of justification. This sheds light on the central puzzle surrounding scientific thought experiment, which is how contemplation of an imaginary scenario can lead to new knowledge about contingent features of the natural world.

Selection type theories solve adaptation problems. Natural selection, clonal selection for antibody production, and selective theories of higher brain function are examples. An abstract characterization of typical selection processes is generated by analyzing and extending previous work on the nature of natural selection. Once constructed, this abstraction provides a useful tool for analyzing the nature of other selection theories and may be of use in new instances of theory construction. This suggests the potential fruitfulness of research to find other theory types and construct their abstractions.

The most important theories in fundamental physics, quantum mechanics and statistical mechanics, posit objective probabilities or chances. As important as chance is there is little agreement about what it is. The usual “interpretations of probability” give very different accounts of chance and there is disagreement concerning which, if any, is capable of accounting for its role in physics. David Lewis has contributed enormously to improving this situation. In his classic paper “A Subjectivist's Guide to Objective Chance” he described a framework for representing single case objective chances, showed how they are connected to subjective credences, and sketched a novel account what they are within his Humean account of scientific laws. Here I will describe these contributions and add a little to them.

Two decades of critique have sensitized historians and philosophers of science to the inadequacies of conventional dichotomies between theory and practice, thereby prompting the search for new ways of writing about science that are less beholden than the old ways to the epistemological mores of theoretical physics, and more faithful to the actual practices not only of physics but of all the natural sciences. The need for alternative descriptions seems particularly urgent if one is to understand the place of theory (and, in parallel, the role of modeling) in contemporary molecular biology, a science where, until now, no division between theory and experiment has obtained, and where distinctions between representing and intervening, and more generally, between basic and applied science, are daily becoming more blurred.

Indeed, the very division between theory and experiment threatens to slight the extensive and sophisticated theoretical analyses (and even modeling) on which experimental work in contemporary molecular biology so often depends. My aim in this paper is to find a way of talking about theoretical practices in biology that is directly rooted in the mix of conceptual and material work that biologists do. As an example of such theoretical practices, I choose for the focus of my analysis the development of a model for gene regulation out of the experimental work of Eric Davidson and his colleagues at Cal Tech.

In this paper the concept of supervenience is employed to explain the relationship between fitness as employed in the theory of natural selection and population biology and the physical, behavioral and ecological properties of organisms that are the subjects of lower level theories in the life sciences. The aim of this analysis is to account simultaneously for the fact that the theory of natural selection is a synthetic body of empirical claims, and for the fact that it continues to be misconstrued, even by biologists, for a tautological system. The notion of supervenience is then employed to provide a new statement of the relation of Mendelian predicates to molecular ones in order to provide for the commensurability and potential reducibility of Mendelian to molecular genetics in a way that circumvents the theoretical complications which appear to stand in the way of such a reduction.

A computational theory of induction must be able to identify the projectible predicates, that is to distinguish between which predicates can be used in inductive inferences and which cannot. The problems of projectibility are introduced by reviewing some of the stumbling blocks for the theory of induction that was developed by the logical empiricists. My diagnosis of these problems is that the traditional theory of induction, which started from a given (observational) language in relation to which all inductive rules are formulated, does not go deep enough in representing the kind of information used in inductive inferences.

As an interlude, I argue that the problem of induction, like so many other problems within AI, is a problem of knowledge representation. To the extent that AI-systems are based on linguistic representations of knowledge, these systems will face basically the same problems as did the logical empiricists over induction.

In a more constructive mode, I then outline a non-linguistic knowledge representation based on conceptual spaces. The fundamental units of these spaces are “quality dimensions”. In relation to such a representation it is possible to define “natural” properties which can be used for inductive projections. I argue that this approach evades most of the traditional problems.

This paper attempts to define Exploratory Data Analysis (EDA) more precisely than usual, and to produce the beginnings of a philosophy of this topical and somewhat novel branch of statistics.

A data set is, roughly speaking, a collection of k-tuples for some k. In both descriptive statistics and in EDA, these k-tuples, or functions of them, are represented in a manner matched to human and computer abilities with a view to finding patterns that are not “kinkera”. A kinkus is a pattern that has a negligible probability of being even partly potentially explicable. A potentially explicable pattern is one for which there probably exists a hypothesis of adequate “explicativity”, which is another technical probabilistic concept. A pattern can be judged to be probably potentially explicable even if we cannot find an explanation. The theory of probability understood here is one of partially ordered (interval-valued), subjective (personal) probabilities. Among other topics relevant to a philosophy of EDA are the “reduction” of data; Francis Bacon's philosophy of science; the automatic formulation of hypotheses; successive deepening of hypotheses; neurophysiology; and rationality of type II.

The role played by the concept of genetic coding in biology is discussed. I argue that this concept makes a real contribution to solving a specific problem in cell biology. But attempts to make the idea of genetic coding do theoretical work elsewhere in biology, and in philosophy of biology, are probably mistaken. In particular, the concept of genetic coding should not be used (as it often is) to express a distinction between the traits of whole organisms that are coded for in the genes and the traits that are not.

Developmental systems theory (DST) expands the unit of replication from genes to whole systems of developmental resources, which DST interprets in terms of cycling developmental processes. Expansion seems required by DST's argument against privileging genes in evolutionary and developmental explanations of organic traits. DST and the expanded replicator brook no distinction between biological and cultural evolution. However, by endorsing a single expanded unit of inheritance and leaving the classical molecular notion of gene intact, DST achieves only a nominal reunification of heredity and development. I argue that an alternative conceptualization of inheritance denies the classical opposition of genetics and development while avoiding the singularity inherent in the replicator concept. It also yields a new unit—the reproducer—which genuinely integrates genetic and developmental perspectives. The reproducer concept articulates the non-separability of “genetic” and “developmental” roles in units of heredity, development, and evolution. DST reformulated in terms of reproducers rather than replicators preserves an empirically interesting distinction between cultural and biological evolution.

I argue that supervenience is an inadequate device for representing relations between different levels of phenomena. I then provide six criteria that emergent phenomena seem to satisfy. Using examples drawn from macroscopic physics, I suggest that such emergent features may well be quite common in the physical realm.

It is argued that the main problem with “the problem of the direction of time” is to figure out what the problem is or is supposed to be. Towards this end, an attempt is made to disentangle and to classify some of the many issues which have been discussed under the label of ‘the direction of time’. Secondly, some technical apparatus is introduced in the hope of producing a sharper formulation of the issues than they have received in the philosophical literature. Finally, some tentative suggestions about the central issues are offered. In particuar, it is suggested that entropy and irreversibility are much less crucial to the central issues than most philosophers would have us believe. This suggestion is not made because of any firm conviction of its correctness but rather because it helps to focus the discussion on some basic but long neglected assumptions which underlie traditional approaches.

“Theoretical pluralism” obtains when there are good evidential reasons for accommodating multiple theories of the same domain. Issues of “relative significance” often arise in connection with the investigation of such domains. In this paper, I describe and give examples of theoretical pluralism and relative significance issues. Then I explain why theoretical pluralism so often obtains in biology—and why issues of relative significance arise—in terms of evolutionary contingencies and the paucity or lack of laws of biology. Finally, I turn from explanation to justification, and raise questions about the purpose and value of concerns and disagreements about relative significance.

In arguing a “role for history,“ Kuhn was proposing a naturalized philosophy of science. That, I argue, is the only viable approach to the philosophy of science. I begin by exhibiting the main general objections to a naturalistic approach. These objections, I suggest, are equally powerful against nonnaturalistic accounts. I review the failure of two nonnaturalistic approaches, methodological foundationism (Carnap, Reichenbach, and Popper) and metamethodology (Lakatos and Laudan). The correct response, I suggest, is to adopt an “evolutionary perspective.” This perspective is defended against one recent critic (Putnam). To argue the plausibility of a naturalistic approach, I next sketch a naturalistic account of theories and of theory choice. This account is then illustrated by the recent revolution in geology. In conclusion I return to Kuhn's question about the role of history in developing a naturalistic theory of science.

The received view of dynamical explanation is that dynamical cognitive science seeks to provide covering-law explanations of cognitive phenomena. By analyzing three prominent examples of dynamicist research, I show that the received view is misleading: some dynamical explanations are mechanistic explanations and in this way resemble computational and connectionist explanations. Interestingly, these dynamical explanations invoke the mathematical framework of dynamical systems theory to describe mechanisms far more complex and distributed than the ones typically considered by philosophers. Therefore, contemporary dynamicist research reveals the need for a more sophisticated account of mechanistic explanation.

The new research program to understand mechanisms in biology has developed rapidly in the last 10 years. Reconsideration of the characterization of mechanisms in biology in the light of this recent work is now in order. This article discusses the perspectival aspect of the characterization of mechanisms (and ways of mitigating rampant perspectivalism), refinements in claims about working entities and kinds of activities, challenges and responses to claims about regularity, productive continuity, and the organizational aspects of a mechanism, and issues about representations of mechanisms in schemas and sketches.

The Reichenbach-Grünbaum thesis of the conventionality of simultaneity is clarified and defended by developing the consequences of the Special Theory when assumptions are not made concerning the one-way speed of light. It is first shown that the conventionality of simultaneity leads immediately to the conventionality of all relative speeds. From this result, the general-length-contraction and time-dilation relations are then derived. Next, the place of time-dilation and length-contraction effects within the Special Theory is examined in the light of the conventionality thesis. The slow-transport method of synchrony is then examined in the light of these results and is shown not to provide an adequate method of uniquely determining the one-way speed of light. Finally, the general ∊-Lorentz transformations for events along the x-axis are derived from three principles: the round-trip light principle, the principle of equal passage times, and the linearity principle. These principles are shown to be independent of one-way velocity assumptions, and thus may form the basis of a Special Theory of Relativity without distant simultaneity assumptions.

Bayesian decision theory can be viewed as the core of psychological theory for idealized agents. To get a complete psychological theory for such agents, you have to supplement it with input and output laws. On a Bayesian theory that employs strict conditionalization, the input laws are easy to give. On a Bayesian theory that employs Jeffrey conditionalization, there appears to be a considerable problem with giving the input laws. However, Jeffrey conditionalization can be reformulated so that the problem disappears, and in fact the reformulated version is more natural and easier to work with on independent grounds.