One of the motivations for Salmon's (1984) causal theory of explanation was the explanatory irrelevance exhibited by many arguments conforming to Hempel's covering-law models of explanation. However, the nexus of causal processes and interactions characterized by Salmon is not rich enough to supply the necessary conception of explanatory relevance. Salmon's (1994) revised theory, which is briefly criticized on independent grounds, fares no better. There is some possibility that the two-tiered structure of explanation described by Salmon (1984) may be pressed into service, but more work would have to be done. Ironically, Salmon's difficulties are similar to those suffered by his seventeenth-century predecessors.

Kenneth C. Schaffner's paper is an important contribution to the literature on behavioral genetics and on genetics in general. Schaffner has a long record of injecting real molecular biology into philosophical discussions of genetics. His treatments of the reduction of Mendelian to molecular genetics (Schaffner 1967, 1969) first drew philosophical attention to the problems of detail that have fuelled both anti-reductionism (Hull 1974, Kitcher 1984) and more sophisticated models of theory reduction (Sarkar 1992, Schaffner 1993). An injection of molecular detail into discussions of genetics is particularly necessary at the present time, when so many philosophers seem happy to discuss the philosophical and ethical implications of molecular biology using gene concepts derived from evolutionary biology (i.e., the ‘evolutionary gene concept’ of G.C Williams (Williams 1966, Dawkins 1982)). Schaffner has long advocated the view that the philosophy of biology should be more than the philosophy of evolution. This paper shows how radically a picture of gene action derived from molecular biology undercuts the popular picture associated with a more evolutionary view of genes as units of heredity or as ‘difference-makers’ mediated by the ‘black box’ of development (Sterelny and Kitcher 1988).

Experiments are commonly thought to have epistemic privilege over simulations. Two ideas underpin this belief: first, experiments generate greater inferential power than simulations, and second, simulations cannot surprise us the way experiments can. In this article I argue that neither of these claims is true of experiments versus simulations in general. We should give up the common practice of resting in-principle judgments about the epistemic value of cases of scientific inquiry on whether we classify those cases as experiments or simulations, per se. To the extent that either methodology puts researchers in a privileged epistemic position, this is context sensitive.

We use evidence from cognitive psychology and the history of science to examine the issue of the theory-ladenness of perceptual observation. This evidence shows that perception is theory-laden, but that it is only strongly theory-laden when the perceptual evidence is ambiguous or degraded, or when it requires a difficult perceptual judgment. We argue that debates about the theory-ladenness issue have focused too narrowly on the issue of perceptual experience, and that a full account of the scientific process requires an examination of theory-ladenness in attention, perception, data interpretation, data production, memory, and scientific communication. We conclude that the evidence for theory-ladenness does not lead to a relativist account of scientific knowledge.

Achinstein, Putnam, and others have urged the rejection of the received view on theories (which construes theories as axiomatic calculi where theoretical terms are given partial observational interpretations by correspondence rules) because (i) the notion of partial interpretation cannot be given precise formulation, and (ii) the observational-theoretical distinction cannot be drawn satisfactorily. I try to show that these are the wrong reasons for rejecting the received view since (i) is false and it is virtually impossible to demonstrate the truth of (ii). Nonetheless, the received view should be rejected because it obscures a number of epistemologically important features of scientific theorizing. I show this by sketching an alternative analysis which reveals some of these features and gives a more faithful picture of scientific theorizing.

Scientists typically use a variety of representations, including different kinds of figures, to present and defend hypotheses. In order to understand the justification of scientific hypotheses, it is essential to understand how visual representations contribute to scientific arguments. Since the logical understanding of arguments involves the truth or falsity of the representations involved, visual representations must have the capacity to bear truth in order to be genuine components of arguments. By drawing on Goodman's analysis of symbol systems, and on Tarski's work on truth, I show that the figures used in science meet this criterion.

This paper explores the consequences of the two most prominent forms of contemporary structural realism for the notion of objecthood. Epistemic structuralists hold that we can know structural aspects of reality, but nothing about the natures of unobservable relata whose relations define structures. Ontic structuralists hold that we can know structural aspects of reality, and that there is nothing else to know—objects are useful heuristic posits, but are ultimately ontologically dispensable. I argue that structuralism does not succeed in ridding a structuralist ontology of objects.

Maynard Smith notes that he provides a natural history and not a philosophical analysis of the use of concepts of information in contemporary biology. Just a natural history, however rich, would do little to resolve the ongoing controversy about the role of these concepts in biology. None of the disputants deny that the biological use of these concepts is pervasive. The dispute is about whether these concepts—and the framework in which they are embedded—continue to be of explanatory value in contemporary biology. Fortunately, Maynard Smith does much more than provide a natural history: his contribution is also a sustained attempt to justify many (though not all) of the uses of information in biology.

This paper defends a counterfactual account of explanation, according to which successful explanation requires tracing patterns of counterfactual dependence of a special sort, involving what I call active counterfactuals. Explanations having this feature must appeal to generalizations that are invariant—stable under certain sorts of changes. These ideas are illustrated by examples drawn from physics and econometrics.

Does the viability of the discovery program depend on showing either (1) that methods of generating new problem solutions, per se, have special probative weight (the per se thesis); or, (2) that the original conception of an idea is logically continuous with its justification (anti-divorce thesis)? Many writers have identified these as the key issues of the discovery debate. McLaughlin, Pera, and others recently have defended the discovery program by attacking the divorce thesis, while Laudan has attacked the discovery program by rejecting the per se thesis. This disagreement over the central issue has led to communication breakdown. I contend that both friends and foes of discovery mistake the central issues. Recognizing a form of divorce helps rather than hurts the discovery program. However, the per se thesis is not essential to the program (nor is the related debate over novel prediction); hence, the status of the per se thesis is a side issue. With these clarifications in hand, we can proceed to the next stage of the discovery debate–the development (or revival) of a generative conception of justification which goes beyond consequentialism to forge a strong linkage of generation (or rather, generatability) with justification.

What makes a measure of well-being valid? The dominant approach today, construct validation, uses psychometrics to ensure that questionnaires behave in accordance with background knowledge. Our first claim is interpretive—construct validation obeys a coherentist logic that seeks to balance diverse sources of evidence about the construct in question. Our second claim is critical—while in theory this logic is defensible, in practice it does not secure valid measures. We argue that the practice of construct validation in well-being research is theory avoidant, favoring a narrow focus on statistical tests while largely ignoring relevant philosophical considerations.

This paper is structured around the three elements of the title. Section 2 claims that (a) structures need objects and (b) scientific structuralism should focus on in re structures. Therefore, pure structuralism is undermined. Section 3 discusses whether the world has ‘excess structure’ over the structure of appearances. The main point is that the claim that only structure can be known is false. Finally, Section 4 argues directly against ontic structural realism that it lacks the resources to accommodate causation within its structuralist slogan.

Although there is a complete consensus among working physicists with respect to the practical and operational meanings of quantum states, and also a rather loosely formulated general philosophic view called the Copenhagen interpretation, a great deal of confusion and divergence of opinions exist as to the details of the measurement process and its effects upon quantum states. This paper reviews the current expositions of the measurement problem, limiting itself for lack of space primarily to the writings of physicists; it calls attention to inconsistencies and proposes resolutions. Except for a summary of the properties of statistical matrices which are needed in Part II, the first part is non-mathematical and deals largely with two kinds of probability, reducible and irreducible probabilities, which need to be distinguished for a proper understanding of the measurement act.

A Bayesian account of independent evidential support is outlined. This account is partly inspired by the work of C. S. Peirce. I show that a large class of quantitative Bayesian measures of confirmation satisfy some basic desiderata suggested by Peirce for adequate accounts of independent evidence. I argue that, by considering further natural constraints on a probabilistic account of independent evidence, all but a very small class of Bayesian measures of confirmation can be ruled out. In closing, another application of my account to the problem of evidential diversity is also discussed.

Several authors have recently contended that modern statistical theory provides a powerful argument in favor of the view that if scientists accept or reject hypotheses at all they do so only in a behavioral sense—i.e., in a sense which reduces “accepting P” to “acting on the basis of P relative to an objective O”. In this paper, the argument from statistics in favor of a behavioral view is outlined; an interpretation of two statistical procedures (Bayes method and signifigance testing) is offered which does not entail a behavioral analysis of “accepting a hypothesis”; and the conclusion that non-behavioral analyses of belief are compatible with the application of current statistical theory in the sciences is tentatively advanced.

In earlier work I have claimed that emotion and some emotions are not ‘natural kinds’. Here I clarify what I mean by ‘natural kind’, suggest a new and more accurate term, and discuss the objection that emotion and emotions are not descriptive categories at all, but fundamentally normative categories.

This paper supplements an earlier one (Wassermann 1978b). Its views aim to reinforce those of Lewontin and other prominent evolutionists, but differ significantly from the opinions of some philosophers of science, notably Popper (1957) and Olding (1978). A basic distinction is made between ‘laws’ and ‘theories of mechanisms’. The ‘Theory of Evolution’ is not characterized by laws, but is viewed here as a hypertheory which explains classifiable evolutionary phenomena in terms of subordinate classifiable theories of ‘evolution-specific mechanisms’ (ESMs), each of which could apply to a host of species. Adaptations could result from ESMs that are rooted in molecular complementarities. The status of optimization theories that aim to predict best adapted states of organisms or populations is also discussed.

The problem of the reality and nature of novelty in the universe has long engaged the attention of philosophers. “There is nothing new under the sun” is one ancient weighty utterance. On the other hand, “you cannot step twice into the same river”, said Heraclitus, for in the interval between your first and second steps, the river has changed and you have changed. One recent attempt to analyze this problem and other affiliated problems, is the theory of emergence.

Ecologist Richard Levins argues population biologists must trade-off the generality, realism, and precision of their models since biological systems are complex and our limitations are severe. Steven Orzack and Elliott Sober argue that there are cases where these model properties cannot be varied independently of one another. If this is correct, then Levins's thesis that there is a necessary trade-off between generality, precision, and realism in mathematical models in biology is false. I argue that Orzack and Sober's arguments fail since Levins's thesis concerns the pragmatic features of model building not just the formal properties of models.

Inflationary cosmology won a large following on the basis of the claim that it solves various problems that beset the standard big bang model. We argue that these problems concern not the empirical adequacy of the standard model but rather the nature of the explanations it offers. Furthermore, inflationary cosmology has not been able to deliver on its proposed solutions without offering models which are increasingly complicated and contrived, which depart more and more from the standard model it was supposed to improve upon, and which sever the connection between cosmology and particle physics that initially made the inflationary paradigm so attractive. Nevertheless, inflationary cosmology remains a promising research program, not least because it offers an explanation of the origin of the density perturbations that seeded the formation of galaxies and other cosmic structures. Tests of this explanation are underway and may settle the issue of whether inflation played an important role in the early universe.