The idea that the use of instruments in science is theory-dependent seems to threaten the extent to which the output of those instruments can act as an independent arbiter of theory. This issue is explored by studying an early use of the electron microscope to observe dislocations in crystals. It is shown that this usage did indeed involve the theory of the electron microscope but that, nevertheless, it was possible to argue strongly for the experimental results, the theory of dislocations being tested, and the theory of the instrument, all at the same time.

1. Probabilism. Statistics is probably the last discipline the ordinary person would associate with ideological wars, but one has been raging there for the last thirty years and more. Until recently the Classical, also known as Frequentist, theory of statistical inference dominated. But gradually a quite different approach has attracted adherents. This, named the Bayesian theory after the eighteenth-century English clergyman, Thomas Bayes, is a phoenix, reborn from the theory of inductive inference dominant from the mid-eighteenth to the late nineteenth century, which said that the measure of confidence proper to employ in an uncertain proposition is its probability (the idea goes back well beyond Bayes; in his great Ars Conjectandi, published posthumously in 1715, James Bernoulli stated that probability is degree of certainty (Part IV, Chapter II)).

Various senses in which laws of nature are supposed to be “universal” are distinguished. Conditions designed to capture the content of the more important of these senses are proposed and the relations among these conditions are examined. The status of universality requirements is briefly discussed.

In this paper we argue that an operational organism concept can help to overcome the structural deficiency of mathematical models in biology. In our opinion, the structural deficiency of mathematical models lies mainly in our inability to identify functionally relevant biological characters in biological systems, and not so much in a lack of adequate mathematical representations of biological processes. We argue that the problem of character identification in biological systems is linked to the question of a properly formulated organism concept. Lastly, we demonstrate how a decomposition of an organism into independent characters in the context of a specific biological process—such as adaptation by means of natural selection—depends on the dynamical properties and invariance conditions of the equations that describe this process.

In this paper, I shall examine attempts to furnish formal models for deductive scientific explanation. All such attempts have had certain defects. The most serious of these defects is to be found in the fact that the extant models seem to be formally restrictive in ways that do not allow any obvious generalization of their conditions which will encompass the full range of all those scientific explanations which must be considered plausible candidates for translation into deductive models.

In this essay, I criticize Laudan's view that methodological rules in science are best understood as hypothetical imperatives, for example, to realize cognitive aim A, follow method B. I criticize his idea that such rules are best evaluated by a naturalized philosophy of science which collects the empirical evidence bearing on the soundness of these rules. My claim is that this view yields a poor explanation of (1) the role of methodological rules in establishing the rationality of scientific practices, (2) the debates scientists have over rival methodological rules, and (3) the reasons on the basis of which scientists actually embrace, challenge, or transform methodological rules. A better explanation is provided by treating at least some methodological rules as foundational standards of theory-appraisal which define the criteria of scientific truth, knowledge, evidence, proof, explanations, etc., for a certain tradition or community of scientific practice. Such standards are in turn grounded or challenged on the basis of their coherence or incoherence with a wider body of scientific judgments. This view leads away from a naturalized philosophy of science back to its more classical epistemological conception.

This paper examines commonly offered arguments to show that human behavior is not deterministic because it is not predictable. These arguments turn out to rest on the assumption that deterministic systems must be governed by deterministic laws, and that these give rise to predictability “in principle” of determined events. A positive account of determinism is advanced and it is shown that neither of these assumptions is true. The relation between determinism, laws, and prediction in practice is discussed as a question in scientific epistemology.

Kevin Elliott and others separate two common arguments for the legitimacy of societal values in scientific reasoning as the gap and the error arguments (respectively, the arguments from underdetermination and from inductive risk). This article poses two questions: How are these two arguments related, and what can we learn from their interrelation? I contend that we can better understand the error argument as nested within the gap because the error is a limited case of the gap with narrower features. Furthermore, this nestedness provides philosophers with conceptual tools for analyzing more robustly how values pervade science.

A twofold taxonomy for emergence is presented into which a variety of contemporary accounts of emergence fit. The first taxonomy consists of inferential, conceptual, and ontological emergence; the second of diachronic and synchronic emergence. The adequacy of weak emergence, a computational form of inferential emergence, is then examined and its relationship to conceptual emergence and ontological emergence is detailed.

The central concern of this article is whether the semantic approach has the resources to appropriately capture the core tenets of structural realism. Chakravartty (2001) has argued that a realist notion of correspondence cannot be accommodated without introducing a linguistic component, which undermines the approach itself. We suggest that this worry can be addressed by an appropriate understanding of the role of language in this context. The real challenge, however, is how to incorporate the core notion of ‘explanatory approximate truth’ in such a way that the emphasis on structure is retained.

As scientists begin to study increasingly complex questions, many have turned to computer simulation to assist in their inquiry. This methodology has been challenged by both analytic modelers and experimentalists. A primary objection of analytic modelers is that simulations are simply too complicated to perform model verification. From the experimentalist perspective it is that there is no means to demonstrate the reality of simulation. The aim of this paper is to consider objections from both of these perspectives, and to argue that a proper understanding and application of robustness analysis is able to resolve them.

This article argues that at least very many judgments of homology rest on prior attributions of selected-effect (SE) function, and that many of the “parts” of biological systems that are rightly classified as homologous are constituted by (are so classified in virtue of) their consequence etiologies. We claim that SE functions are often used in the prior identification of the parts deemed to be homologous and are often used to differentiate more restricted homologous kinds within less restricted ones. In doing so, we discuss recent criticism of this view that has been offered (especially that offered by Paul Griffiths).

Both Representation Theorem Arguments and Dutch Book Arguments support taking probabilistic coherence as an epistemic norm. Both depend on connecting beliefs to preferences, which are not clearly within the epistemic domain. Moreover, these connections are standardly grounded in questionable definitional/metaphysical claims. The paper argues that these definitional/metaphysical claims are insupportable. It offers a way of reconceiving Representation Theorem arguments which avoids the untenable premises. It then develops a parallel approach to Dutch Book Arguments, and compares the results. In each case preference-defects serve as a diagnostic tool, indicating purely epistemic defects.

When philosophers and psychologists think about consciousness, they generally focus on one or more of three features: phenomenality (how experiences feel), intentionality (that experiences are “of” something, that experiences mean something), and introspectibility (our awareness of the phenomenality and intentionality of experience). Using examples from empirical psychology and neuroscience, I argue that consciousness is not a unitary state, that, instead, these three features characterize different and dissociable states, which often happen to occur together. Understanding these three features as dissociable from each other will resolve philosophical disputes and facilitate scientific investigation.

John Dupré (1984) has recently criticized the theory of probabilistic causality developed by, among others, Good (1961–62), Suppes (1970), Cartwright (1979), and Skyrms (1980). He argues that there is a tension or incompatibility between one of its central requirements for the presence of a causal connection, on the one hand, and a feature of the theory pointed out by Elliott Sober and me (1983), on the other. He also argues that the requirement just alluded to should be given up. I defend the theory against Dupré‘s criticisms and conclude with comments on Dupré‘s appraisal of the bearing of his arguments on the nature of probabilistic causal laws.

Uncertainty relations and complementarity of canonically conjugate position and momentum observables in quantum theory are discussed with respect to some general coupling properties of a function and its Fourier transform. The question of joint localization of a particle on bounded position and momentum value sets and the relevance of this question to the interpretation of position-momentum uncertainty relations is surveyed. In particular, it is argued that the Heisenberg interpretation of the uncertainty relations can consistently be carried through in a natural extension of the usual Hilbert space frame of the quantum theory.

Various laws and theories in the natural and social sciences are presented with a view to discerning the syntactic and semantic characteristics of many idealizations in science. Three different kinds of idealizations are discussed: ideal conditions, ideal cases, and idealized theories. An ideal condition is a formula in which state variables occur, whose existential closure is false, and for which there is another formula that can be constructed out of the original formula such that the existential closure of the new formula is true. An ideal case is a statement which is logically equivalent to a universal conditional which has an ideal condition as its antecedent. And an idealized theory is a set of false universal conditional statements. Alternative syntactic and semantic analyses are viewed and criticized.

A consideration of some basic problems that arise in the attempt to provide an adequate characterization of statistical explanation is taken to show that an understanding of the nature of scientific explanation requires us to deal with the philosophical problems connected with the nature of prior probabilities.

An invisible hand seems to play an important role in science. In this paper I set out the general structure of invisible-hand explanations, counter some objections that have been raised to them, and detail the role that they play in science. The most important issue is the character of the mechanisms that are supposed to bring about invisible-hand effects.