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.

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.

To arrive at their final evaluation of a manuscript or grant proposal, reviewers must convert a submission’s strengths and weaknesses for heterogeneous peer review criteria into a single metric of quality or merit. I identify this process of commensuration as the locus for a new kind of peer review bias. Commensuration bias illuminates how the systematic prioritization of some peer review criteria over others permits and facilitates problematic patterns of publication and funding in science. Commensuration bias also foregrounds a range of structural strategies for realigning peer review practices and institutions with the aims of science.

The fact that the names of biological species refer independently of identifying descriptions does not support the view of Ghiselin and Hull that species are individuals. Species may be regarded as natural kinds whose members share an essence which distinguishes them from the members of other species and accounts for the fact that they are reproductively isolated from the members of other species. Because evolutionary theory requires that species be spatiotemporally localized their names cannot occur in scientific laws. If natural kind status is denied to species on this ground, it must also be denied to most classes of concrete entities which are now accorded such status.

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 first part of this article finds Craver’s mutual manipulability theory (MM) of constitution inadequate, as it definitionally ties constitution to the feasibility of ideal experiments, which, however, are unrealizable in principle. As an alternative, the second part develops an abductive theory of constitution (NDC), which exploits the fact that phenomena and their constituents are unbreakably coupled via common causes. The best explanation for this fact is the existence of an additional dependence relation, namely, constitution. NDC has important ramifications for constitutional discovery—most notably, that there is no experimentum crucis for constitution, not even under ideal discovery circumstances.

Many scientific models lack an established representation relation to actual targets and instead refer to merely possible processes, background conditions, and results. This article shows how such models can be appraised. On the basis of the discussion of how-possibly explanations, five types of learning opportunities are distinguished. For each of these types, an example—from economics, biology, psychology, and sociology—is discussed. Contexts and purposes are identified in which the use of a model offers a genuine opportunity to learn. These learning opportunities offer novel justifications for modeling practices that fall between the cracks of standard representationalist appraisals of models.

Industry is a major source of funding for scientific research. There is also a growing concern for how it corrupts researchers faced with conflicts of interest. As such, the debate has focused on whether researchers have maintained their integrity. In this article we draw on both the history of medicine and formal modeling to argue that given methodological diversity and a merit-based system, industry funding can bias a community without corrupting any particular individual. We close by considering a policy solution (i.e., independent funding) that may seem to promote unbiased inquiry but that actually exacerbates the problem without additional restrictions.

The sensitive dependence on initial conditions (SDIC) associated with nonlinear models imposes limitations on the models’ predictive power. We draw attention to an additional limitation than has been underappreciated, namely, structural model error (SME). A model has SME if the model dynamics differ from the dynamics in the target system. If a nonlinear model has only the slightest SME, then its ability to generate decision-relevant predictions is compromised. Given a perfect model, we can take the effects of SDIC into account by substituting probabilistic predictions for point predictions. This route is foreclosed in the case of SME, which puts us in a worse epistemic situation than SDIC.

Two major reasons feminists are concerned with science relate to science's social effects: that science can be a powerful ally in the struggle for equality for women; and that all too frequently science has been a generator and perpetuator of inequality. This concern with the social effects of science leads feminists to a different mode of appraising science from the purely epistemic one prized by most contemporary philosophers of science. The upshot, I suggest, is a new program for philosophy of science, a program for a socially responsible philosophy of science.

This paper develops an account of explanation in biology which does not involve appeal to laws of nature, at least as traditionally conceived. Explanatory generalizations in biology must satisfy a requirement that I call invariance, but need not satisfy most of the other standard criteria for lawfulness. Once this point is recognized, there is little motivation for regarding such generalizations as laws of nature. Some of the differences between invariance and the related notions of stability and resiliency, due respectively to Sandra Mitchell and Brian Skyrms, are explored.

A popular and plausible response against Laudan's “pessimistic induction” has been what I call “preservative realism,” which argues that there have actually been enough elements of scientific knowledge preserved through major theory-change processes, and that those elements can be accepted realistically. This paper argues against preservative realism, in particular through a critical review of Psillos's argument concerning the case of the caloric theory of heat. Contrary to his argument, the historical record of the caloric theory reveals that beliefs about the properties of material caloric, rejected by subsequent theories, were indeed central to the successes of the caloric theory. Therefore caloric remains a favorable case for Laudan. Further, I argue that even confirmed cases of preservation do not warrant an inference to truth.

In Epistemic Cultures (1999), Karin Knorr Cetina argues that different scientific fields exhibit different epistemic cultures. She claims that in high energy physics (HEP) individual persons are displaced as epistemic subjects in favor of experiments themselves. In molecular biology (MB), by contrast, individual persons remain the primary epistemic subjects. Using Ed Hutchins’ (1995) account of navigation aboard a traditional US Navy ship as a prototype, I argue that both HEP and MB exhibit forms of distributed cognition. That is, in both fields cognition is distributed across individual persons and complex artifacts. The cognitive system producing the knowledge is heterogeneous. Nevertheless, in both fields we can reserve epistemic agency for the human components of these systems. We do not need to postulate new distributed cognitive agents, let alone ones exhibiting new forms of consciousness.

David Lewis (1979) defends the thesis of the asymmetry of overdetermination: later affairs are seldom overdetermined by earlier affairs, but earlier affairs are usually overdetermined by later affairs. Recently, Carol Cleland (2002) has argued that since the distinctive methodologies of historical science and experimental science exploit different aspects of this asymmetry, the methodology of historical science is just as good, epistemically speaking, as that of experimental science. This paper shows, first, that Cleland's epistemological conclusion does not follow from the thesis of the asymmetry of overdetermination, because overdetermination (in Lewis's sense) is compatible with epistemic underdetermination. The paper also shows, contra Cleland, that there is at least one interesting sense in which historical science is epistemically inferior to experimental science, after all, because local underdetermination problems are more widespread in historical than in experimental science.

Richard Jeffrey's generalization of Bayes' rule of conditioning follows, within the theory of belief functions, from Dempster's rule of combination and the rule of minimal extension. Both Jeffrey's rule and the theory of belief functions can and should be construed constructively, rather than normatively or descriptively. The theory of belief functions gives a more thorough analysis of how beliefs might be constructed than Jeffrey's rule does. The inadequacy of Bayesian conditioning is much more general than Jeffrey's examples of uncertain perception might suggest. The “parameter α” that Hartry Field has introduced into Jeffrey's rule corresponds to the “weight of evidence” of the theory of belief functions.

Reasoning that compares two objects or situations to draw conclusions about previously unknown properties of one of them has traditionally been taken to be ampliative and probabilistic. I propose that it is apodeictic reasoning from a premise about isomorphic structures that is often uncertain, but which we may have good reasons to believe. I characterize the structures and their isomorphism, describe patterns of reasoning appropriate to them, and discuss some complications not immediately obvious.

One of the tasks with which communication engineers are presented is that of devising a mechanism by which a significant sequence of words, a message, produced by somebody, the sender of the message, is reproduced at some other place, with the shortest practical time lag. The reproduction must be such that the receiver of the message will be able to understand what the sender meant by his message, at least, if he knows the sender's language (or, perhaps, his specific use of the language). The following illustration is typical: A writes on a sheet of paper “I love you” and wishes that B, 3000 miles away, should become aware of the full content of this message, with little delay and at a low cost. There will be institutions, in a capitalistic society, which will compete with each other in providing A, for a price, with the required service. Those companies which perform these services most satisfactorily, i.e., with an overall better combination of faithfulness, time lag, and cost, will get the job. The executives of these companies will hire engineers and put them to work on improving this overall combination.

Van Fraassen (1989) argues that Inference to the Best Explanation is incoherent in the sense that adopting it as a rule for belief change will make one susceptible to a dynamic Dutch book. The present paper argues against this. A strategy is described that allows us to infer to the best explanation free of charge.