I am grateful for the opportunity to share with you some of my convictions, anxieties, and hopes. My central conviction is the immense value of a mental outlook which can be called by the suggestive and historically recognized name Enlightenment, whose exact characterization, however, requires careful discussion. It is an outlook widely shared, though with innumerable personal variations, by the members of the Philosophy of Science Association, by the broader community of scientists, science teachers, and philosophers, and by an ill-defined part of the general public. My anxieties are that the cultivation and the influence of this mental outlook in our nation and in the world are threatened in various ways, among them ideological attacks, slackening of educational standards, and demoralization of its adherents. My central hope is that Enlightenment will prevail despite these threats.

This paper explores versions of agency or manipulability theories of causation and argues that they are unacceptable both for the well-known reasons of their anthropomorphism, limited scope, and circularity and because they are subsumed by an alternative “independence” theory of causation, which is free of these difficulties.

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.

In this paper I will examine the history of the first three, of a sequence of five, experiments performed by the Mann-O'Neill collaboration at the Princeton-Pennsylvania Accelerator. The experiments were conducted over a period of four years and measured aspects of K+ meson decay. Each of the experiments was done with essentially the same basic apparatus, with modifications for each of the specific measurements. We will see the increasing expertise of the experimenters as the experiments progressed. The third measurement was technically more difficult and built upon the acquired knowledge of how the experimental apparatus worked.

This paper discusses a series of case studies on observations, experiments, and the theoretical interpretation between 1890 and 1960 of a shift of dark Fraunhofer lines in the solar spectrum. I argue for the use of flow charts to analyze interconnections and to identify sequences of research strategies. Also I advocate using a newly-developed tool called “block diagram” representation of experimental systems as an appropriate method to identify recurrent patterns in the interplay of instrumentation, experiment, and theory in research episodes.

Starting with some illustrative examples, I develop a systematic account of a specific type of experimentation—an experimentation which is not, as in the “standard view”, driven by specific theories. It is typically practiced in periods in which no theory or—even more fundamentally—no conceptual framework is readily available. I call it exploratory experimentation and I explicate its systematic guidelines. From the historical examples I argue furthermore that exploratory experimentation may have an immense, but hitherto widely neglected, epistemic significance.

Based on the historical case of galvanic experimentation in Germany, I identify five types of experimentation which explored and shaped the new phenomenon rather than tested theoretical predictions. Verification evaluated initial reports of Galvani's phenomenon. Simplification reduced the experimental protocol to the fewest and most basic steps. Optimization found experimental conditions that magnified the observed effect. Exploration tested a wide variety of metals, animals or configurations. Application modified the experiment to address unresolved related problems. Attempts to derive laws of the phenomenon or establish its underlying causes were unsuccessful primarily due to the great variability of the experimental results.

The method of positron emission tomography (PET imaging) illustrates the circular logic popular in subtractive neuroimaging and linear reductive cognitive psychology. Both require that strictly feed-forward, modular, cognitive components exist, before the fact, to justify the inference of particular components from images (or other observables) after the fact. Also, both require a “true” componential theory of cognition and laboratory tasks, before the fact, to guarantee reliable choices for subtractive contrasts. None of these possibilities are likely. Consequently, linear reductive analysis has failed to yield general, reliable, componential accounts.

New research tools such as PET can produce dramatic results. But they can also produce dramatic artifacts. Why is PET to be trusted? We examine both the rationale that justifies interpreting PET as measuring brain activity and the strategies for interpreting PET results functionally. We show that functional ascriptions with PET make important assumptions and depend critically on relating PET results to those secured through other research techniques.

The relationship between economics and the philosophy of natural science has changed substantially during the last few years. What was once exclusively a one-way relationship from philosophy to economics now seems to be much closer to bilateral exchange. The purpose of this paper is to examine this new relationship. First, I document the change. Second, I examine the situation within contemporary philosophy of science in order to explain why economics might have its current appeal. Third, I consider some of the issues that might jeopardize the success of this philosophical project.

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.

The failure of the attempt by Michael Polanyi to capture the social organization of science by comparing it to the operation of a market bears salutary lessons for modern philosophers of science in their rush to appropriate market models and metaphors. In this case, an initially plausible invisible hand argument ended up as crude propaganda for the uniquely privileged social support of science.

Goldman joins the ranks of epistemologists, philosophers, and science studies scholars trying to use economic models of science. For Goldman, these models are part of social rather than individual epistemics. His hope is that these models will illustrate that non-epistemic goals of individual scientists such as professional success do not necessarily undermine epistemic aims of science such as the acquisition of truth. This paper shows that there are inconsistencies between Goldman's individual and social epistemics, that these models do not live up to Goldman's standards of evaluation, and that the economic models that Goldman uses are not social.

Assessing the hydrogeological modeling at the Yucca Mountain and Maxey Flats nuclear repositories reveals a number of important ways in which theory choice can go wrong. The two cases suggest that there are at least six important criteria for evaluating the suitability of scientific models to be used for predictions intended to serve public policy. More generally, the paper argues that applied philosophy of science, as practiced in environmental policymaking, requires one to employ ethical rationality as well as scientific rationality, to heed the advice of the moral philosopher, not merely that of the epistemologist or philosopher of science.

The problem of hunger is a problem of the inequitable distribution of food entitlements. I argue that ‘modern’ science is implicated in the current form of this problem and that it can only contribute to its resolution, rather than exacerbation, if the forms of its implication are acknowledged. But this requires acceptance of the claim that science is not value-neutral. In part this paper is also an examination, in a particular problem context, of some dimensions of disputes over the value neutrality of science.

Each of the three papers offers a different model for the role philosophers of science might play in consideration of the relations of science to society. These comments address common themes in the three papers, articulate further questions for each, and suggest some historical shifts that require different forms of philosophical engagement now than in the early part of the century.

This short paper serves as an introduction to a debate between representatives of two fundamentally different points of view regarding the nature of scientific inference. Colin Howson and Peter Urbach represent a Bayesian point of view and Deborah Mayo represents a version of classical statistics called error statistics. The paper begins by reviewing earlier versions of the same two points of view due to Rudolf Carnap and Hans Reichenbach, respectively. After a few remarks about philosophical approaches to understanding scientific reasoning between 1960 and 1980, I turn to substantive differences between the two approaches.

The Bayesian theory is outlined and its status as a logic defended. In this it is contrasted with the development and extension of Neyman-Pearson methodology by Mayo in her recently published book (1996). It is shown by means of a simple counterexample that the rule of inference advocated by Mayo is actually unsound. An explanation of why error-probablities lead us to believe that they supply a sound rule is offered, followed by a discussion of two apparently powerful objections to the Bayesian theory, one concerning old evidence and the other optional stopping.