Deidealization as a topic in its own right has attracted remarkably little philosophical interest despite the extensive literature on idealization. One reason for this is the often implicit assumption that idealization and deidealization are, potentially at least, reversible processes. We question this assumption by analyzing the challenges of deidealization within a menu of four broad categories: deidealizing as recomposing, deidealizing as reformulating, deidealizing as concretizing, and deidealizing as situating. On closer inspection, models turn out much more inflexible than the reversal thesis would have us believe, and deidealization emerges as a creative part of modeling.

Biological processes are often explained by identifying the underlying mechanisms that generate a phenomenon of interest. I characterize a basic account of mechanistic explanation and then present three challenges to this account, illustrated with examples from molecular biology. The basic mechanistic account (1) is insufficient for explaining nonsequential and nonlinear dynamic processes, (2) is insufficient for explaining the inherently stochastic nature of many biological mechanisms, and (3) fails to give a proper framework for analyzing organization. I suggest that biological processes are best approached as a multidimensional gradient—with some processes being paradigmatic cases of mechanisms and some being marginal cases.

I address a question recently raised by Simon Saunders concerning the relationship between the space-time structure of Newton-Cartan theory and that of what I will call “Maxwell-Huygens space-time.” This discussion will also clarify a connection between Saunders’s work and a recent paper by Eleanor Knox.

There is sufficient evidence from more recent experiments in psychology that equal retinal stimulus-elements do not lead to equal experiences and reactions except under certain rather specific conditions. An unsophisticated observer will find himself surprised to be able to cover with his own finger a person entering the door of his living room. When the finger is moved to the right or left, thus doing away with the precise retinal coincidence with the person, the observer will soon become unable to recognize intuitively the actual retinal stimulus equality of the two distant things. This usually holds even for the case when he is making every inner effort towards an antagonistic, analytic perceptual attitude of the type which is used by painters or draughtsmen in order to represent the environmental situation in a similar way as it would project itself on a photographic plate or on a retina.

The argument from inductive risk attempts to show that practical and ethical costs of errors should influence standards of evidence for accepting scientific claims. A common objection charges that this argument presupposes a behavioral theory of acceptance that is inappropriate for science. I respond by showing that the argument from inductive risk is supported by a nonbehavioral theory of acceptance developed by Cohen, which defines acceptance in terms of premising. Moreover, I argue that theories designed to explain how acceptance can be guided exclusively by epistemic values suffer from difficulties that do not afflict Cohen’s theory.

This paper defends a realist interpretation of theories and a modest realism concerning the existence of quantities as providing the best account both of the logic of quantity concepts and of scientific measurement practices. Various operationist analyses of measurement are shown to be inadequate accounts of measurement practices used by scientists. We argue, furthermore, that appeals to implicit definitions to provide meaning for theoretical terms over and above operational definitions fail because implicit definitions cannot generate the requisite descriptive content. The special case of establishing a temperature scale is examined to show that nonrealist accounts fail to provide insight into the theoretical connections that scientific laws postulate to hold among quantities.

Glymour's “bootstrap” account of confirmation is designed to provide an analysis of evidential relevance, which has been a serious problem for hypothetico-deductivism. As set out in Theory and Evidence, however, the “bootstrap” condition allows confirmation in clear cases of evidential irrelevance. The difficulties with Glymour's account seem to be due to a basic feature which it shares with hypothetico-deductive accounts, and which may explain why neither can give a satisfactory analysis of evidential relevance.

In a number of essays published over the last decade or so, W. V. Quine has made some interesting suggestions concerning the ontology of theories. (3; 4; 5; 6; 7; 9; 10) If I understand him correctly, one of his principal objects has been to formulate a criterion by means of which one can correctly decide what are the ontological commitments of any given theory. My aim in this paper is to reveal what I think are inadequacies in Quine's criterion and to indicate the direction in which a proper formulation lies.

Where E is the proposition that [If H and O were true, H would explain O], William Roche and Elliot Sober have argued that P(H | O&E) = P(H | O). In this article I argue that not only is this equality not generally true, it is false in the very kinds of cases that Roche and Sober focus on, involving frequency data. In fact, in such cases O raises the probability of H only given that there is an explanatory connection between them.

Reductionism is a metaphysical thesis, a claim about explanations, and a research program. The metaphysical thesis reductionists advance (and antireductionists accept) is that all facts, including all biological facts, are fixed by the physical and chemical facts; there are no non-physical events, states, or processes, and so biological events, states and processes are “nothing but” physical ones. The research program can be framed as a methodological prescription which follows from the claim about explanations. Antireductionism does not dispute reductionism's metaphysical claim, but rejects the explanatory claim and so the methodological moral. To a first approximation what reductionists and anti-reductionists disagree about is whether explanations in functional biology can be or need to be explained or completed or perhaps replaced by explanations in terms of molecular biology. And this disagreement over the adequacy of explanations in functional biology drives a significant methodological disagreement with consequence for the research program of biology.

An argument opposing the unrestricted use of quantification in modal logic has been put forward by Quine. Central to this argument are the two phrases,

(1) The Morning Star,

(2) The Evening Star.

One form of the argument is obtained by considering the following two statements:

(3) It is necessary that the Morning Star is identical with the Morning Star.

(4) It is not necessary that the Evening Star is identical with the Morning Star.

On the received view, the Representational Theory of Measurement reduces measurement to the numerical representation of empirical relations. This account of measurement has been widely criticized. In this article, I provide a new interpretation of the Representational Theory of Measurement that sidesteps these debates. I propose to view the Representational Theory of Measurement as a library of theorems that investigate the numerical representability of qualitative relations. Such theorems are useful tools for concept formation that, in turn, is one crucial aspect of measurement for a broad range of cases in linguistics, rational choice, metaphysics, and the social sciences.

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many times over, and because computing resources are finite, uncertainty assessment is more feasible using models that demand less computer processor time. Such models are generally simpler in the sense of being more idealized, or less realistic. So modelers face a trade-off between realism and uncertainty quantification. Seeing this trade-off for the important epistemic issue that it is requires a shift in perspective from the established simplicity literature in philosophy of science.

Does Hume's skeptical critique of inductive reasoning depend for its force on the assumption that such reasoning is a matter of following general rules? I explore this question in connection with John Norton's “Material Theory of Induction.”

Science Bought and Sold collects a large portion of the most relevant works on the ‘economics of scientific knowledge production,’ as well as other more recent and unpublished papers on the topic, and the long introductory essay by the editors is an illuminating guide to the field. In this critical notice, I argue that economic theorising about scientific research is providing a peaceful meeting point for many of the combatants in the ‘science wars,’ one from which both epistemic and political questions about science can be more rationally set forth.

This paper examines the “causal theory of reference”, according to which science aims at the discovery of “essences” which are the objects of reference of natural kind terms (among others). This theory has been advanced as an alternative to traditional views of “meaning”, on which a number of philosophical accounts of science have relied, and which have been criticized earlier by the present author. However, this newer theory of reference is shown to be equally subject to fatal internal difficulties, and to be incompatible with actual science as well. Indeed, it rests on assumptions which it shares with the purportedly opposing theory of meaning. Behind those common assumptions is the supposition that the nature of science can be illuminated by an examination of alleged necessities of language which are independent of the results and methods of scientific inquiry. An alternative view of science is proposed, according to which the goals and language of science develop as integral parts of the process of demarcating science from non-science, a process in which the notion of a “reason” gradually assumes a decisive role. On this view, the comparability, competition, and development of scientific ideas are understood without reliance on either common “meanings” or common “references” as fundamental tools of analysis.

David Sloan Wilson has recently revived the idea of a group mind as an application of group selectionist thinking to cognition. Central to my discussion of this idea is the distinction between the claim that groups have a psychology and what I call the social manifestation thesis—a thesis about the psychology of individuals. Contemporary work on this topic has confused these two theses. My discussion also points to research questions and issues that Wilson's work raises, as well as their connection to externalist conceptions of the mind familiar since the work of Putnam and Burge.

This paper discusses Simpson's paradox and the problem of positive relevance in probabilistic causality. It is argued that Cartwright's solution to Simpson's paradox fails because it ignores one crucial form of the paradox. After clarifying different forms of the paradox, it is shown that any adequate solution to the paradox must allow a cause to be both a negative cause and a positive cause of the same effect. A solution is then given that can handle the form of the paradox that Cartwright's solution ignored, and allows causes to be both a positive and a negative cause of an effect.

The primacy of physics generates a philosophical problem that the naturalist must solve in order to be entitled to an egalitarian acceptance of the ontological commitments he or she inherits from the special sciences and fundamental physics. The problem is the generalized causal exclusion argument. If there is no genuine causation in the domains of the special sciences but only in fundamental physics then there are grounds for doubting the existence of macroscopic objects and properties, or at least the concreteness of them. The aim of this paper is to show that the causal exclusion problem derives its force from a false dichotomy between Humeanism about causation and a notion of productive or generative causation based on a defunct model of the physical world.

After sorting different structuralist claims, I argue that structural realist ideas are instantiated in the social sciences, providing both clarification of social science research and support for some components of structural realism. My main focus is on three distinct ways that the social sciences can be about structural relations—exemplified by claims about social structure, reduced form structures in causal modeling, and equilibrium explanations—and on the implication of structuralist ideas for thinking about issues concerning causal explanation and nonreductive pictures of the unity of the science.