Michael Strevens

Assumptions of stochastic independence are crucial to statistical models in science. Under what circumstances is it reasonable to suppose that two events are independent? When they are not causally or logically connected, so the standard story goes. But scientific models frequently treat causally dependent events as stochastically independent, raising the question whether there are kinds of causal connection that do not undermine stochastic independence. This paper provides one piece of an answer to this question, treating the simple case of two tossed coins with and without a midair collision

David Lewis, Michael Thau, and Ned Hall have recently argued that the Principal Principle—an inferential rule underlying much of our reasoning about probability—is inadequate in certain respects, and that something called the ‘New Principle’ ought to take its place. This paper argues that the Principle Principal need not be discarded. On the contrary, Lewis et al. can get everything they need—including the New Principle—from the intuitions and inferential habits that inspire the Principal Principle itself, while avoiding the problems that originally caused them to abandon that principle.

Science as we know it is “dappled”. Its picture of the world is a mosaic in which different aspects of the world, different systems, are represented by narrow-scope theories or models that are largely disconnected from one another. The best explanation for this disunity in our representation of the world, Nancy Cartwright has proposed, is a disunity in the world itself: rather than being governed by a small set of strict fundamental laws, events unfold according to a patchwork of principles covering different kinds of systems or segments of reality, each with something less than full omnipotence and with the possibility of anomic indeterminism at the boundaries. This paper attempts to undercut Cartwright’s argument for a dappled world by showing that the motley nature of science, both now and even at the completion of empirical inquiry, can equally well be explained by proponents of the “fundamentalist” view that the universe’s initial conditions and fundamental physical laws determine everything that ever happens.

Bayesian confirmation theory—abbreviated to in these notes—is the predominant approach to confirmation in late twentieth century philosophy of science. It has many critics, but no rival theory can claim anything like the same following. The popularity of the Bayesian approach is due to its flexibility, its apparently effortless handling of various technical problems, the existence of various a priori arguments for its validity, and its injection of subjective and contextual elements into the process of confirmation in just the places where critics of earlier approaches had come to think that subjectivity and sensitivity to context were necessary.

When new theoretical terms are introduced into scientific discourse, prevailing accounts imply, analytic or semantic truths come along with them, by way of either definitions or reference-fixing descriptions. But there appear to be few or no analytic truths in scientific theory, which suggests that the prevailing accounts are mistaken. This paper looks to research on the psychology of natural kind concepts to suggest a new account of the introduction of theoretical terms that avoids both definition and reference-fixing description. At the core of the account is a novel psychological process that I call introjection.

Philip Kitcher has advocated and advanced an influential antireductionist picture of science on which the higher-level sciences pursue their aims largely independently of the lower-level sciences -- a view of the sciences as autonomous. Explanatory autonomy as Kitcher understands it is incompatible with explanatory reductionism, the view that a high-level explanation is inevitably improved by providing a lower-level explanation of its parts. This paper explores an alternative conception of autonomy based on another major theme of Kitcher's philosophy of science: the importance of the division of cognitive labor. That the sciences are in practice autonomous is compatible with reductionism, I argue, if we understand the high-level sciences' systematic explanatory disregard of lower-level details of implementation as practically, rather than intellectually, motivated.

The two major modern accounts of explanation are the causal and unification accounts. My aim in this paper is to provide a kind of unification of the causal and the unification accounts, by using the central technical apparatus of the unification account to solve a central problem faced by the causal account, namely, the problem of determining which parts of a causal network are explanatorily relevant to the occurrence of an explanandum. The end product of my investigation is a causal account of explanation that has many of the advantages of the unification account.

A physical system has a chaotic dynamics, according to the dictionary, if its behavior depends sensitively on its initial conditions, that is, if systems of the same type starting out with very similar sets of initial conditions can end up in states that are, in some relevant sense, very different. But when science calls a system chaotic, it normally implies two additional claims: that the dynamics of the system is relatively simple, in the sense that it can be expressed in the form of a mathematical expression having relatively few variables, and that the the geometry of the system’s possible trajectories has a certain aspect, often characterized by a strange attractor.

The concept of causation plays a central role in many philosophical theories, and yet no account of causation has gained widespread acceptance among those who have investigated its foundations. Theories based on laws, counterfactuals, physical processes, and probabilistic dependence and independence relations (the list is by no means exhaustive) have all received detailed treatment in recent years---{}and, while no account has been entirely successful, it is generally agreed that the concept has been greatly clari{}ed by the attempts. In this magni{}cent book, Woodward aims to give a uni{}ed account of causation and causal explanation in terms of the notion of a manipulation (or intervention, terms which can be read interchangeably). Not only does he produce in my view the most illuminating and comprehensive account of causation on o{}er, his theory also opens a great many avenues for future work in the area, and has rami{}cations for many other areas of philosophy. Making Things Happen ought to be of interest not only to philosophers of causation and philosophers of science, but to any philosopher whose concerns involve assumptions about the nature of causation, laws, or explanation

Research programs regularly compete to achieve the same goal, such as the discovery of the structure of DNA or the construction of a TEA laser. The more the competing programs share information, the faster the goal is likely to be reached, to society's benefit. But the "priority rule"—the scientific norm mandating that the first program to reach the goal in question receive all the credit for the achievement—provides a powerful disincentive for programs to share information. How, then, is the clash between social and self interest resolved in scientific practice? This paper investigates what Robert Merton called science's "communist" norm, which mandates universal sharing of knowledge, and uses mathematical models of discovery to argue that a communist regime may be on the whole advantageous and fair to all parties, and so might be implemented by a social contract that all scientists would be willing to sign.

According to principles of probability coordination, such as Miller's Principle or Lewis's Principal Principle, you ought to set your subjective probability for an event equal to what you take to be the objective probability of the event. For example, you should expect events with a very high probability to occur and those with a very low probability not to occur. This paper examines the grounds of such principles. It is argued that any attempt to justify a principle of probability coordination encounters the same difficulties as attempts to justify induction. As a result, no justification can be found.

This paper justifies the inference of probabilities from symmetries. I supply some examples of important and correct inferences of this variety. Two explanations of such inferences -- an explanation based on the Principle of Indifference and a proposal due to Poincaré and Reichenbach -- are considered and rejected. I conclude with my own account, in which the inferences in question are shown to be warranted a posteriori, provided that they are based on symmetries in the mechanisms of chance setups.

It is argued that the relation of instance confirmation has a role to play in scientific methodology that complements, rather than competing with, a modern account of inductive support such as Bayesian confirmation theory. When an instance confirms a hypothesis, it provides inductive support, but it also provides two things that other inductive supporters normally do not: first, a connection to “empirical data” that makes science epistemically special, and second, inductive support not only for the hypothesis as a whole, but for its parts. Further, when it is conceived in the right way, instance confirmation can duck the arguments most often thought to refute it. A causal account of instantiation, thus of instance confirmation, is offered that looks to deliver on all of the foregoing promises.

It seems very important to us whether or not a generalization offers counter-factual support—but why? Surely what happens in other possible worlds can neither help nor hurt us? This paper explores the question whether counter-factual support does, nevertheless, have some practical value. (The question of theoretical value will be addressed but then put aside.) The following thesis is proposed: the counterfactual-supporting generalizations are those for which there exists a compact and under normal circumstances knowable basis determining the fine-grained pattern of actual variation between the properties associated by the generalization (e.g., for the generalization Fs tend to be G, the exact circumstances under which any particular F is G); further, the better we understand the basis and the scope of the support offered, the greater our knowledge of fine-grained variation. We care about counterfactual support because we care about actual fine-grained variation