Michael Strevens

Complexity theory attempts to explain, at the most general possible level, the interesting behaviors of complex systems. Two such behaviors are the emergence of simple or stable high-level behavior from relatively complex low-level behavior, and the emergence of sophisticated high-level behavior from relatively simple low-level behavior; they are often found nested in the same system. Concerning the emergence of simplicity, this essay examines Herbert Simon's explanation from near-decomposability and a stochastic explanation that generalizes the approach of statistical physics. A more general notion of an abstract difference-making structure is introduced with examples, and a discussion of evolvability follows. Concerning the emergence of sophistication, this essay focuses on, first, the energetics approach associated with dissipative structures and the fourth law of thermodynamics, and second, the notion of a complex adaptive system.

Robert Merton observed that better-known scientists tend to get more credit than less well-known scientists for the same achievements; he called this the Matthew effect. Scientists themselves, even those eminent researchers who enjoy its benefits, regard the effect as a pathology: it results, they believe, in a misallocation of credit. If so, why do scientists continue to bestow credit in the manner described by the effect? This paper advocates an explanation of the effect on which it turns out to allocate credit fairly after all, while at the same time making sense of scientists' opinions to the contrary.

This paper examines the standard Bayesian solution to the Quine–Duhem problem, the problem of distributing blame between a theory and its auxiliary hypotheses in the aftermath of a failed prediction. The standard solution, I argue, begs the question against those who claim that the problem has no solution. I then provide an alternative Bayesian solution that is not question-begging and that turns out to have some interesting and desirable properties not possessed by the standard solution. This solution opens the way to a satisfying treatment of a problem concerning ad hoc auxiliary hypotheses.

Sciences of complex systems thrive on compositional theories – toolkits that allow the construction of models of a wide range of systems, each consisting of various parts put together in different ways. To be tractable, a compositional theory must make shrewd choices about the parts and properties that constitute its basic ontology. One such choice is to decompose a system into spatiotemporally discrete parts. Compositional theories in the high-level sciences follow this rule of thumb to a certain extent, but they also make essential use of what I call distributed ontologies: divisions of the system into entities or states of affairs each depending on sets of fundamental-level facts that to a large extent overlap. The point is developed using the example of statistical theories in which the probabilities are ontologically distributed.

Elliott Sober argues that the statistical slogan “Absence of evidence is not evidence of absence” cannot be taken literally: it must be interpreted charitably as claiming that the absence of evidence is (typically) not very much evidence of absence. I offer an alternative interpretation, on which the slogan claims that absence of evidence is (typically) not objective evidence of absence. I sketch a definition of objective evidence, founded in the notion of an epistemically objective likelihood, and I show that in Sober’s paradigm case, the slogan can, on this understanding, be sustained.

Why are causal generalizations in the higher-level sciences “inexact”? That is, why do they have apparent exceptions? This paper offers one explanation: many causal generalizations cite as their antecedent—the \(F\) in \(Fs\,\, {\textit{are}}\,\, G\) —a property that is not causally relevant to the consequent, but which is rather “entangled” with a causally relevant property. Entanglement is a relation that may exist for many reasons, and that allows of exceptions. Causal generalizations that specify entangled but causally irrelevant antecedents therefore tolerate exceptions

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.