This article investigates several consequences of a recent trend in philosophy of mind to shift the relata of realization from mental state–physical state to function-mechanism. It is shown, by applying both frameworks to the neuroscientific case study of memory consolidation, that, although this shift can be used to avoid the immediate antireductionist consequences of the traditional argument from multiple realizability, what is gained is a far more modest form of reductionism than recent philosophical accounts have intimated and neuroscientists themselves have claimed.

When conceived as an empirical claim, it is natural to wonder how one might test the hypothesis of multiple realization. I consider general issues of testability, show how they apply specifically to the hypothesis of multiple realization, and propose an auxiliary assumption that, I argue, must be conjoined to the hypothesis of multiple realization to ensure its testability. I argue further that Bechtel and Mundale (1999) go astray because they fail to appreciate the need for this auxiliary assumption.

Multiple realization historically mandated the autonomy of psychology, and its principled irreducibility to neuroscience. Recently, multiple realization and its implications for the reducibility of psychology to neuroscience have been challenged. One challenge concerns the proper understanding of reduction. Another concerns whether multiple realization is as pervasive as is alleged. I focus on the latter question. I illustrate multiple realization with actual, rather than hypothetical, cases of multiple realization from within the biological sciences. Though they do support a degree of autonomy for higher levels of explanation and organization, they do not have the dire consequences critics of multiple realization fear.

Despite some recent advances, multiple realization remains a largely misunderstood thesis. Consider the dispute between Lawrence Shapiro and Carl Gillett over the application of Shapiro's recipe for deciding when we have genuine cases of multiple realization. I argue that Gillett follows many philosophers in mistakenly supposing that multiple realization is absolute and transitive. Both of these are problematic. They are tempting only when we extract the question of multiple realization from the explanatory context in which it is invoked. Anchoring multiple realizability in its theoretical context provides grounds for arbitrating disagreements. Doing so, I argue, favors the view advanced by Shapiro.

The neutral and nearly neutral theories of molecular evolution are sometimes characterized as theories about drift alone, where drift is described solely as an outcome, rather than a process. We argue, however, that both selection and drift, as causal processes, are integral parts of both theories. However, the nearly neutral theory explicitly recognizes alleles and/or molecular substitutions that, while engaging in weakly selected causal processes, exhibit outcomes thought to be characteristic of random drift. A narrow focus on outcomes obscures the significant role of weakly selected causal processes in the nearly neutral theory.

Ecological fitness has been suggested to provide a unifying definition of fitness. However, a metric for this notion of fitness was in most cases unavailable except by proxy with differential reproductive success. In this article, I show how differential persistence of lineages can be used as a way to assess ecological fitness. This view is inspired by a better understanding of the evolution of some clonal plants, colonial organisms, and ecosystems. Differential persistence shows the limitation of an ensemblist noncausal understanding of evolution. Causal explanations are necessary to understand the evolution by natural selection of these biological systems.

Models that fail to satisfy the Markov condition are unstable because changes in state variable values may cause changes in the values of background variables, and these changes in background lead to predictive error. Such error arises because non-Markovian models fail to track the causal relations generating the values of response variables. This has implications for discussions of the level of selection: under certain plausible conditoins most standard models of group selection will not satisfy the Markov condition when fit to data from real populations. These models neither correctly represent the causal structure generating nor correctly explain the phenomena of interest.

A twofold taxonomy for emergence is presented into which a variety of contemporary accounts of emergence fit. The first taxonomy consists of inferential, conceptual, and ontological emergence; the second of diachronic and synchronic emergence. The adequacy of weak emergence, a computational form of inferential emergence, is then examined and its relationship to conceptual emergence and ontological emergence is detailed.

I challenge the usual approach of defining emergence in terms of properties of wholes “emerging” upon properties of parts. This approach indeed fails to meet the requirement of nontriviality, since it renders a bunch of ordinary properties emergent; however, by defining emergence as the incompressibility of a simulation process, we have an objective meaning of emergence because the difference between the processes satisfying the incompressibility criterion and the other processes does not depend on our cognitive abilities. Finally, this definition fulfills the nontriviality and the scientific-adequacy requirements better than the combinatorial approach, emergence here being a predicate of processes rather than of properties.

Rather than taking the ontological fundamentality of an ideal microphysics as a starting point, this article sketches an approach to the problem of levels that swaps assumptions about ontology for assumptions about inquiry. These assumptions can be implemented formally via computational modeling techniques that will be described below. It is argued that these models offer a way to save some of our prominent commonsense intuitions concerning levels. This strategy offers a way of exploring the individuation of higher level properties in a systematic and formally constrained manner.

Until recently, economics was generally understood to be a nonexperimental science with a hypothetico-deductive methodology. This article considers how the methodology of economics has changed with the spread of experimental methods. Initially, most experimental economists saw their work as testing pre-existing theories. However, a method of systematic inductive enquiry in which theory plays a less central role is now evolving. This method is structured around the discovery and progressive refinement of regularities. “Exhibits”—experimental designs that generate significant regularities—are taking over some of the functions formerly performed by theoretical models.

The growing acceptance and success of experimental economics has increased the interest of researchers in tackling philosophical and methodological challenges to which their work increasingly gives rise. I sketch some general issues that call for the combined expertise of experimental economists and philosophers of science, of experiment, and of inductive-statistical inference and modeling.

This article explores some issues having to do with the use of experimental results from one-shot games to reach conclusions about the existence of social preferences that are taken to figure in the explanation of cooperation in repeated interactions in real life.

The Ultimatum Game is one of the most successful experimental designs in the history of the social sciences. In this article I try to explain this success—what makes it a “paradigmatic experiment”—stressing in particular its versatility. Despite the intentions of its inventors, the Ultimatum Game was never a good design to test economic theory, and it is now mostly used as a heuristic tool for the observation of nonstandard preferences or as a “social thermometer” for the observation of culture-specific norms.

In two recent papers Barry Loewer (2001, 2004) has suggested to interpret probabilities in statistical mechanics as chances in David Lewis's (1994) sense. I first give a precise formulation of this proposal, then raise two fundamental objections, and finally conclude that these can be overcome only at the price of interpreting these probabilities epistemically.

The Boltzmann and Gibbs approaches to statistical mechanics have very different definitions of equilibrium and entropy. The problems associated with this are discussed, and it is suggested that they can be resolved, to produce a version of statistical mechanics incorporating both approaches, by redefining equilibrium not as a binary property (being/not being in equilibrium) but as a continuous property (degrees of equilibrium) measured by the Boltzmann entropy and by introducing the idea of thermodynamic-like behavior for the Boltzmann entropy. The Kac ring model is used as an example to test the proposals.

In this article I consider the challenges for exporting causal knowledge raised by complex biological systems. In particular, James Woodward's interventionist approach to causality identified three types of stability in causal explanation: invariance, modularity, and insensitivity. I consider an example of robust degeneracy in genetic regulatory networks and knockout experimental practice to pose methodological and conceptual questions for our understanding of causal explanation in biology.

Leading philosophical accounts presume that Thomas H. Morgan's transmission theory can be understood independently of experimental practices. Experimentation is taken to be relevant to confirming, rather than interpreting, the transmission theory. But the construction of Morgan's theory went hand in hand with the reconstruction of the chief experimental object, the model organism Drosophila melanogaster. This raises an important question: when a theory is constructed to account for phenomena in carefully controlled laboratory settings, what knowledge, if any, indicates the theory's relevance to phenomena outside highly controlled settings? The answer, I argue, is found within the procedural knowledge embedded within laboratory practice.