A quantum algorithm succeeds not because the superposition principle allows ‘the computation of all values of a function at once’ via ‘quantum parallelism’, but rather because the structure of a quantum state space allows new sorts of correlations associated with entanglement, with new possibilities for information-processing transformations between correlations, that are not possible in a classical state space. I illustrate this with an elementary example of a problem for which a quantum algorithm is more efficient than any classical algorithm. I also introduce the notion of ‘pseudotelepathic’ games and show how the difference between classical and quantum correlations plays a similar role here for games that can be won by quantum players exploiting entanglement, but not by classical players whose only allowed common resource consists of shared strings of random numbers (common causes of the players’ correlated responses in a game).

In a recent paper, Malament (2004) employs a time reversal transformation that differs from the standard one, without explicitly arguing for it. This is a new and important understanding of time reversal that deserves arguing for in its own right. I argue that it improves upon the standard one. Recent discussion has focused on whether velocities should undergo a time reversal operation. I address a prior question: What is the proper notion of time reversal? This is important, for it will affect our conclusion as to whether our best theories are time-reversal symmetric, and hence whether our spacetime is temporally oriented.

In Frisch 2004 and 2005 I showed that the standard ways of modeling particle-field interactions in classical electrodynamics, which exclude the interactions of a particle with its own field, results in a formal inconsistency, and I argued that attempts to include the self-field lead to numerous conceptual problems. In this paper I respond to criticism of my account in Belot 2007 and Muller 2007. I concede that this inconsistency in itself is less telling than I suggested earlier but argue that existing solutions to the theory's foundational problems do not support the kind of traditional philosophical conception of scientific theorizing defended by Muller and Belot.

The article defends the doctrine that Linnaean taxa, including species, have essences that are, at least partly, underlying intrinsic, mostly genetic, properties. The consensus among philosophers of biology is that such essentialism is deeply wrong, indeed incompatible with Darwinism. I argue that biological generalizations about the morphology, physiology, and behavior of species require structural explanations that must advert to these essential properties. The objection that, according to current “species concepts,” species are relational is rejected. These concepts are primarily concerned with what it is for a kind to be a species and throw little light on the essentialist issue of what it is for an organism to be a member of a particular kind. Finally, the article argues that this essentialism can accommodate features of Darwinism associated with variation and change.

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.

Theorizing in ecology and evolution often proceeds via the construction of multiple idealized models. To determine whether a theoretical result actually depends on core features of the models and is not an artifact of simplifying assumptions, theorists have developed the technique of robustness analysis, the examination of multiple models looking for common predictions. A striking example of robustness analysis in ecology is the discovery of the Volterra Principle, which describes the effect of general biocides in predator-prey systems. This paper details the discovery of the Volterra Principle and the demonstration of its robustness. It considers the classical ecology literature on robustness and introduces two individual-based models of predation, which are used to further analyze the Volterra Principle. The paper also introduces a distinction between parameter robustness, structural robustness, and representational robustness, and demonstrates that the Volterra Principle exhibits all three kinds of robustness.

What sort of claims do scientific models make and how do these claims then underwrite empirical successes such as explanations and reliable policy interventions? In this paper I propose answers to these questions for the class of models used throughout the social and biological sciences, namely idealized deductive ones with a causal interpretation. I argue that the two main existing accounts misrepresent how these models are actually used, and propose a new account.

In biology, the ‘ecological orientation’ rests on a commitment to examining systems, and the conceptual challenge of defining that system now employs techniques and concepts adapted from diverse disciplines (i.e., systems philosophy, cybernetics, information theory, computer science) that are applied to biological simulations and model building. Immunology has joined these efforts, and the question posed here is whether the discipline will remain committed to its theoretical concerns framed by the notions of protecting an insular self, an entity demarcated from its environment, or will shift its focus of interest to a wider context. An ecological perspective emphasizes the interchange between the organism and its environment, the processing of information, and the regulation arising from responses to this larger context. Moving from the first attempts at modeling the immune system as a closed network, immunologists have joined the general interest in systems analysis, and that move might portend a significant shift to an open, more holistic consideration of immune regulation.

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.