We provide a taxonomy of the two most important debates in the philosophy of the cognitive and neural sciences. The first debate is over methodological individualism: is the object of the cognitive and neural sciences the brain, the whole animal, or the animal—environment system? The second is over explanatory style: should explanation in cognitive and neural science be reductionist-mechanistic, interlevel mechanistic, or dynamical? After setting out the debates, we discuss the ways in which they are interconnected. Finally, we make some recommendations that we hope will help philosophers interested in the cognitive and neural sciences to avoid dead ends.

According to a standard view of the second law of thermodynamics, our belief in the second law can be justified by pointing out that low-entropy macrostates are less probable than high-entropy macrostates, and then noting that a system in an improbable state will tend to evolve toward a more probable state. I would like to argue that this justification of the second law is unhelpful at best and wrong at worst, and will argue that certain puzzles sometimes associated with the second law are merely artifacts of this questionable justification.

The epistemic state of complete ignorance is not a probability distribution. In it, we assign the same, unique, ignorance degree of belief to any contingent outcome and each of its contingent, disjunctive parts. That this is the appropriate way to represent complete ignorance is established by two instruments, each individually strong enough to identify this state. They are the principle of indifference (PI) and the notion that ignorance is invariant under certain redescriptions of the outcome space, here developed into the ‘principle of invariance of ignorance’ (PII). Both instruments are so innocuous as almost to be platitudes. Yet the literature in probabilistic epistemology has misdiagnosed them as paradoxical or defective since they generate inconsistencies when conjoined with the assumption that an epistemic state must be a probability distribution. To underscore the need to drop this assumption, I express PII in its most defensible form as relating symmetric descriptions and show that paradoxes still arise if we assume the ignorance state to be a probability distribution.

After reviewing several failed arguments that laws cannot change, I use the laws’ special relation to counterfactuals to show how temporary laws would have to differ from eternal but time-dependent laws. Then I argue that temporary laws are impossible and that neither Lewis's nor Armstrong's analyses of law nicely accounts for the laws’ immutability.

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.

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.