The “Cosmological Constant Problem” (CCP) is widely considered a crisis in contemporary theoretical physics. Unfortunately, the search for its resolution is hampered by open disagreement about what is, strictly, the problem. This disagreement stems from the observation that the CCP is not a problem within any of our current theories, and nearly all of the details of those future theories for which the CCP could be made a problem are up for grabs. Given this state of affairs, I discuss how one ought to make sense of the role of the CCP in physics and generalize some lessons from it.

The article investigates the relations between Hausdorff and non-Hausdorff manifolds as objects of general relativity. We show that every non-Hausdorff manifold can be seen as a result of gluing together some Hausdorff manifolds. In the light of this result, we investigate a modal interpretation of a non-Hausdorff differential manifold, according to which it represents a bundle of alternative space-times, all of which are compatible with a given initial data set.

Many theories of actual causation implicitly endorse the claim that if c is an actual cause of e, then either c causes e directly or every intermediary by which c indirectly causes e is itself both an actual cause of e and also an actual effect of c. We think this compositionality constraint is plausible. However, as we show, it is not always satisfied by the causal attributions ordinary people make. We conclude by considering what philosophers working on causation should do when the deliverances of their theories diverge from what ordinary people say.

Binding specificity is a centrally important concept in molecular biology, yet it has received little philosophical attention. Here I aim to remedy this by analyzing binding specificity as a causal property. I focus on the concept’s role in drug design, where it is highly prized and hence directly studied. From a causal perspective, understanding why binding specificity is a valuable property of drugs contributes to an understanding of causal selection—of how and why scientists distinguish between causes, not just causes from noncauses. In particular, the specificity of drugs is precisely what underwrites their value as experimental interventions on biological processes.

I evaluate the plausibility of explanatory elitism, the view that a good scientific explanation of an outcome will show that it was highly probable. I consider an argument from Michael Strevens that elitism is the only view that can account for the historical acceptance of probabilistic theories in physics. I argue that biology provides better test cases for evaluating elitism and conclude that theories in that domain were favored in virtue of conferring correct, and not necessarily high, probabilities on outcomes.

I argue that the human mind includes an innate domain-specific system for representing precise small numerical quantities. This theory contrasts with object-tracking theories and with domain-general theories that only make use of mental models. I argue that there is a good amount of evidence for innate representations of small numerical quantities and that such a domain-specific system has explanatory advantages when infants’ poor working memory is taken into account. I also show that the mental models approach requires previously unnoticed domain-specific structure and consequently that there is no domain-general alternative to an innate domain-specific small number system.

Children acquire complex concepts like dog earlier than simple concepts like brown, even though our best neuroscientific theories suggest that learning the former is harder than learning the latter and, thus, should take more time (Markus Werning). This is the complex-first paradox. We present a novel solution to the complex-first paradox. Our solution builds on a generalization of Fei Xu and Joshua B. Tenenbaum’s Bayesian model of word learning. By focusing on a rational theory of concept learning, we show that it is easier to infer the meaning of complex concepts than that of simple concepts.

As stochastic independence is essential to the mathematical development of probability theory, it seems that any foundational work on probability should be able to account for this property. Bayesian decision theory appears to be wanting in this respect. Savage’s postulates on preferences under uncertainty entail a subjective expected utility representation, and this asserts only the existence and uniqueness of a subjective probability measure, regardless of its properties. What is missing is a preference condition corresponding to stochastic independence. To fill this significant gap, the article axiomatizes Bayesian decision theory afresh and proves several representation theorems in this novel framework.

According to the Requirement of Total Evidence, when assessing the credibility of hypotheses, we should endeavor to take into account all of the relevant evidence at our disposal instead of just some proper part of that evidence. In “The Fine-Tuning Argument and the Requirement of Total Evidence,” Peter Fisher Epstein offers two alleged counterexamples to this requirement. I show that, on at least one very natural interpretation of the requirement, his alleged counterexamples are not genuine. I close by explaining why this conclusion is important.

The requirement of total evidence is a mainstay of Bayesian epistemology. Peter Fisher Epstein argues that the requirement generates mistaken conclusions about several examples that he devises. Here we examine the example of Epstein’s that we find most interesting and argue that Epstein’s analysis of it is flawed.