There is not a unique inductive skeptical position; there are grades of inductive skepticism. There is nothing much to say about complete skepticism, but some more restricted skeptical positions may be profitably analyzed.

The use of the Law of Likelihood (LL) as a general tool for assessing rival hypotheses has been criticized for its ambiguous treatment of background information. The LL endorses radically different answers depending on what information is designated as background versus evidence. I argue that once one distinguishes between two questions about evidentiary support, the ambiguity vanishes. I demonstrate this resolution by applying it to a debate over the status of the ‘fine-tuning argument’.

When evaluating the success of causal theories of explanation the focus has typically been on the legitimacy of causal relations and on putative examples of explanations that we cannot capture in causal terms. Here I motivate the existence of a third kind of problem: the difficulty of accounting for explanatory disputes. Moreover, I argue that this problem remains even if the first two are settled and that it threatens to undercut one of the central motivations for causal accounts of explanation, namely, the causal account of the directionality of scientific explanation.

This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems.

Selective scientific realists disagree on which theoretical posits should be regarded as essential to the empirical success of a scientific theory. A satisfactory account of essentialness will show that the (approximate) truth of the selected posits adequately explains the success of the theory. Therefore, (a) the essential elements must be discernible prospectively; (b) there cannot be a priori criteria regarding which type of posit is essential; and (c) the overall success of a theory, or ‘cluster’ of propositions, not only individual derivations, should be explicable. Given these desiderata, I propose a “unification criterion” for identifying essential elements.

In game-theoretic signaling models, evolution tends to favor perfectly precise signaling systems, but in the natural world communication is almost always imprecise. I argue that standard explanations for this discrepancy are only partially sufficient, and I show that communication is often ambiguous because signal senders take advantage of context sensitivity. As evidence, I make two additions to the signaling model: a cost for more complex signaling strategies and the ability to combine information in signals with independent information. Analysis and simulation of the altered model shows that it leads to the predicted outcome of evolution favoring ambiguous signaling.

I uncover here a conflict in Kant’s natural philosophy. His matter theory and laws of mechanics are in tension. Kant’s laws are fit for particles but are too narrow to handle continuous bodies, which his doctrine of matter demands. To fix this defect, Kant ultimately must ground the Torque Law; that is, the impressed torque equals the change in angular momentum. But that grounding requires a premise—the symmetry of the stress tensor—that Kant denies himself. I argue that his problem would not arise if he had kept his early theory of matter as made of mass points, or “physical monads.”

Current theories of the division of cognitive labor are confined to the “context of justification,” assuming exogenous theories. But new theories are made from the same labor that is used for developing existing theories, and if none of this labor is ever allocated to create new alternatives, then scientific progress is impossible. A unified model is proposed in which theories are no longer given but a function of the division of labor in the model itself. The interactions of individuals balancing the exploitation of existing theories and the exploration of new theories results in a robust cyclical pattern.