Comparativism is the view that comparative confidences (e.g., being more confident that P than that Q) are more fundamental than degrees of belief (e.g., believing that P with some strength x). I outline the basis for a new, nonprobabilistic version of comparativism inspired by a suggestion made by Frank Ramsey in “Probability and Partial Belief.” I show how, and to what extent, ‘Ramseyan comparativism’ might be used to weaken the (unrealistically strong) probabilistic coherence conditions that comparativism traditionally relies on.

We examine contemporary game-theoretic accounts of ownership as a convention. New results from dynamic networks complicate matters, suggesting that if ownership is conventional, it should not be as prevalent as it seems to be. In fact, such models reveal a tendency toward antiownership norms. The value of resources may be crucial: low stakes lead to conventional ownership, but ownership norms rarely evolve; high stakes lead to a predominance of ownership at the cost of its conventionality. We argue that conventional ownership norms can originate in nonconventional ways and discuss some philosophical implications.

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.

All natural languages exhibit a distinction between content words (nouns, verbs, etc.) and function words (determiners, auxiliaries, tenses, etc.). Yet surprisingly little has been said about the emergence of this universal architectural feature of human language. This article argues that the existence of this distinction requires the presence of nontrivial compositionality and identifies assumptions that have previously been made in the literature that provably guarantee only trivial composition. It then presents a signaling game with variable contexts and shows how the distinction can emerge via reinforcement learning.

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.

We present here a hierarchical model for the evolution of compositional language. The model has the structure of a two-sender/one-receiver Lewis signaling game augmented with executive agents who may learn to influence the behavior of the basic senders and receiver. The model shows how functional agents might coevolve representational roles even as they evolve a reliable compositional language in the context of costly signaling. When successful, the evolved language captures both the compositional structure of properties in the world and the compositional structure of successful actions involving those properties.

The understanding that comes with scientific explanation is regarded as one of the central epistemic aims of science. In earlier work I have argued that scientists achieve understanding of phenomena by basing their explanations on intelligible theories, where intelligibility is a contextually determined value. In this article, I address the question of how the aim of understanding relates to other epistemic aims of science, such as prediction of empirical evidence and accurate description of phenomena. Moreover, I examine the associated values and analyze their role and interaction through a historical case study.

In this article, I first outline the view developed in my recent book on the role of idealization in scientific understanding. I discuss how this view leads to the recognition of a number of kinds of variability among scientific representations, including variability introduced by the many different aims of scientific projects. I then argue that the role of idealization in securing understanding distances understanding from truth but that this understanding nonetheless gives rise to scientific knowledge. This discussion will clarify how my view relates to three other recent books on understanding by Henk de Regt, Catherine Elgin, and Kareem Khalifa.

Several argue that truth cannot be science’s sole epistemic goal, for it would fail to do justice to several scientific practices that advance understanding. I challenge these arguments but only after making a small concession: science’s sole epistemic goal is not truth as such; rather, its goal is finding true answers to relevant questions. Using examples from the natural and social sciences, I then show that scientific understanding’s epistemically valuable features are either true answers to relevant questions or a means thereof.