Daniel J. Singer

The is-ought gap is Hume’s claim that we can’t get an ‘ought’ from just ‘is’s. Prior (“The Autonomy of Ethics,” 1960) showed that its most straightforward formulation, a staple of introductory philosophy classes, fails. Many authors attempt to resurrect the claim by restricting its domain syntactically or by reformulating it in terms of models of deontic logic. Those attempts prove to be complex, incomplete, or incorrect. I provide a simple reformulation of the is-ought gap that closely fits Hume’s description of it. My formulation of the gap avoids the proposed counterexamples from Prior and offers a natural explanation of why they seem compelling. Moreover, I show that my formulation of the gap is guaranteed by standard theories of the semantics of normative terms, and that provides a more general reason to accept it.

The traditional solutions to the Sleeping Beauty problem say that Beauty should have either a sharp 1/3 or sharp 1/2 credence that the coin flip was heads when she wakes. But Beauty’s evidence is incomplete so that it doesn’t warrant a precise credence, I claim. Instead, Beauty ought to have a properly imprecise credence when she wakes. In particular, her representor ought to assign \(R(H\!eads)=[0,1/2]\) . I show, perhaps surprisingly, that this solution can account for the many of the intuitions that motivate the traditional solutions. I also offer a new objection to Elga’s restricted version of the principle of indifference, which an opponent may try to use to collapse the imprecision

Understanding the dynamics of information is crucial to many areas of research, both inside and outside of philosophy. Using computer simulations of three kinds of information, germs, genes, and memes, we show that the mechanism of information transfer often swamps network structure in terms of its effects on both the dynamics and the fitness of the information. This insight has both obvious and subtle implications for a number of questions in philosophy, including questions about the nature of information, whether there is genetic information, and how to arrange scientific communities.

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes