The traditional possible-worlds model of belief describes agents as 'logically omniscient' in the sense that they believe all logical consequences of what they believe, including all logical truths. This is widely considered a problem if we want to reason about the epistemic lives of non-ideal agents who-much like ordinary human beings-are logically competent, but not logically omniscient. A popular strategy for avoiding logical omniscience centers around the use of impossible worlds: worlds that, in one way or another, violate the laws of logic. In this paper, we argue that existing impossible-worlds models of belief fail to describe agents who are both logically non-omniscient and logically competent. To model such agents, we argue, we need to 'dynamize' the impossible-worlds framework in a way that allows us to capture not only what agents believe, but also what they are able to infer from what they believe. In light of this diagnosis, we go on to develop the formal details of a dynamic impossible-worlds framework, and show that it successfully models agents who are both logically non-omniscient and logically competent.

When one has both epistemic and practical reasons for or against some belief, how do these reasons combine into an all-things-considered reason for or against that belief? The question might seem to presuppose the existence of practical reasons for belief. But we can rid the question of this presupposition. Once we do, a highly general ‘Combinatorial Problem’ emerges. The problem has been thought to be intractable due to certain differences in the combinatorial properties of epistemic and practical reasons. Here we bring good news: if we accept an independently motivated version of epistemic instrumentalism—the view that epistemic reasons are a species of instrumental reasons—we can reduce The Combinatorial Problem to the relatively benign problem of how to weigh different instrumental reasons against each other. As an added benefit, the instrumentalist account can explain the apparent intractability of The Combinatorial Problem in terms of a common tendency to think and talk about epistemic reasons in an elliptical manner.

Orthodox Bayesianism is a highly idealized theory of how we ought to live our epistemic lives. One of the most widely discussed idealizations is that of logical omniscience: the assumption that an agent's degrees of belief must be probabilistically coherent to be rational. It is widely agreed that this assumption is problematic if we want to reason about bounded rationality, logical learning, or other aspects of non-ideal epistemic agency. Yet, we still lack a satisfying way to avoid logical omniscience within a Bayesian framework. Some proposals merely replace logical omniscience with a different logical idealization; others sacrifice all traits of logical competence on the altar of logical non-omniscience. We think a better strategy is available: by enriching the Bayesian framework with tools that allow us to capture what agents can and cannot infer given their limited cognitive resources, we can avoid logical omniscience while retaining the idea that rational degrees of belief are in an important way constrained by the laws of probability. In this paper, we offer a formal implementation of this strategy, show how the resulting framework solves the problem of logical omniscience, and compare it to orthodox Bayesianism as we know it.

Normally, when evidence speaks for or against believing some proposition, it does so by virtue of speaking for or against the truth of that proposition. If, for example, I look out the window and see that the sky is darkening, the evidence I have thereby acquired speaks in favor of believing that it will rain by virtue of indicating that it will, in fact, rain....

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