Lingzhi Shi

We use the normality framework of Goodman and Salow (2018, 2021, 2023b) to investigate of dynamics of rational belief. The guiding idea is that people are entitled to believe that their circumstances aren’t especially abnormal. More precisely, a rational agent’s beliefs rule out all and only those possibilities that are either (i) ruled out by their evidence or (ii) sufficiently less normal than some other possibility not ruled out by their evidence. Working within this framework, we argue that the logic of rational belief revision is much weaker than is usually supposed. We do so by isolating a natural family of orthodox principles about belief revision, describing realistic cases in which these principles seem to fail, and showing how these counterexamples are predicted by independently motivated models of the cases in question. In these models, whether one evidential possibility counts as sufficiently less normal than another is determined by underlying probabilities (together with a contextually determined question). We argue that the resulting probabilistic account of belief compares favorably with other such accounts, including Lockeanism (Foley, 1993), a ‘stability’ account inspired by Leitgeb (2017), the ‘tracking theory’ of Lin and Kelly (2012), and the influential precursor of Levi (1967). We show that all of these accounts yield subtly different but similarly heterodox logics of belief revision.

In a series of recent papers, Timothy Williamson has argued for the surprising conclusion that there are cases in which you know a proposition in spite of its being overwhelmingly improbable given what you know that you know it. His argument relies on certain formal models of our imprecise knowledge of the values of perceptible and measurable magnitudes. This paper suggests an alternative class of models that do not predict this sort of improbable knowing. I show that such models are motivated by independently plausible principles in the epistemology of perception, the epistemology of estimation, and concerning the connection between knowledge and justified belief.

A core tenet of Bayesian epistemology is that Bayesian conditionalization is the rule of rational credal revision. But it has been pointed out in the recent literature that if learning can be nontransparent, then Bayesian conditionalization does not universally maximize expected accuracy. This result raises an explanatory challenge for any externalist Bayesian who does not want to give up on a connection between accuracy and epistemic rationality: Why is Bayesian conditionalization the rule of rational credal revision, despite the fact that it does not universally maximize expected accuracy? This article proposes an answer to this challenge. The article argues that an updating rule can be evaluated, not only on the basis of its expected accuracy in particular learning situations, but also on the basis of its total expected accuracy across different learning situations. The article proposes a notion of global evidential constancy, and shows that, out of all globally evidentially constant rules, Bayesian conditionalization has the highest total expected accuracy across all learning situations, including nontransparent ones. Bayes is back!

We explore the view that Frege's puzzle is a source of straightforward counterexamples to Leibniz's law. Taking this seriously requires us to revise the classical logic of quantifiers and identity; we work out the options, in the context of higher-order logic. The logics we arrive at provide the resources for a straightforward semantics of attitude reports that is consistent with the Millian thesis that the meaning of a name is just the thing it stands for. We provide models to show that some of these logics are non-degenerate.

The externalist says that your evidence could fail to tell you what evidence you do or not do have. In that case, it could be rational for you to be uncertain about what your evidence is. This is a kind of uncertainty which orthodox Bayesian epistemology has difficulty modeling. For, if externalism is correct, then the orthodox Bayesian learning norms of conditionalization and reflection are inconsistent with each other. I recommend that an externalist Bayesian reject conditionalization. In its stead, I provide a new theory of rational learning for the externalist. I defend this theory by arguing that its advice will be followed by anyone whose learning dispositions maximize expected accuracy. I then explore some of this theory’s consequences for the rationality of epistemic akrasia, peer disagreement, undercutting defeat, and uncertain evidence.

The strongest version of the dogmatism puzzle argues that, when we know something, we should resolve to ignore or avoid evidence against it. The best existing responses are fallibilist, and hold that decisions should be governed by underlying probabilities rather than our knowledge. I argue that this is an overreaction: by paying close attention to the principles governing belief-revision, and to subtly different ways in which knowledge can govern decision-making, we can dissolve the puzzle without the need for controversial theoretical commitments. The resulting solution demonstrates fruitful and underexplored points of interaction between ‘traditional’ epistemology and ‘formal’ theories of belief-revision, and clears the ground for more systematic theorizing about how and when we should be open to changing our minds.

When, if ever, do we wrong ourselves? The Self-Other Symmetric answer is: when we do to ourselves what would wrong a consenting other. The standard objection, which has gone unchallenged for decades, is that Symmetry seems to imply that we wrong ourselves in too many cases—where rights are unwaivable, or “self-consent” is lacking. We argue that Symmetry not only survives these would-be counterexamples; it explains and unifies them. The key to Symmetry is not, as critics have supposed, the bizarre claim that we must literally give ourselves consent if we are to avoid wronging ourselves. Instead, it is that we authorize ourselves simply by making decisions, just as we can authorize others by making decisions jointly.

We pose and resolve several vexing decision theoretic puzzles. Some are variants of existing puzzles, such as 'Trumped' (Arntzenius and McCarthy 1997), 'Rouble trouble' (Arntzenius and Barrett 1999), 'The airtight Dutch book' (McGee 1999), and 'The two envelopes puzzle' (Broome 1995). Others are new. A unified resolution of the puzzles shows that Dutch book arguments have no force in infinite cases. It thereby provides evidence that reasonable utility functions may be unbounded and that reasonable credence functions need not be countably additive. The resolution also shows that when infinitely many decisions are involved, the difference between making the decisions simultaneously and making them sequentially can be the difference between riches and ruin. Finally, the resolution reveals a new way in which the ability to make binding commitments can save perfectly rational agents from sure losses.

Radically permissive ontologies like mereological universalism and material plenitude are typically motivated by concerns about arbitrariness or anthropocentrism: it would be objectionably arbitrary, the thought goes, to countenance only those objects that we ordinarily take there to be. Despite the prevalence of this idea, it isn't at all clear what it is for a theory to be “objectionably arbitrary,” or what follows from a commitment to avoiding arbitrariness in metaphysics. This paper aims to clarify both questions, and examines whether arguments from arbitrariness really are the proper foundations for one or both varieties of ontological permissivism. I argue that these considerations (even when made more precise) are far less successful at motivating radical forms of permissivism than we often take them to be. To do better, permissivists must either supply a much more developed metaphysics of material objects, or a controversial (but tempting) conception of what we're doing when we do metaphysics.