Recent epistemology has seen a striking rise in interest in the notion of normality, including in the analysis of justified belief, defeasible reasoning, and knowledge. In the analysis of knowledge in particular, normality has been used to support modal analyses of knowledge, according to which knowledge is safely true belief. In this paper, I sound a note of caution regarding this proposal. As I will argue, the counterexamples that originally seemed to threaten the safety analysis of knowledge in its more (...) traditional formulations have natural counterparts that continue to threaten the newer, normality-based formulations. Moreover, these reformulated counterexamples seem to exploit structural features of the notion of normality itself, rather than one or another particular conception of normality. (shrink)

According to the JUSTIFIED FAIR COINS principle, if I know that a coin is fair, and I lack justification for believing that it won’t be flipped, then I lack justification for believing that it won’t land tails. What this principle says, in effect, is that the only way to have justification for believing that a fair coin won’t land tails, is by having justification for believing that it won’t be flipped at all. Although this seems a plausible and innocuous principle, (...) in a recent paper Dorr, Goodman and Hawthorne use it in devising an intriguing puzzle which places all justified beliefs about the future in jeopardy. They point out, further, that one very widespread theory of justification predicts that JUSTIFIED FAIR COINS is false, giving us additional reason to reject it. In this paper, I will attempt to turn this dialectic around. I will argue that JUSTIFIED FAIR COINS does not inevitably lead to scepticism about the future, and the fact that it is incompatible with a widespread theory of justifi... (shrink)

We offer a general framework for theorizing about the structure of knowledge and belief in terms of the comparative normality of situations compatible with one’s evidence. The guiding idea is that, if a possibility is sufficiently less normal than one’s actual situation, then one can know that that possibility does not obtain. This explains how people can have inductive knowledge that goes beyond what is strictly entailed by their evidence. We motivate the framework by showing how it illuminates knowledge about (...) the future, knowledge of lawful regularities, knowledge about parameters measured using imperfect instruments, the connection between knowledge, belief, and probability, and the dynamics of knowledge and belief in response to new evidence. (shrink)

Philosophy and Phenomenological Research, Volume 104, Issue 1, Page 19-49, January 2022.

Surprises are important in our everyday lives as well as in our scientific and philosophical theorizing—in psychology, information theory, cognitive-neuroscience, philosophy of science, and confirmation theory. Nevertheless, there is no satisfactory theory of what makes something surprising. It has long been acknowledged that not everything unexpected is surprising. The reader had no reason to expect that there will be exactly 190 words in this abstract and yet there is nothing surprising about this fact. We offer a novel theory that explains (...) when and why an unexpected fact is surprising. We distinguish between descriptive and normative notions of what is surprising; clarify the sense in which surprising facts are unexpected; and, finally, develop and defend the significance account of surprise, according to which a fact is surprising to an agent if and to the extent that it is both unexpected and significant to the agent. Since a surprising fact can be significant to an agent in various ways—personal, moral, epistemic, and aesthetic—surprise is not merely or primarily epistemic. Fitting surprise reflects more than a person’s view of what is; it reflects a person’s view of what is significant. (shrink)

The article develops and justifies, on the basis of the epistemological argumentation theory, two central pieces of the theory of evaluative argumentation interpretation: 1. criteria for recognizing argument types and 2. rules for adding reasons to create ideal arguments. Ad 1: The criteria for identifying argument types are a selection of essential elements from the definitions of the respective argument types. Ad 2: After presenting the general principles for adding reasons (benevolence, authenticity, immanence, optimization), heuristics are proposed for finding missing (...) reasons, for deductive arguments, e.g., semantic tableaux are suggested. (shrink)