I will discuss a variety of cases such that the subject's believing truly is somewhat of an accident, but less so than in a Gettier case. In each case, this is because her reasons are not ultimately undefeated full stop, but they are ultimately undefeated with certain qualifications. For example, the subject's reasons might be ultimately defeated considered in themselves but ultimately undefeated considered as a proper part of an inference to the best explanation that is undefeated without qualification. In each section of the paper, I consider different qualifications and show how in each case we get an epistemic standing I call “coach-class knowledge”. First-class knowledge requires justifying reasons that are undefeated without qualification. Coach-class knowledge only requires qualified lack of defeat. I will use this distinction to bring debates over knowledge from falsehood and fake barns to an ecumenical resolution. In both cases, the subject enjoys coach-class rather than first-class knowledge. I will also show that the defeasible reasoning tradition can better account for graded accidental truth than safety theories.

Recent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks, and social media. Analytics in terms of defeasible reasoning – for example, for decision making – could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.

Our lives are permeated by various forms of legality, produced by multiple bodies - both statist and non-statist. The pervasive presence of non-statist, soft law schemes in the contemporary society poses a challenge for legal theory: how to conceptualize legal-like structures that evolve outside the boundaries of the state and are able nonetheless to exert significant normative power? Understanding this phenomenon requires, I argue, a new model of law that will not be bounded by the binary (either/or) structure of traditional jurisprudence and sociology of law. I respond to this challenge by developing a degree-theoretic model of legal-normativity which I term "fuzzy law". This model offers a new conceptual vocabulary for thinking about soft law as a social phenomenon. The model draws on three main theoretical sources: the theory of complementary pairs, fuzzy-set theory, and defeasible reasoning. I examine the jurisprudential and sociological implications of the fuzzy law model through a discussion of the dialectics of reasoning with fuzzy rules and an exploration of the coordination dynamics of quasi-legal systems.

Defeasible logic is a rule-based nonmonotonic logic, with both strict and defeasible rules, and a priority relation on rules. We show that inference in the propositional form of the logic can be performed in linear time. This contrasts markedly with most other propositional nonmonotonic logics, in which inference is intractable.