Daniel Lassiter – författare

This book explores graded expressions of modality, a rich and underexplored source of insight into modal semantics. Studies on modal language to date have largely focussed on a small and non-representative subset of expressions, namely modal auxiliaries such as must, might, and ought. Here, Daniel Lassiter argues that we should expand the conversation to include gradable modals such as more likely than, quite possible, andvery good. He provides an introduction to qualitative and degree semantics for graded meaning, using the Representational Theory of Measurement to expose the complementarity between these apparently opposed perspectives on gradation. The volume explores and expands the typology of scales among English adjectives and uses the result to shedlight on the meanings of a variety of epistemic and deontic modals. It also demonstrates that modality is deeply intertwined with probability and expected value, connecting modal semantics with the cognitive science of uncertainty and choice.

This book explores graded expressions of modality, a rich and underexplored source of insight into modal semantics. Studies on modal language to date have largely focussed on a small and non-representative subset of expressions, namely modal auxiliaries such as must, might, and ought. Here, Daniel Lassiter argues that we should expand the conversation to include gradable modals such as more likely than, quite possible, and very good. He provides an introduction to qualitative and degree semantics for graded meaning, using the Representational Theory of Measurement to expose the complementarity between these apparently opposed perspectives on gradation. The volume explores and expands the typology of scales among English adjectives and uses the result to shed light on the meanings of a variety of epistemic and deontic modals. It also demonstrates that modality is deeply intertwined with probability and expected value, connecting modal semantics with the cognitive science of uncertainty and choice.

This book explores graded expressions of modality, a rich and underexplored source of insight into modal semantics. Studies on modal language to date have largely focussed on a small and non-representative subset of expressions, namely modal auxiliaries such as must, might, and ought. Here, Daniel Lassiter argues that we should expand the conversation to include gradable modals such as more likely than, quite possible, and very good. He provides an introduction to qualitative and degree semantics for graded meaning, using the Representational Theory of Measurement to expose the complementarity between these apparently opposed perspectives on gradation. The volume explores and expands the typology of scales among English adjectives and uses the result to shed light on the meanings of a variety of epistemic and deontic modals. It also demonstrates that modality is deeply intertwined with probability and expected value, connecting modal semantics with the cognitive science of uncertainty and choice.

The European Summer School in Logic, Language and Information (ESSLLI) is organized every year by the Association for Logic, Language and Information (FoLLI) in different sites around Europe. The main focus of ESSLLI is on the interface between linguistics, logic and computation. ESSLLI offers foundational, introductory and advanced courses, as well as workshops, covering a wide variety of topics within the three areas of interest: Language and Computation, Language and Logic, and Logic and Computation. During two weeks, around 50 courses and 10 workshops are offered to the attendants, each of 1.5 hours per day during a five days week, with up to seven parallel sessions. ESSLLI also includes a student session (papers and posters by students only, 1.5 hour per day during the two weeks) and four evening lectures by senior scientists in the covered areas.The 15 revised full papers presented were carefully reviewed and selected. The papers are organized in topical sections on The papers are organized in topical sections on language and computation; logic and computation; and logic and language.