Probabilistic semantics for vagueness

In this dissertation I argue that truth-conditional semantics for vague predicates, combined with a Bayesian account of statistical inference incorporating knowledge of truth-conditions of utterances, generates false predictions regarding negations and metalinguistic inference. I thus propose a fundamentally probabilistic semantics for vagueness on which the meaning of a vague predicate is a likelihood function on the states it encodes, with these likelihoods being generated via reinforcement learning in a signaling game.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Philosophy

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Lee, Steven Fong-Yi

Contributors dc:contributor

• Lasersohn, Peter N.
• McCarthy, Timothy G.
• Livengood, Jonathan M.
• Levinstein, Benjamin A

• semantics
• pragmatics
• vagueness
• probability
• game theory
• artificial intelligence
• reinforcement learning
• Bayes
• Bayesian
• linguistics
• philosophy
• statistical inference
• cognitive science
• Grice
• formal semantics
• Montague
• Sorites
• logic
• dynamic semantics
• truth-conditions

Statement dc:rights

• Copyright 2019 Steven Fong-yi Lee

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/106242

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/106242