Files in this item

FilesDescriptionFormat

application/pdf

application/pdfLEE-DISSERTATION-2019.pdf (5MB)
(no description provided)PDF

Description

Title:Probabilistic semantics for vagueness
Author(s):Lee, Steven Fong-Yi
Director of Research:Lasersohn, Peter N.
Doctoral Committee Chair(s):Lasersohn, Peter N.
Doctoral Committee Member(s):McCarthy, Timothy G.; Livengood, Jonathan M.; Levinstein, Benjamin A
Department / Program:Philosophy
Discipline:Philosophy
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):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
Abstract: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.
Issue Date:2019-12-06
Type:Text
URI:http://hdl.handle.net/2142/106242
Rights Information:Copyright 2019 Steven Fong-yi Lee
Date Available in IDEALS:2020-03-02
Date Deposited:2019-12


This item appears in the following Collection(s)

Item Statistics