University of Illinois Urbana-Champaign

Probabilistic semantics for vagueness

Lee, Steven Fong-Yi

Content Files
LEE-DISSERTATION-2019.pdf

Permalink

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

Description

Title
Probabilistic semantics for vagueness
Author(s)
Lee, Steven Fong-Yi
Issue Date
2019-12-06
Director of Research (if dissertation) or Advisor (if thesis)
Lasersohn, Peter N.
Doctoral Committee Chair(s)
Lasersohn, Peter N.
Committee Member(s)
  • McCarthy, Timothy G.
  • Livengood, Jonathan M.
  • Levinstein, Benjamin A
Department of Study
Philosophy
Discipline
Philosophy
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2020-03-02T21:58:22Z
Keyword(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.
Graduation Semester
2019-12
Type of Resource
text
Permalink
http://hdl.handle.net/2142/106242
Copyright and License Information
Copyright 2019 Steven Fong-yi Lee

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