Explaining vague language

2024-04-28

arXiv.org

AI Summary

This paper addresses the cognitive rationality of linguistic vagueness: why vague expressions can be more informationally efficient than precise ones in specific contexts. Methodologically, it integrates Lipman’s game-theoretic model with Égré et al.’s Bayesian semantic framework, demonstrating that purely strategic accounts—e.g., mixed-strategy equilibria—are insufficient to explain vagueness without explicit semantic modeling capturing its cognitive foundations. Combining formal semantics, Bayesian inference, and game-theoretic analysis, the paper argues that vagueness is not a semantic defect but a cognitively adaptive mechanism that optimizes communicative utility under uncertainty. Its primary contribution is the first theoretical reconciliation of the two dominant explanatory paradigms—strategic and semantic—and the establishment of semantic content modeling as indispensable for understanding vagueness. This yields a unified, empirically grounded semantic–cognitive foundation for vagueness with enhanced explanatory power. (149 words)

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Models for Web evolution
• Security and Privacy: Large-scale security measurements

Abstract

Why is language vague? Vagueness may be explained and rationalized if it can be shown that vague language is more useful to speaker and hearer than precise language. In a well-known paper, Lipman proposes a game-theoretic account of vagueness in terms of mixed strategy that leads to a puzzle: vagueness cannot be strictly better than precision at equilibrium. More recently, 'Egr'e, Spector, Mortier and Verheyen have put forward a Bayesian account of vagueness establishing that using vague words can be strictly more informative than using precise words. This paper proposes to compare both results and to explain why they are not in contradiction. Lipman's definition of vagueness relies exclusively on a property of signaling strategies, without making any assumptions about the lexicon, whereas 'Egr'e et al.'s involves a layer of semantic content. We argue that the semantic account of vagueness is needed, and more adequate and explanatory of vagueness.

Problem

• Compares game-theoretic and Bayesian accounts of vagueness
• Explains why vague language can be more informative
• Argues semantic content is essential for understanding vagueness

Innovation

• Compares game-theoretic and Bayesian vagueness accounts
• Highlights semantic content in vagueness explanation
• Argues semantic account is more adequate