Vagueness Is Rational under Uncertainty

Frazee, Joey; Beaver, David

doi:10.1007/978-3-642-14287-1_16

Cited by 63 publications

(15 citation statements)

References 19 publications

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“…Although Lassiter (2011) and Frazee and Beaver (2010) describe their positions from different perspectives, they amount to similar approaches. Lassiter's article, though marginally later was developed independently and is fuller in detail.…”

Section: Lassiter's and Frazee And Beaver's Epistemic Modelsmentioning

confidence: 96%

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Towards a Probabilistic Semantics for Vague Adjectives

Sutton

2015

Bayesian Natural Language Semantics and Pragmatics

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A way of modelling the meanings of vague terms directly in terms of the uncertainties they give rise to is explored. These uncertainties are modelled with a probabilistic, Bayesian version of situation semantics on which meaning is captured as the correlations between uses of words and the types of situations those words are used to refer to. It is argued that doing so provides a framework from which vagueness arises naturally. It is also claimed that such a framework is in a position to better capture the boundarylessness of vague concepts.

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Section: Lassiter's and Frazee And Beaver's Epistemic Modelsmentioning

confidence: 96%

“…One way to try to assuage this tension is to introduce uncertainty into semantics. For example, one could maintain a threshold for truth, but model vagueness as uncertainty about where the threshold, in some context, lies (Lassiter 2011;Frazee and Beaver 2010). However, arguably, uncertainty over thresholds nonetheless carves boundaries.…”

Section: Introductionmentioning

confidence: 94%

Towards a Probabilistic Semantics for Vague Adjectives

Sutton

2015

Bayesian Natural Language Semantics and Pragmatics

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“…17 Our discussion follows Edgington's seminal work most closely. For other related work connecting probability with philosophical and linguistic questions involving vagueness, see Borel (1907) (with translation and commentary in Égré and Barberousse (2014)), Black (1937), Kyburg and Schubert (1993), Kyburg (2000), Lawry (2008), Frazee and Beaver (2010), Lassiter (2011Lassiter ( ), Égré (2011, Sutton (2013); Égré to appear. A detailed comparative analysis of these accounts would take us too far afield here; see however Égré and Barberousse (2014); Égré to appear for some discussion along these lines.…”

Section: The Soritesmentioning

confidence: 97%

Adjectival vagueness in a Bayesian model of interpretation

2015

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We derive a probabilistic account of the vagueness and context-sensitivity of scalar adjectives from a Bayesian approach to communication and interpretation. We describe an iterated-reasoning architecture for pragmatic interpretation and illustrate it with a simple scalar implicature example. We then show how to enrich the apparatus to handle pragmatic reasoning about the values of free variables, explore its predictions about the interpretation of scalar adjectives, and show how this model implements Edgington's (Analysis 2:193-204,1992, Keefe and Smith (eds.) Vagueness: a reader, 1997) account of the sorites paradox, with variations. The Bayesian approach has a number of explanatory virtues: in particular, it does not require any special-purpose machinery for handling vagueness, and it is integrated with a promising new approach to pragmatics and other areas of cognitive science. Edgington (1992Edgington ( , 1997 proposes an attractive unified approach to the Sorites, Lottery, and Preface paradoxes. According to Edgington, these puzzles are all explained by a generalization of classical logic which has the formal structure of the probability calculus, with an accompanying generalized notion of valid reasoning. She gives a number of strong arguments to the effect that a degree-based theory of vagueness with the formal structure of probabilities is preferable to one with the structure of classical fuzzy logic. However, she explicitly disavows the idea that the degrees involved in B Daniel Lassiter

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“…Here are two reasons why the latter strategy makes sense. For one, probability can be needed for explanatory purposes, such as, e.g., in modeling vague language use (Frazee and Beaver 2010;Franke 2012;Lassiter and Goodman 2014;. We will see examples in Sections 5 and 6.…”

Section: Probabilisticmentioning

confidence: 99%

Probabilistic pragmatics, or why Bayes’ rule is probably important for pragmatics

Franke

Jäger

2016

Zeitschrift Für Sprachwissenschaft

124

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Probabilistic pragmatics aspires to explain certain regularities of language use and interpretation as behavior of speakers and listeners who want to satisfy their conversational interests in a context that may contain a substantial amount of uncertainty. This approach differs substantially from more familiar approaches in theoretical pragmatics. To set it apart, we here work out some of its key distinguishing features and show, by way of some simple examples, how probabilistic pragmatics instantiates these.

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Vagueness Is Rational under Uncertainty

Cited by 63 publications

References 19 publications

Towards a Probabilistic Semantics for Vague Adjectives

Towards a Probabilistic Semantics for Vague Adjectives

Adjectival vagueness in a Bayesian model of interpretation

Probabilistic pragmatics, or why Bayes’ rule is probably important for pragmatics

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