Recent developments in Bayesian experimental pragmatics have received much attention. The Rational Speech Act (RSA) model formalizes core concepts of traditional pragmatic theories quantitatively and makes predictions that fit empirical data nicely. In this paper, we analyze the RSA model and its relation to closely related game theoretic approaches, by spelling out its belief, goal and action components. We introduce some alternatives motivated from the game theoretic tradition and compare models incorporating these alternatives systematically to the original RSA model, using Bayesian model comparison, in terms of their ability to predict relevant empirical data. The result suggests that the RSA model could be adapted and extended to improve its predictive power, in particular by taking speaker preferences into account.

This paper addresses two issues that arise in a degree-based approach to the semantics of positive forms of gradable adjectives such as tall in the sentence John is tall (e.g., Kennedy & McNally 2005;Kennedy 2007): First, how the standard of comparison is contextually determined; Second, why gradable adjectives exhibit the relative-absolute distinction. Combining ideas of previous evolutionary and probabilistic approaches (e.g., Potts 2008; Franke 2012; Lassiter 2011; Lassiter & Goodman 2013), we propose a new model that makes exact and empirically testable probabilistic predictions about speakers' use of gradable adjectives and that derives the relative-absolute distinction from considerations of optimal language use. Along the way, we distinguish between vagueness and loose use, and argue that, within our approach, vagueness can be understood as the result of uncertainty about the exact degree distribution within the comparison class.

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This paper focuses on English directional modified numerals up to n, which triggers opposite inference patterns in speaker-uncertainty and authoritativepermission contexts. I propose that these opposite inference patterns are due to pragmatic inference about an unspecified semantic lower bound of up to n, based on its similarities to gradable adjectives and vague characteristics. The value of the semantic lower bound in different contexts is predicted by a general pragmatic principle of interaction between informativity and applicability independently motivated in previous probabilistic models on gradable adjectives.