 In recent years, a few promising extensions of this pragmatic reasoning scheme have been proposed. One is to include probabilistic choice functions to model agents’ approximately rational choices, so as to allow for a much more direct link with experimental data (for overview see Franke &amp; Jäger 2016, Goodman &amp; Frank 2016). Such probabilistic pragmatic models have been applied to a number of phenomena of interest, including reasoning about referential expressions in context (Frank &amp; Goodman 2012), ignorance implicatures (Goodman &amp; Stuhlmüller 2013), context effects on scalar implicatures (Degen et al. 2015), non-literal interpretation of number terms (Kao et al. 2014), pragmatic meaning modulation associated with particular intonation (Bergen &amp; Goodman 2015, Stevens 2016), and Quantity implicatures in complex sentences (Bergen et al. 2016, Potts et al. 2016). These models of pragmatic reasoning have furthermore been used successfully also in natural language processing applications (Andreas &amp; Klein 2016, Monroe et al. 2017) and other areas of linguistic research, such as sociolinguistics (Burnett 2019). Another recent approach is to allow for uncertainty of the semantic meanings of the involved expressions, and for uncertainty of the beliefs and preferences of the participants of thee conversation and of other aspects of the ‘common ground’ (e.g., Brochhagen 2017). Finally, in recent work more pragmatic phenomena are accounted for than just generalised conversational implicatures, such as particular conversational implicature, explicatures or free enrichment (e.g., Parikh 2010), the analysis of presupposition (Qing et al. 2016), and politeness (e.g. Clark 2012, Yoon et al. 2020).