Successful communication regularly requires listeners to makepragmatic inferences — enrichments beyond the literal mean-ing of a speaker’s utterance. For example, when interpretinga sentence such as “Alice ate some of the cookies,” listenersroutinely infer that Alice did not eat all of them. A Griceanaccount of this phenomenon assumes the presence of alterna-tives (like “all of the cookies”) with varying degrees of infor-mativity, but it remains an open question precisely what thesealternatives are. To address this question, we collect empiricalmeasurements of speaker and listener judgments about vary-ing sets of alternatives across a range of scales and use these asinputs to a computational model of pragmatic inference. Thisapproach allows us to test hypotheses about how well differ-ent sets of alternatives predict pragmatic judgments by peo-ple. Our findings suggest that comprehenders likely considera broader set of alternatives beyond those logically entailed bythe initial message.

The combinatorial expressivity of natural language enables speakers to communicate a single idea in myriad ways. Howdo speakers decide which utterance to use? Under the Uniform Information Density (UID) hypothesis, speakers shouldplan their utterances to minimize listener comprehension difficulty by spreading out new information, for example, byusing complementizers or avoiding contractions before high-surprisal content. We explore how UID behaviors may resultfrom pragmatic considerations (e.g., social reasoning in context) using a computational pragmatics model. We showthat artificial pragmatic agents communicating under noise conditions exhibit key UID effects: (A) speakers provide cuesbefore high surprisal content, (B) given a UID-cue, listeners infer oncoming content is high-surprisal, (C) synthetic corporagenerated from speakers reflects a signature UID effect: a positive relationship between likelihood of optional elementsand surprisal of oncoming content. Thus, UID may follow from more general principles of pragmatic communication inthe presence of noise.

Despite their diversity, languages around the world share aconsistent set of properties and distributional regularities. Forexample, the distribution of word frequencies, the distributionof syntactic dependency lengths, and the presence of ambigu-ity are all remarkably consistent across languages. We dis-cuss a framework for studying how these system-level proper-ties emerge from local, in-the-moment interactions of rational,pragmatic speakers and listeners. To do so, we derive a novelobjective function for measuring the communicative efficiencyof linguistic systems in terms of the interactions of speakersand listeners. We examine the behavior of this objective ina series of simulations focusing on the communicative func-tion of ambiguity in language. These simulations suggest thatrational pragmatic agents will produce communicatively effi-cient systems and that interactions between such agents pro-vide a framework for examining efficient properties of lan-guage structure and use more broadly.