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Languages are powerful solutions to coordination problems: they providestable, shared expectations about how the words we say correspond to thebeliefs and intentions in our heads. Yet language use in a variable andnon-stationary social environment requires linguistic representations to beflexible: old words acquire new ad hoc or partner-specific meanings on the fly.In this paper, we introduce CHAI (Continual Hierarchical Adaptation throughInference), a hierarchical Bayesian theory of coordination and conventionformation that aims to reconcile the long-standing tension between these twobasic observations. We argue that the central computational problem ofcommunication is not simply transmission, as in classical formulations, butcontinual learning and adaptation over multiple timescales. Partner-specificcommon ground quickly emerges from social inferences within dyadicinteractions, while community-wide social conventions are stable priors thathave been abstracted away from interactions with multiple partners. We presentnew empirical data alongside simulations showing how our model provides acomputational foundation for several phenomena that have posed a challenge forprevious accounts: (1) the convergence to more efficient referring expressionsacross repeated interaction with the same partner, (2) the gradual transfer ofpartner-specific common ground to strangers, and (3) the influence ofcommunicative context on which conventions eventually form.

From partners to populations: A hierarchical Bayesian account of coordination and convention