  Languages are powerful solutions to coordination problems: they provide
stable, shared expectations about how the words we say correspond to the
beliefs and intentions in our heads. Yet language use in a variable and
non-stationary social environment requires linguistic representations to be
flexible: old words acquire new ad hoc or partner-specific meanings on the fly.
In this paper, we introduce a hierarchical Bayesian theory of convention
formation that aims to reconcile the long-standing tension between these two
basic observations. More specifically, we argue that the central computational
problem of communication is not simply transmission, as in classical
formulations, but learning and adaptation over multiple timescales. Under our
account, rapid learning within dyadic interactions allows for coordination on
partner-specific common ground, while social conventions are stable priors that
have been abstracted away from interactions with multiple partners. We present
new empirical data alongside simulations showing how our model provides a
cognitive foundation for explaining several phenomena that have posed a
challenge for previous accounts: (1) the convergence to more efficient
referring expressions across repeated interaction with the same partner, (2)
the gradual transfer of partner-specific common ground to novel partners, and
(3) the influence of communicative context on which conventions eventually
form.
