  A key property of linguistic conventions is that they hold over an entire
community of speakers, allowing us to communicate efficiently even with people
we have never met before. At the same time, much of our language use is
partner-specific: we know that words may be understood differently by different
people based on our shared history. This poses a challenge for accounts of
convention formation. Exactly how do agents make the inferential leap to
community-wide expectations while maintaining partner-specific knowledge? We
propose a hierarchical Bayesian model to explain how speakers and listeners
solve this inductive problem. To evaluate our model's predictions, we conducted
an experiment where participants played an extended natural-language
communication game with different partners in a small community. We examine
several measures of generalization and find key signatures of both
partner-specificity and community convergence that distinguish our model from
alternatives. These results suggest that partner-specificity is not only
compatible with the formation of community-wide conventions, but may facilitate
it when coupled with a powerful inductive mechanism.
