  Emergentism and pragmatics are two research fields that study the dynamics of
linguistic communication along substantially different timescales and
intelligence levels. From the perspective of multi-agent reinforcement
learning, they correspond to stochastic games with reinforcement training and
stage games with opponent awareness. Given that their combination has been
explored in linguistics, we propose computational models that combine
short-term mutual reasoning-based pragmatics with long-term language
emergentism. We explore this for agent communication referential games as well
as in Starcraft II, assessing the relative merits of different kinds of mutual
reasoning pragmatics models both empirically and theoretically. Our results
shed light on their importance for making inroads towards getting more natural,
accurate, robust, fine-grained, and succinct utterances.
