  Cooperative communication plays a central role in theories of human
cognition, language, development, culture, and human-robot interaction. Prior
models of cooperative communication are algorithmic in nature and do not shed
light on why cooperation may yield effective belief transmission and what
limitations may arise due to differences between beliefs of agents. Through a
connection to the theory of optimal transport, we establishing a mathematical
framework for cooperative communication. We derive prior models as special
cases, statistical interpretations of belief transfer plans, and proofs of
robustness and instability. Computational simulations support and elaborate our
theoretical results, and demonstrate fit to human behavior. The results show
that cooperative communication provably enables effective, robust belief
transmission which is required to explain feats of human learning and improve
human-machine interaction.
