Cooperative communication plays a central role in theories of human cognition, language, development, and culture, and is increasingly relevant in human-algorithm and robot interaction. Existing models are algorithmic in nature and do not shed light on the statistical problem solved in cooperation or on constraints imposed by violations of common ground. We present a mathematical theory of cooperative communication that unifies three broad classes of algorithmic models as approximations of Optimal Transport (OT). We derive a statistical interpretation for the problem approximated by existing models in terms of entropy minimization, or likelihood maximizing, plans. We show that some models are provably robust to violations of common ground, even supporting online, approximate recovery from discovered violations, and derive conditions under which other models are provably not robust. We do so using gradient-based methods which introduce novel algorithmic-level perspectives on cooperative communication. Our mathematical approach complements and extends empirical research, providing strong theoretical tools derivation of a priori constraints on models and implications for cooperative communication in theory and practice.
Speakers: Pei Wang, Junqi Wang, Pushpi Paranamana, Patrick Shafto