Modeling Communication to Coordinate Perspectives in Cooperation
  Communication is highly overloaded. Despite this, even young children are
good at leveraging context to understand ambiguous signals. We propose a
computational account of overloaded signaling from a shared agency perspective
which we call the Imagined We for Communication. Under this framework,
communication helps cooperators coordinate their perspectives, allowing them to
act together to achieve shared goals. We assume agents are rational
cooperators, which puts constraints on how signals can be sent and interpreted.
We implement this model in a set of simulations demonstrating this model's
success under increasing ambiguity as well as increasing layers of reasoning.
Our model is capable of improving performance with deeper recursive reasoning;
however, it outperforms comparison baselines at even the shallowest level,
highlighting how shared knowledge and cooperative logic can do much of the
heavy-lifting in language.
