  Cooperation information sharing is important to theories of human learning
and has potential implications for machine learning. Prior work derived
conditions for achieving optimal Cooperative Inference given strong, relatively
restrictive assumptions. We relax these assumptions by demonstrating
convergence for any discrete joint distribution, robustness through equivalence
classes and stability under perturbation, and effectiveness by deriving bounds
from structural properties of the original joint distribution. We provide
geometric interpretations, connections to and implications for optimal
transport, and connections to importance sampling, and conclude by outlining
open questions and challenges to realizing the promise of Cooperative
Inference.
