A mathematical theory of cooperative communication
Pei Wang, Junqi Wang, Pushpi Paranamana, Patrick Shafto
Oral presentation: Orals & Spotlights Track 35: Neuroscience/Probabilistic
on 2020-12-10T18:30:00-08:00 - 2020-12-10T18:45:00-08:00
on 2020-12-10T18:30:00-08:00 - 2020-12-10T18:45:00-08:00
Poster Session 7 (more posters)
on 2020-12-10T21:00:00-08:00 - 2020-12-10T23:00:00-08:00
GatherTown: Probabilistic Methods and Inference ( Town A0 - Spot A0 )
on 2020-12-10T21:00:00-08:00 - 2020-12-10T23:00:00-08:00
GatherTown: Probabilistic Methods and Inference ( Town A0 - Spot A0 )
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Toggle Abstract Paper (in Proceedings / .pdf)
Abstract: 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.