Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach


  • Samuel Westby Northeastern University
  • Christoph Riedl Northeastern University



HAI: Human-Machine Teams, CMS: Bayesian Learning, CMS: Simulating Humans, HAI: Learning Human Values and Preferences


We develop a network of Bayesian agents that collectively model the mental states of teammates from the observed communication. Using a generative computational approach to cognition, we make two contributions. First, we show that our agent could generate interventions that improve the collective intelligence of a human-AI team beyond what humans alone would achieve. Second, we develop a real-time measure of human's theory of mind ability and test theories about human cognition. We use data collected from an online experiment in which 145 individuals in 29 human-only teams of five communicate through a chat-based system to solve a cognitive task. We find that humans (a) struggle to fully integrate information from teammates into their decisions, especially when communication load is high, and (b) have cognitive biases which lead them to underweight certain useful, but ambiguous, information. Our theory of mind ability measure predicts both individual- and team-level performance. Observing teams' first 25% of messages explains about 8% of the variation in final team performance, a 170% improvement compared to the current state of the art.




How to Cite

Westby, S., & Riedl, C. (2023). Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 6119-6127.



AAAI Technical Track on Humans and AI