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.

翻译：我们构建了一个贝叶斯智能体网络，该网络能从观察到的通信中集体建模队友的心理状态。采用认知生成计算框架，我们做出两项贡献：首先，我们证明该智能体能够生成干预措施，使人类-人工智能团队的集体智能超越纯人类团队所能达到的水平；其次，我们开发了人类心智理论能力的实时测量方法，并检验了关于人类认知的相关理论。我们通过在线实验收集数据，实验中有29个五人纯人类团队（共145人）通过聊天系统协作完成认知任务。研究发现：（a）人类在决策时难以完全整合队友信息，尤其在通信负荷较高时表现尤为明显；（b）存在认知偏差，导致他们低估某些有用但模糊的信息。我们提出的心智理论能力测量方法能预测个体与团队层面的表现。仅观察团队前25%的消息即可解释最终团队绩效约8%的变异，这相较于当前最优方法提升了170%。