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:我们开发了一个贝叶斯人代理网络, 集体模拟观测到的通信中队友的精神状态。 我们用一种基因化计算方法来识别认知, 我们做出两种贡献。 首先, 我们证明我们的代理人可以产生一些干预措施, 来改善人类- AI团队的集体智能, 超越人类独能实现的目标。 其次, 我们开发了人类心智能力理论的实时度量, 以及关于人类认知的实验理论。 我们使用从一个在线实验中收集的数据, 在网上实验中, 29个人类五人团队中的145人通过聊天系统进行沟通, 解决认知任务。 我们发现, 人类(a) 努力将队友的信息充分融入他们的决定, 特别是在通信负荷高的时候, (b) 有认知偏差, 导致他们体重不足某些有用但含混不清的信息。 我们的心智能力测量理论预测了个人和团队一级的业绩。 观察小组头25%的信息解释了最后团队表现的8%, 与当前艺术状况相比, 改进了170 % 。