论文标题
人类团队中的集体智慧：贝叶斯的心理理论方法
Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach
论文作者
论文摘要
我们开发了一个贝叶斯特工网络，该网络从观察到的交流中共同对队友的心理状态进行了建模。使用生成的计算方法来认知，我们做出了两种贡献。首先，我们表明我们的经纪人可以产生干预措施，以改善人类团队的集体智慧，超出人类的实现。其次，我们制定了人类思维能力理论和关于人类认知理论的实时度量。我们使用从在线实验中收集的数据，在线实验中，其中29个仅由五个人组成的人的145个人通过基于聊天的系统进行交流以解决认知任务。我们发现，人类（a）努力将队友的信息完全整合到他们的决策中，尤其是在交流负荷很高的情况下，并且（b）具有认知偏见，使他们体重不足某些有用但模棱两可的信息。我们的心理能力理论衡量标准可以预测个人和团队水平的表现。观察团队的前25％的消息解释了最终团队绩效变化的8％，与当前的最新状态相比，提高了170％。
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.
