LEAP-HI: Safety and Learning from Errors and Near Misses in the Human-Automation Interaction of Socio-Technical Infrastructure Systems

现代社会技术基础设施系统在动态环境中运行，其中人类操作员与执行安全关键任务的日益自主的技术之间存在高度互动。这个领先的美国繁荣，健康和基础设施工程（LEAP-HI）项目将研究这些半自动化社会技术基础设施系统中安全，工作量和经济因素之间的平衡，通过整合系统工程，人因工程，决策和组织理论，经济生产理论。这一科学研究贡献支持NSF的使命，以促进科学的进步和促进我们的国家福利。预期效益包括工作安排的改善、与工作有关的健康成果以及社会技术基础设施系统的经济可行性。该项目还将支持外联活动，以激发学生进入STEM相关领域的社会技术领域的兴趣。该项目使用系统级理论方法，为现代社会技术基础设施系统提供可计算的安全操作区域。它开发了一种新的心理工作负荷指标，包括由多种生理测量和主观人类感知确定的分布式态势感知。它通过研究认知偏见如何影响对自动化的信任以及将任务委托给自动化技术的决定来探索决策理论的新领域。它还研究了识别和归因的原因错误，未遂事件和事件的一部分，在这些现代系统的组织学习。此外，该研究建立在经济生产理论的基础上，在确定基础设施的安全范围时，将工作量、安全和经济观点结合起来。最后，该研究评估的动态权衡工作量，边际安全性和经济性的安全信封内，以改善基础设施的决策。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

Modern socio-technical infrastructure systems operate in dynamic environments where there is a high level of interaction between human operators and increasingly autonomous technologies that carry out safety-critical tasks. This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) project will research the balance between safety, workload, and economic considerations in these semi-automated socio-technical infrastructure systems to provide improved design and policy tools to both infrastructure providers and regulators by integrating the disciplines of systems engineering, human factors engineering, decision and organizational theories, and economic production theory. This scientific research contribution supports NSF's mission to promote the progress of science and to advance our national welfare. The expected benefits include improvements in the organization of work, health outcomes associated with the work, and the economic viability of socio-technical infrastructure systems. The project will also support outreach activities to stimulate interest in the socio-technical domain area for students going into STEM-related fields. This project uses a systems-level theoretical approach that provides a computable safe area of operation for modern socio-technical infrastructure systems. It develops a new metric of mental workload that includes distributed situational awareness as determined by multiple physiological measurements and subjective human perceptions. It explores new regions in decision theory by studying how cognitive biases influence trust in automation and decisions to delegate tasks to automated technologies. It also examines the identification and attribution of causes of errors, near misses, and incidents as part of organizational learning in these modern systems. Additionally, the research builds on economic production theory to integrate workload, safety, and economic perspectives in determining an infrastructure’s safety envelope. Finally, the research assesses the dynamic trade-offs among workload, marginal safety, and economics within the safety envelope to improve infrastructure decision-making.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.