EAGER: SAI: Collaborative Research: Conceptualizing Interorganizational Processes for Supporting Interdependent Lifeline Infrastructure Recovery

加强美国基础设施（SAI）是美国国家科学基金会的一个项目，旨在促进以人为本的基础研究和潜在的变革性研究，以加强美国的基础设施。有效的基础设施为社会经济活力和广泛的生活质量改善提供了坚实的基础。强大、可靠和有效的基础设施刺激私营部门的创新，发展经济，创造就业机会，使公共部门提供的服务更有效率，加强社区，促进机会平等，保护自然环境，加强国家安全，并推动美国的领导地位。为了实现这些目标，需要来自科学和工程学科的专业知识。SAI侧重于人类推理和决策、治理以及社会和文化过程的知识如何使有效基础设施的建设和维护成为可能，从而改善生活和社会，并以技术和工程的进步为基础。美国的生命线基础设施，如饮用水、电力系统和地面交通，包括使美国人生存和工作的技术、组织和专业知识。它们是高度相互依赖的，需要彼此的正常运作。然而，太平洋西北地区的基础设施对卡斯卡迪亚俯冲带（CSZ）地震（9.0级以上）准备不足，未来45年左右发生地震的概率为7-15%。它可能同时摧毁或破坏许多基础设施，使生活在几个月或几年的困难中，因为这些生命线在整个地区慢慢恢复。本项目考察了操作人员和管理人员，特别是维护饮用水、电网和地面交通基础设施运行的专业人员，如何准备好应对这种罕见的极端危险。本研究的目的是通过改善地震后相互依赖的基础设施的协调准备和恢复，并告知如何设计、开发和维护这些相互依赖的基础设施，以增强其复原力和可持续性，从而改变美国为下一次CSZ地震做准备的方式。在协调回收这些各种生命线基础设施的过程中避免浪费，每天可以挽救数千人的生命，并为美国经济带来数百万美元的收入。研究小组正在进行桌面应急响应演习、详细访谈和小组讨论，并与控制室操作员、维护主管、实时支持人员以及饮用水、电网和地面交通基础设施的管理人员进行讨论。这些活动有助于确定关键的共享或相互关联的控制变量（例如，电压、储层释放速率、管理结构），如果不加以认识和规划，这些变量可能严重妨碍协调采油。这些数据用于开发基于主体的模型（ABM），这是一种计算机模型，将人类行为数据纳入对各种情况作出反应的“主体”的设计中，以确定各种地震和基础设施响应场景的结果，而不是在面试/讨论/练习工作中使用的结果。华盛顿州和俄勒冈州是最容易受到CSZ地震影响的两个州，在整个项目过程中，将咨询和告知其基础设施规划和应急响应的关键信息提供者，以最大限度地发挥其价值，并通过政策简报、白皮书和演示文稿将结果更广泛地分发给多个利益相关者，以提高其对从业者的价值。该项目旨在为进一步研究和开发一种新的决策援助工具提供科学依据。该项目提出的社会科学（例如，组织运营分析，信任，价值观）和工程理论和方法（例如，弹性工程，ABM）的整合在很大程度上未经测试，但有可能提供关于组织间过程如何支持或阻碍相互依赖的基础设施的协调恢复的第一个见解。尽管CSZ地震事件具有独特性，但鉴于在其他灾害事件中确实会同时发生多个基础设施的故障，预计通过该项目产生的许多新知识将推广到其他灾害和地区。匿名定性数据和开源仿真软件将向其他研究人员和从业人员公开提供。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.American lifeline infrastructures, such as drinkable water, power systems, and ground transportation, include technology, organizations, and expertise that keep Americans alive and working. They are highly interdependent and require each other’s functioning for normal operation. Yet Pacific Northwest infrastructures are under-prepared for the Cascadia subduction zone (CSZ) earthquake (magnitude 9.0+), with a 7-15% probability over the next 45 years or so. It likely will destroy or damage many infrastructures at the same time, making life difficult for months or years as these lifelines are slowly restored across the region. This project examines how operators and managers—specifically, the expert staff who keep the drinkable water, power grid, and ground transportation infrastructures going—are prepared to handle such rare extreme hazards. The aim of this research is to transform how the U.S. prepares for the next CSZ earthquake, by improving coordinated preparedness and recovery of interdependent infrastructures after the earthquake, and informing how these interdependent infrastructures are designed, developed, and maintained to enhance their resilience and sustainability. Avoiding waste in coordinated recovery of these various lifeline infrastructures could save thousands of lives as well as millions of dollars to the U.S. economy every day. The research team is conducting table-top emergency response exercises, detailed interviews, and group discussions and with control room operators, maintenance supervisors, real-time support staff, and managers across potable water, the power grid, and ground transportation infrastructures. These activities help to identify critical shared or interconnected control variables (e.g., electrical voltages, reservoir release rates, management structures) that may severely hamper coordinated recovery if not acknowledged and planned for. These data are used to develop an agent-based model (ABM), a computer model that incorporates human behavior data into the design of “agents” who respond to various situations, to identify the outcomes of various earthquake and infrastructure response scenarios beyond those used in the interview/discussion/exercise work. Key informants in infrastructure planning and emergency response in Washington and Oregon, the states most vulnerable to consequences of a CSZ earthquake, will be consulted and informed throughout the project to maximize its value, with wider distribution of results to multiple stakeholders through policy briefs, white papers, and presentations to enhance its value for practitioners. The project aims to provide scientific evidence for a new decision-aid tool to be further researched and developed. This project’s proposed integration of social science (e.g., organizational operations analysis, trust, values) and engineering theories and methods (e.g., resilience engineering, ABM) is largely untested but has the potential to provide first insights into how interorganizational processes support or hinder coordinated recovery of interdependent infrastructures. Despite the uniqueness of a CSZ earthquake event, it is expected that much of the new knowledge generated through this project will be generalizable to other disasters and regions, given that failures of multiple infrastructures do occur simultaneously in other hazard events. Anonymized qualitative data and open-source simulation software will be made publicly available for other researchers and practitioners.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.