CRISP 2.0 Type 1: Accelerating restoration through information-sharing: Understanding operator behavior for improved management of interdependent infrastructure

灾后迅速恢复基础设施服务是社区恢复的基础。当基础设施部门相互依赖，并且运营商可能缺乏对其他系统条件和恢复计划的了解，相互等待其他人采取行动时，可能会造成恢复延迟。对于相互依赖的基础设施恢复，运营商的决策在研究中被忽视了，但这是加快恢复时间的一个重要因素。为了改善相互依赖的基础设施系统的恢复，有一个基本的需要拓宽当前的视角，包括1)单个运营商如何为其基础设施系统做出恢复决策，以及2)如何改进这些决策。关键弹性相互依赖基础设施系统和流程（CRISP）项目比较了包括信息共享在内的各种干预措施在减少恢复时间方面的有效性。该项目将相互依赖的冷冻水、信息技术和马里兰大学的电力基础设施作为最初的案例研究。为了探索案例研究之外的有用性，与美国军事设施的合作将评估共同管理的机会，给定模型，以改善基础设施的恢复时间。研究生也将在这些方法方面得到指导和培训，以促进对相关物理和决策结构的多学科理解，以改善基础设施故障后的恢复。该项目将深入了解运营商如何在相互依赖的基础设施环境中利用信息做出决策。从技术上讲，该研究通过整合监管分析、半结构化访谈、故障树分析、基于代理的建模和严肃游戏等新工具，来确定信息、其他推动和监管干预如何改变决策，并最终改变基础设施恢复过程。建模和严肃的游戏方法是建立在对物理行为、观察到的操作员行为、管理决策规则和操作员启发式的理解之上的。这项研究的独特之处在于相互依赖的基础设施建模和决策科学的交叉。这项跨学科研究旨在通过运营商的行为对相互依赖的基础设施进行新的理论理解，并确定切实可行的干预措施，以改善这些相互依赖系统的共同管理。通过比较计算机模拟的基础设施恢复模型，进一步增加了价值，这些模型产生了“理论”最优，与运营商通过严肃的游戏揭示的“实际”最优。通过评估当前学术优化采收率模型与实际作业者采收率行为之间的差异，该研究在相互依赖基础设施领域取得了重大进展。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。

Rapid restoration of infrastructure services following a disaster is fundamental for community recovery. Restoration delays may be created when infrastructure sectors are interdependent and when their operators, potentially lacking knowledge of other systems' conditions and restoration plans, mutually wait for others to act. For interdependent infrastructure recovery, the decisions made by the operators has been overlooked in research, yet it is an important factor in quicker recovery times. To improve recovery of interdependent infrastructure systems, there is a fundamental need to broaden the current perspective to include 1) how individual operators make recovery decisions for their infrastructure systems, and 2) how to improve these decisions. This Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) project compares the effectiveness of a variety of interventions, including information sharing, in reducing restoration times. The project uses interdependent chilled water, information technologies, and electric-power infrastructures at the University of Maryland as an initial case study. To explore the usefulness beyond the case study, a collaboration with a U.S. military installation will evaluate the co-management opportunities, given the models, to improve infrastructure recovery time. Graduate students will also be mentored and trained in these approaches to facilitate a multidisciplinary understanding of linked physical and decision structures to improve infrastructure recovery after a failure.This project will produce insights into how operators use information to make decisions in the context of interdependent infrastructure. Technically, the research employs novel tools by integrating regulatory analysis, semi-structured interviews, fault tree analysis, agent-based modeling, and serious gaming, to determine how information, and other nudges and regulatory interventions, alter decisions, and ultimately infrastructure recovery processes. The modeling and serious gaming approaches are strongly grounded in an understanding of the physical behavior, observed operator behavior, and governing decision rules and heuristics of operators. Unique to this study is the intersection of interdependent infrastructure modeling and decision science. This interdisciplinary research seeks to develop a new theoretical understanding of interdependent infrastructure informed by operator behavior and to identify practical interventions that improve the co-management of these interdependent systems. Further value is added by comparing computer simulated infrastructure recovery models that produce a "theoretical" optimum to an operator-revealed "practical" optimum via serious gaming. This presents a significant advance to the interdependent infrastructure field by evaluating the divide between current academic optimization recovery models and actual operator recovery behavior given inducements for better 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.