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根本。如果基础设施部门相互依赖，如果其运营商可能缺乏对其他系统状况和恢复计划的了解，相互等待其他系统采取行动，就可能造成恢复延误。对于相互依赖的基础设施恢复，运营商所做的决定在研究中被忽视，但它是加快恢复时间的重要因素。为了改善相互依赖的基础设施系统的恢复，从根本上需要拓宽当前的视角，以包括1）各个运营商如何为其基础设施系统做出恢复决策，以及2）如何改善这些决策。这个关键的弹性相互依赖的基础设施系统和流程（CRISP）项目比较了各种干预措施的有效性，包括信息共享，以减少恢复时间。该项目使用相互依赖的冷冻水，信息技术和电力基础设施在马里兰州大学作为初步的案例研究。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根据模型评估共同管理的机会，以改善基础设施的恢复时间。研究生也将接受这些方法的指导和培训，以促进对相互关联的物理和决策结构的多学科理解，从而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背景下使用信息进行决策。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的游戏来使用新的工具，以确定信息和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基于对物理行为的理解，观察到的操作员行为，以及管理决策规则和操作员的策略。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。这种跨学科的研究旨在发展一种新的理论理解的相互依赖的基础设施，由运营商的行为，并确定实际的干预措施，改善这些相互依赖的系统的共同管理。通过比较计算机模拟的基础设施恢复模型，产生一个“理论”的最佳操作员揭示的“实际”的最佳通过严重的游戏，进一步增加了价值。通过评估当前学术优化恢复模型和实际操作员恢复行为之间的差异，为更好的决策提供了激励，这为相互依赖的基础设施领域带来了重大进步。该奖项反映了NSF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.