CAREER: Policy-Infrastructure-Community Interdependencies: The Next Frontiers in Dynamic Networks

灾害是人类和基础设施的危险和脆弱性的综合影响造成的。该学院早期职业发展（CAREER）补助金旨在了解人与基础设施之间的相互作用，以应对危险。这些相互作用往往在灾害发生期间和之后导致意想不到的结果。一个基础设施系统的故障会直接或间接地影响到其他基础设施，并对社会经济部门造成严重影响。此外，在基础设施管理中缺乏对社会和组织方面的考虑，可能导致损害社区的意外后果。因此，这些相互作用是不确定的，它们随着时间的推移而变化，以应对危险，发展和技术。如果我们能够捕捉到这种相互依赖的动态，我们就可以积极干预，以防止连锁故障，减少损失，并改善恢复。该项目的目标是（一）开发下一代动态网络，以模拟政策，基础设施和社区之间的不确定的相互依赖关系，（二）通过社区参与和以人为本的研究项目培训未来的工程从业人员和研究人员，以及（三）通过媒体，艺术和女童子军活动提高公众对备灾的认识。该项目符合NSF的使命，以促进科学的进步和促进国家福利。具体而言，这种综合研究和教育的成果将改变政策制定，以确保社区在短期和长期风险下的复原力和可持续性。研究方法建立在统计网络模型上，该模型根据对结构或历史数据的部分观察提供未来网络结构的概率估计。这些模型提供了一种编码控制网络结构的潜在参数的方法，为推理和预测提供了优势。该项目将开发一类新的统计网络模型，以适应多个网络的动态。这些模型将由利益相关者提供信息，并与决策分析和贝叶斯方法等经验方法和建模技术相结合，以捕捉灾害期间的人类行为。顶点和边协变量的合并将使用预测分析的统计学习方法完成。这些新的模型将使用两个城市城市化和自然灾害增加的数据驱动情景来实施，以促进对不同背景下可持续性和复原力评估多样性的了解。将开发网络预测分析算法，以量化可持续弹性指标。通过跨学科研究项目和国际经验提供沉浸式教育机会，让学生参与社区，增强学习体验，同时为研究和以人为本的灾难解决方案做出贡献。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。

Disasters result from the combined effect of hazards and vulnerability of humans and infrastructures. This Faculty Early Career Development (CAREER) grant seeks to understand the interactions between people and infrastructures in response to hazards. These interactions often lead to unexpected outcomes during and in the aftermath of disasters. The failure of an infrastructure system can directly or indirectly affect other infrastructures and result in crippling effects in socio-economic sectors. In addition, a lack of consideration of social and organizational aspects in infrastructure management can lead to unintended consequences that harm communities. As such, these interactions are uncertain, and they change over time in response to hazards, development, and technology. If we can capture the dynamics of such inter-dependencies, we can proactively intervene to prevent cascading failures, reduce losses, and improve recovery. The objectives of this project are to (i) develop the next generation of dynamic networks to model the uncertain inter-dependencies between policy, infrastructure, and the community, (ii) train future engineering practitioners and researchers through community engagement and human-centered research projects, and (iii) increase public awareness of disaster preparedness through media, arts, and Girl Scouts activities. This project aligns with NSF’s mission to promote the progress of science and to advance the national welfare. Specifically, the outcome of this integrated research and education will transform policy making to ensure the resilience and sustainability of communities under short- and long-term risks.The research approach is founded in statistical network models which provide a probabilistic estimation of future network structures based on partial observation of the structure or historical data. These models provide an approach to encode latent parameters that govern network structures, offering advantages for inference and prediction. This project will develop a new class of statistical network models that accommodates the dynamics of multiple networks. The models will be informed by stakeholders and integrated with empirical approaches and modeling techniques such as decision analysis and Bayesian methods to capture human behavior during disasters. Incorporation of vertex and edge covariates will be accomplished using statistical learning methods for predictive analytics. These new models will be implemented using data-driven scenarios of two cities with increased urbanization and natural hazards to advance knowledge on the diversity of sustainability and resilience assessments in different contexts. Algorithms for network predictive analytics will be developed to quantify sustainable resilience indicators. Immersive educational opportunities through interdisciplinary research projects and international experiences will engage students with communities and enhance their learning experience while contributing to research and human-centered solutions to disasters.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.