CRISP Type 2: Collaborative Research: Simulation-Based Hypothesis Testing of Socio-Technical Community Resilience Using Distributed Optimization and Natural Language Processing.

电力、制造业和金融系统等关键基础设施是社会运转和社区健康的关键。该项目产生的新知识将改善关键基础设施的设计和管理，以建立应对小规模破坏和大规模灾害的复原力。该项目的重点是不同类型的关键基础设施之间的社会和技术联系。该研究深入探讨了社会力量对关键基础设施的影响，以及关键基础设施在促进社区认同和福祉方面的作用。从这项研究中产生的知识和工具为改善关键基础设施的运作和运作的战略提供了信息，以实现社会确定的目标。休斯顿和西雅图地区的专家和决策者为项目成果作出贡献并进行评估，以确保由此产生的工具与关注提高社区抗灾能力的利益攸关方相关。从土木工程，计算机科学和社会科学的研究方法结合联合收割机，以实现该项目的目标。该研究的三个主要目标是：1）系统地重新思考关键基础设施作为社会和技术系统的网络，2）建立计算机模拟模型，以探索重大和轻微中断后的关键基础设施性能，3）测试假设，以了解关键基础设施如何提高弹性并支持社区的多样化需求。研究团队整合定性和定量数据来构建项目的模拟模型。学者们从许多文本来源（如社交媒体、新闻报道、政府文件和行业报告）中汇编和分析有关过去关键基础设施中断和灾难的定性数据。他们使用新的自然语言处理（NLP）方法分析文本数据，以确定描述关键基础设施和社区复原力的关键变量，以及它们之间的关系。他们从政府和学术数据库等现有来源收集描述相关变量的定量和地理数据。他们还在辅导讲习班期间使用定制调查技术从专家那里获得定量数据。然后，他们使用数据分析的结果来指定计算机模型，以模拟在关键基础设施中断和灾难之后跨多个地理尺度发生的许多事件，资源交换和决策。最后，团队设计技术来整合和优化构建的计算机模型。这使得能够有效地检验关于关键基础设施绩效与社区复原力之间关系的假设。

Critical infrastructures, such as electric, manufacturing, and financial systems, are key to the functioning of society and the health of communities. The new knowledge from this project will improve the design and management of critical infrastructure to build resilience in the face of minor disruptions and large disasters. The project focuses on the social and technical links between different types of critical infrastructure. The research provides insights into the influence of social forces on critical infrastructure and the roles of critical infrastructure in promoting a community's identity and well-being. The knowledge and tools generated from this research inform strategies to improve the functioning and operation of critical infrastructure in order to achieve socially defined goals. Houston and Seattle area experts and decision makers contribute to and evaluate project outcomes to ensure that resulting tools are relevant to stakeholders concerned with increasing community resilience capacity. Research methods from civil engineering, computer science, and social science combine to achieve the goals of the project. Three primary goals of the research are to 1) systematically rethink critical infrastructure as a web of social and technical systems, 2) build computer simulation models to explore critical infrastructure performance after major and minor disruptions, and 3) test hypotheses to appreciate how critical infrastructure can improve resilience and support the diverse needs of communities. The research team integrates qualitative and quantitative data to construct the project's simulation models. The scholars compile and analyze qualitative data about past critical infrastructure disruptions and disasters from many text sources, such as social media, news stories, government documents, and industry reports. They use new natural language processing (NLP) methods analyze the text data in order to identify key variables describing critical infrastructure and community resilience, as well as the relationships among them. They collect quantitative and geographic data describing related variables from existing sources, such as government and academic databases. They elicit quantitative data also from experts using customized survey techniques during facilitated workshops. They then use the results of the data analysis to specify computer models that simulate the many events, resource exchanges, and decisions that occur across multiple geographic scales after critical infrastructure disruptions and disasters. Finally, the team devises techniques to integrate and optimize the constructed computer models. This permits efficient testing of hypotheses about the relationships between critical infrastructure performance and community resilience.