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

关键的基础设施，如电力、制造业和金融系统，是社会运作和社区健康的关键。本项目的新知识将改善关键基础设施的设计和管理，以建立应对小破坏和大灾害的复原力。该项目侧重于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。本研究产生的知识和工具为改善关键基础设施的功能和运营提供了策略，以实现社会定义的目标。休斯顿和西雅图地区的专家和决策者为项目成果做出贡献并对其进行评估，以确保所产生的工具与关注提高社区恢复能力的利益相关者相关。研究方法从土木工程，计算机科学和社会科学相结合，以实现项目的目标。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，探索重大和轻微中断后关键基础设施的性能；3)测试假设，以了解关键基础设施如何提高弹性和支持社区的多样化需求。研究团队将定性和定量数据相结合，构建项目仿真模型。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）中收集和分析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弹性的关键变量，以及它们之间的关系。他们从现有来源（如政府和学术数据库）收集描述相关变量的定量和地理数据。他们还在便利的讲习班期间使用定制的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，这些模型可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，该团队设计了集成和优化构建的计算机模型的技术。这允许对关键基础设施性能和社区弹性之间关系的假设进行有效测试。

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.