Collaborative Research: Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis

自然-人类世界的特点是高度相互关联的系统，在这些系统中，没有一个学科具备识别系统性风险和缓解目标的更广泛迹象的能力。例如，气候多变和变化加剧了农业、生态、能源、金融和水文方面的哪些风险？例如，太空天气方面的风险如何与能源、水和金融联系在一起？计算和数据科学的最新进展，以及每个领域的数据革命，现在都为解决这些问题提供了手段。研究人员共同建立了PRISM合作研究所，开创了跨不同领域的大规模、多分辨率、动态数据的集成，以改进对风险(可能出现极端结果和系统故障)的预测。调查人员的愿景是开发一个跨领域的框架，在领域专业知识的背景下利用大数据来发现新的关键风险指标，全面确定它们的相互联系，预测未来的风险和溢出潜力，并广泛衡量系统性风险。调查人员将与利益攸关方合作，最终创建关键风险缓解的早期预警和目标，并加强对世界各地破坏性事件的准备；通过博士后研究人员和学生交流、研究务虚会和研讨会，形成广泛和独特的伙伴关系，以教育下一代数据科学家；通过招募和培训STEM中代表性不足的人，包括妇女和代表不足的少数族裔学生，扩大参与范围，并通过PRISM数据图书馆网络服务实现的方法、工具和数据集，扩大对利益攸关方社区的影响。PRISM合作研究所的数据密集型跨学科研究方向包括：(I)关键风险指标(CRI)；研究人员将CRI定义为与灾难性(累积)活动或灾难性事件造成的毁灭性、破坏性损失的累积或急性风险暴露具体相关的可量化信息。PRISM的目标是识别许多领域的关键风险和现有指标，并通过利用数据革命开发新的CRI；(Ii)动态风险互联；调查人员将动态建模和预测CRIS，PRISM的目标是强有力地识别关键社会风险的稀疏、可解释的领先-滞后风险依赖结构，使用最先进的方法来适应CRI的复杂性，如非平稳、时空和多分辨率属性；(Iii)系统风险指标(SRI)；PRISM将通过预测关键风险溢出和创建SRI来模拟跨域系统风险，以促进利益相关者干预分析；(Iv)验证和放大；利益相关者的参与；调查人员将在具有不同风险暴露(急性与累积)和灾难特征(即时与持续)的综合案例研究上部署PRISM分析框架，并将就关键风险及其决策变量定期征求关键利益相关者的意见，以更好地告知他们对政策与实践的操作理解。该项目是国家科学基金会利用数据革命(HDR)大想法活动的一部分，由HDR和NSF数学和物理科学理事会内的数学科学部联合支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The natural-human world is characterized by highly interconnected systems, in which a single discipline is not equipped to identify broader signs of systemic risk and mitigation targets. For example, what risks in agriculture, ecology, energy, finance and hydrology are heightened by climate variability and change? How might risks in, for example, space weather, be connected with energy, water and finance? Recent advances in computing and data science, and the data revolution in each of these domains have now provided a means to address these questions. The investigators jointly establish the PRISM Cooperative Institute for pioneering the integration of large-scale, multi-resolution, dynamic data across different domains to improve the prediction of risks (potentials for extreme outcomes and system failures). The investigators' vision is to develop a trans-domain framework that harnesses big data in the context of domain expertise to discover new critical risk indicators, holistically identify their interconnections, predict future risks and spillover potential, and to measure systemic risk broadly. The investigators will work with stakeholders to ultimately create early warnings and targets for critical risk mitigation and grow preparedness for devastating events worldwide; form wide and unique partnerships to educate the next generation of data scientists through postdoctoral researcher and student exchanges, research retreats, and workshops; and broaden participation through recruiting and training of those under-represented in STEM, including women and underrepresented minority students, and impact on stakeholder communities via methods, tools and datasets enabled by PRISM Data Library web services.The PRISM Cooperative Institute's data-intensive cross-disciplinary research directions include: (i) Critical Risk Indicators (CRIs); The investigators define CRIs as quantifiable information specifically associated with cumulative or acute risk exposure to devastating, ruinous losses resulting from a disastrous (cumulative) activity or a catastrophic event. PRISM aims to identify critical risks and existing indicators in many domains, and develop new CRIs by harnessing the data revolution; (ii) Dynamic Risk Interconnections; The investigators will dynamically model and forecast CRIs and PRISM aims to robustly identify a sparse, interpretable lead-lag risk dependence structure of critical societal risks, using state-of-the-art methods to accommodate CRI complexities such as nonstationary, spatiotemporal, and multi-resolution attributes; (iii) Systemic Risk Indicators (SRIs); PRISM will model trans-domain systemic risk, by forecasting critical risk spillovers and via the creation of SRIs for facilitating stakeholder intervention analysis; (iv) Validation & Stakeholder Engagement; The investigators will deploy the PRISM analytical framework on integrative case studies with distinct risk exposure (acute versus cumulative) and catastrophe characteristics (immediate versus sustained), and will solicit regular input from key stakeholders regarding critical risks and their decision variables, to better inform their operational understanding of policy versus practice.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by HDR and the Division of Mathematical Sciences within the NSF Directorate of Mathematical and Physical Sciences.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.