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


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. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.

Critical Risk Indicators (CRIs)

We 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. We aim to identify critical risks and existing indicators in many domains, and develop new CRIs by harnessing the data revolution.

Dynamic Risk Interconnections

We will dynamically model and forecast CRIs. We aim 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 multiresolution attributes.

Systemic Risk Indicators (SRIs)

We will model trans-domain systemic risk, by forecasting critical risk spillovers and via the creation of SRIs for facilitating stakeholder intervention analysis.

Validation & Stakeholder Engagement

We will deploy the PRISM analytical framework on integrative case studies with distinct risk exposure (acute versus cumulative) and catastrophe characteristics (immediate versus sustained). We will solicit regular input from key stakeholders regarding critical risks and their decision variables, to better inform our operational understanding of policy versus practice.