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

Abstract:

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 PRISM Cooperative Institute was established 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). We will introduce Critical Risk Indicators (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 will explore Dynamic Risk Interconnections through dynamic models for CRIs; and we will introduce Systemic Risk Indicators (SRIs) to model trans-domain systemic risk, to assess CRI spillover potential, and to help identify systemically important risks for stakeholder intervention.