Definitions and taxonomy for High Impact Low Probability (HILP) and outlier events

High Impact Low Probability events (HILPs) , often referred to as outliers, are becoming more important in disaster management because they are linked to complex risks and tipping points in interconnected systems. Recent events, such as the cascading effects of the coronavirus pandemic, rising uncertainties from global geopolitical instability, and successive and concurrent extremes driven by climate change, underscore the limitations of relying solely on severe but plausible scenarios for risk practitioners and policymakers. Despite the critical need to integrate HILPs into risk assessment models and emergency preparedness, the field is fragmented, with inconsistent definitions and methodologies. We present a perspective developed under the HORIZON AGILE project (AGnostic risk management for high Impact Low probability Events), which introduces two comprehensive definitions of HILPs and a taxonomy designed to enhance risk assessment, resilience analysis, and crisis management. We provide a validated scientific definition for the academic community and an operational definition tailored for practitioners and stakeholders. Additionally, our taxonomy offers a structured framework to address outlier events that often fall below traditional risk thresholds, ensuring that low-probability, high-impact scenarios with cascading and concurrent dynamics are effectively integrated into risk registers, legislation, and standards development. This study shows how this approach improves methods like stress testing and scenario modelling, especially for the loss of critical services . This empowers policymakers, practitioners, and stakeholders to include more scenarios in their strategies, enhancing resilience and preparedness.

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Publication Details

Journal
International Journal of Disaster Risk Reduction
Published
2025-05-15
DOI
https://doi.org/10.1016/j.ijdrr.2025.105504
Citations
4
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
9.92
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article

Definitions and taxonomy for High Impact Low Probability (HILP) and outlier events

Nazli Yonca Aydin, Benjamin D. Trump, Margherita Maraschini, Mirta B. Gordon et al.
4 citations
International Journal of Disaster Risk Reduction
Anomaly Detection Techniques and Applications
9.92
article

Definitions and taxonomy for High Impact Low Probability (HILP) and outlier events

Nazli Yonca Aydin, Benjamin D. Trump, Margherita Maraschini, Mirta B. Gordon, Lauren McMillan, Tina Comes, Igor Linkov, Mark Pelling, Silvia Torresan, Gianluca Pescaroli, João Pedro Oliveira
article en
4 citations

Abstract

High Impact Low Probability events (HILPs) , often referred to as outliers, are becoming more important in disaster management because they are linked to complex risks and tipping points in interconnected systems. Recent events, such as the cascading effects of the coronavirus pandemic, rising uncertainties from global geopolitical instability, and successive and concurrent extremes driven by climate change, underscore the limitations of relying solely on severe but plausible scenarios for risk practitioners and policymakers. Despite the critical need to integrate HILPs into risk assessment models and emergency preparedness, the field is fragmented, with inconsistent definitions and methodologies. We present a perspective developed under the HORIZON AGILE project (AGnostic risk management for high Impact Low probability Events), which introduces two comprehensive definitions of HILPs and a taxonomy designed to enhance risk assessment, resilience analysis, and crisis management. We provide a validated scientific definition for the academic community and an operational definition tailored for practitioners and stakeholders. Additionally, our taxonomy offers a structured framework to address outlier events that often fall below traditional risk thresholds, ensuring that low-probability, high-impact scenarios with cascading and concurrent dynamics are effectively integrated into risk registers, legislation, and standards development. This study shows how this approach improves methods like stress testing and scenario modelling, especially for the loss of critical services . This empowers policymakers, practitioners, and stakeholders to include more scenarios in their strategies, enhancing resilience and preparedness.

International Journal of Disaster Risk ReductionVol. 127
National Science and Technology Center for Disaster Reduction (TW), University of Cambridge (GB), University of Michigan (US), Northumbria University (GB), Bridge University (SS), CMCC Foundation - Euro-Mediterranean Center on Climate Change (IT), University College London (GB), Carnegie Mellon University (US), Delft University of Technology (NL)
Openalex Percentile: Top 2%
Anomaly Detection Techniques and Applications
9.92
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