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.
Authors
- Nazli Yonca Aydin (ORCID: https://orcid.org/0000-0002-9628-8998)
- Benjamin D. Trump (ORCID: https://orcid.org/0000-0002-4097-733X)
- Margherita Maraschini (ORCID: https://orcid.org/0000-0003-2435-9141)
- Mirta B. Gordon (ORCID: https://orcid.org/0000-0002-8186-7591)
- Lauren McMillan (ORCID: https://orcid.org/0000-0003-3851-8019)
- Tina Comes (ORCID: https://orcid.org/0000-0002-8721-8314)
- Igor Linkov (ORCID: https://orcid.org/0000-0002-0823-8107)
- Mark Pelling (ORCID: https://orcid.org/0000-0002-6472-9875)
- Silvia Torresan (ORCID: https://orcid.org/0000-0002-9758-7084)
- Gianluca Pescaroli (ORCID: https://orcid.org/0000-0003-3468-2970)
- João Pedro Oliveira (ORCID: https://orcid.org/0009-0000-3370-3915)
Institutions
- 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)
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