Risk Analysis· 2026Q1
Inseparability in Disaster Risk Science: Accounting for Coupled, Co‐Evolving Systems
- 1citations
- Q1SCImago
- 2026year
Short summary
Disaster risk science should adopt 'inseparability' as a foundational premise, treating hazard, exposure, and vulnerability as dynamically coupled processes rather than separable variables, to better account for systemic phenomena like compound disasters and cascading failures.
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Key points
- Contemporary risk landscapes are shaped by systemic phenomena (compound disasters, cascading failures) that challenge traditional risk frameworks.
- The classical risk equation assumes separability, which breaks down in feedback-rich, co-evolving systems.
- The paper proposes 'inseparability' as a foundational premise for disaster risk science.
- Hazard, exposure, and vulnerability should be viewed as dynamically coupled processes, not separable variables.
- This shift necessitates reframing vulnerability, addressing cross-scale dynamics, adopting adaptive management, and valuing knowledge co-production.
AI-generated from the title and abstract; the full text is not read.
Abstract
Contemporary risk landscapes are shaped by systemic phenomena-compound disasters, cascading failures, non-stationarity, and cross-domain crises-that challenge conventional frameworks. While the classical risk equation (Risk = Hazard × Vulnerability × Exposure) remains a useful heuristic, it relies on a separability assumption that breaks down in feedback-rich, co-evolving systems. This failure reflects a familiar diagnosis: risk can be mistaken for a complicated problem, decomposable and optimizable, when it is a complex one, characterized by emergence and adaptation. Our contribution herein is not this distinction but its analytical consequence. Drawing on an interdisciplinary workshop on systemic risk and resilience, we propose inseparability as a foundational premise for disaster risk science. Hazard, exposure, and vulnerability might be better understood as dynamically coupled processes rather than separable variables. We derive implications across four axes: reframing vulnerability as a non-ergodic system property; addressing cross-scale dynamics; shifting from risk control to adaptive management; and treating knowledge co-production as a scientific necessity.
The authors' abstract, as published at the source. Risk Analysis, 2026 · DOI ↗
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Field: Sociology and Political Science
Sociology and Political ScienceSocial Sciences