Quantitative risk analyses depend on models of event severity, exposed units (people, assets, environment), and the vulnerability of those exposed units. Understanding the physical events involved in extreme events and natural disasters, as well as their consequences, is becoming increasingly complex due to socioeconomic shifts, climate change, and the systemic and dependent nature of the hazards.
The special focus of the Risk Analysis and Modeling research group is on the development of innovative methods and models that account for this complexity, plus the application of these techniques to developing robust risk-management strategies and adaptation options to improve resilience to extreme events.
Process-based stakeholder approaches are used, linked to the notion of iterative risk management, which is key to adapting to a changing and uncertain future.
These approaches are based on decision-support tools and methodologies, like the CATSIM model.
The methods and models employed are at the cutting edge of risk analysis. They include:
i) The explicit incorporation of tail dependencies via copulas within large-scale assessments;
ii) The differentiation of risk management options according to risk-layers;
iii) The inclusion of cascading effects and inter-dependencies of risk bearers.
The Risk Analysis and Modeling research theme is part of two IIASA cross-cutting projects: Systemic Risk and Network Dynamics and Accounting for socioeconomic heterogeneity in IIASA models.
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Improving the resilience of society to catastrophic natural hazards through new risk-management partnerships More
Last edited: 20 September 2017
International Institute for Applied Systems Analysis (IIASA)
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