About this Research Topic
The scientific study of natural hazards and their associated risks depends crucially on the history of events that have occurred. For rare extreme events, this history may be very sparse, and this may lead to statistical over-fitting.
Whilst the Earth and Atmospheric sciences are observational rather than experimental, it is possible to undertake counterfactual thought experiments, recognizing that all hazard events are generated by underlying stochastic processes which can be simulated numerous times.
Rather than treating the historical record as being a fixed and unchangeable data source, stochastic simulation of the past can be undertaken to enhance the analysis of tail risk associated with rare extreme events. Counterfactual thinking provides a conceptual bridge to the psychology of surprise. By reimagining history, otherwise surprising events can be anticipated which may have no historical precedent.
Counterfactual risk analysis has already been demonstrated to reveal hitherto unknown
surprises, including events previously described as Black Swans. Just as the concept of a Black Swan is universally applicable to all perils, the same holds for counterfactual risk analysis. Examples may be drawn from all branches of the Earth and Atmospheric sciences, from runaway volcanic explosions and great earthquakes, to major storms and floods and compound climate change events.
The impacts of hazard events are subject to a significant degree of stochastic variability, which has only been partially explored. The search for downward counterfactuals, i.e. worse outcomes, is illuminating for all risk stakeholders, including engineers, architects, civic authorities and insurers. Counterfactual risk analysis is an evolving inter-disciplinary area of research that can generate new insights for the management and mitigation of extreme risks.
Contributions are invited for all natural hazard applications. Papers addressing issues relating to climate change would be welcome.
Keywords: counterfactual risk, Black Swan, extreme hazard, surprise events, stochastic simulation
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