Downward Counterfactual Search for Extreme Events

Abstract

An event catalog is a foundation of the risk analysis for any natural hazard. Especially if the catalog is comparatively brief relative to the return periods of possible events, it may well be deficient in extreme events that are of special importance to risk stakeholders. It is common practice for quantitative risk analysts to construct ensembles of future scenarios that include extreme events that are not in the event catalog. But past poor experience for many hazards shows that these ensembles are still liable to be missing crucial unknown events. An explicit systematic procedure is proposed here for searching for these key missing events. This procedure starts with the historical catalog events, and explores alternative realizations of them where things turned for the worse. These are termed downward counterfactuals. By repeatedly exploring ways in which the event loss might have been incrementally worse, missing events can be discovered that may take risk analysts, and risk stakeholders, by surprise. The downward counterfactual search for extreme events is illustrated with examples drawn from a variety of natural hazards. Attention is drawn to the problem of overfitting to the historical record, and the value of stochastic modeling of the past.

Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

“…Instead, modelling and simulation can be used to explore extreme unseen events (Sect. 4.2) as well as resampling and stochastic weather generation techniques that provide a quantitative underpinning to storyline approaches (Woo, 2019;Shepherd et al, 2018;Matthews et al, 2019). The role of spatial and temporal correlations in hydrological hazards like drought is important for probabilistic calculations of risk.…”

Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

“…Instead, modelling and simulation can be used to explore the so far unseen events. As the name suggests, the climate modelling framework in Section 4.2 may be particularly well suited to this task, but so too are resampling and stochastic weather generation techniques that provide a quantitative underpinning to storyline approaches (Woo, 2019;Shepherd et al, 2018;Matthews et al, 2019).…”

Modeling Downward Counterfactual Events: Unrealized Disasters and why they Matter

“…The proposed downward counterfactual framework to uncover unrealized, unexpected events has six key steps, described below and in Figure 1. This work builds on key downward counterfactual risk literature, specifically as it is described in Woo (2019). In previous descriptions of a downward counterfactual search, the process was as follows: one could imagine what would make an event worse by some amount-say 10%-and then find at least one pathway toward that increased consequence.…”

“…Current risk analysis falls short of addressing these forms of extreme uncertainty, so we propose a framework to identify unexpected events for the purpose of building resilience in an increasingly dynamic and uncertain world. In particular, we propose to build upon recent downward counterfactual thought in natural hazards and risk literature (e.g., Woo et al, 2017;Aspinall and Woo, 2019;Woo, 2019) for the purpose of a more robust risk analysis and disaster planning and preparedness. Downward counterfactual thought, an analysis of what might have been worse compared to what has actually transpired, according to experts in psychology, is more challenging than its optimistic foil, upward counterfactual thought, where one reimagines a better outcome than the one that has transpired (Roese, 1997).…”