Exploring the Space of Possibilities in Cascading Disasters with Catastrophe Dynamics

Abstract

Some of the most devastating natural events on Earth, such as earthquakes and tropical cyclones, are prone to trigger other natural events, critical infrastructure failures, and socioeconomic disruptions. Man-made disasters may have similar effects, although to a lesser degree. We investigate the space of possible interactions between 19 types of loss-generating events, first by encoding possible one-to-one interactions into an adjacency matrix A, and second by calculating the interaction matrix M of emergent chains-of-events. We first present the impact of 24 topologies of A on M to illustrate the non-trivial patterns of cascading processes, in terms of the space of possibilities covered and of interaction amplification by feedback loops. We then encode A from 29 historical cases of cascading disasters and compute the matching matrix M. We observe, subject to data incompleteness, emergent cascading behaviors in the technological and socioeconomic systems, across all possible triggers (natural or man-made); disease is also a systematic emergent phenomenon. We find interactions being mostly amplified via two events: network failure and business interruption, the two events with the highest in-degree and betweenness centralities. This analysis demonstrates how cascading disasters grow in and cross over natural, technological, and socioeconomic systems.

Are We Missing the Target? A Bias-Variance Perspective on Multi-Hazard Risk Assessment

“…The direct consequence of reductionism to the topic presented herein is that current hazard related risk assessment treats the natural phenomena (hazards) mostly in isolation. In the context of the bias-variance perspective, "single-hazard" methods can be considered highly biased as isolation of phenomena leads to the omission of relational behaviours, causal processes, and their resulting emergent properties [e.g., risk amplification (Mignan et al, 2014;Mignan and Wang, 2020)]. From this perspective, using multi-hazard ensembles seems to be a valuable proposition for the future of risk assessment as the added complexity and richness (meaning gathering of data of different types as opposed to data gathering of the same type) (Figure 2) would lead to a reduction of the bias and predictive model closer to the "ground truth" [not dissimilar to "ensemble learning" approaches in machine learning (Opitz and Maclin, 1999)].…”

Introduction to the Special Issue on Cascading Disaster Modelling and Prevention

“…Mignan and Wang [ 5 ] analyzed the occurrence of 24 different types of natural, socioeconomic, and health-related hazard events, in a set of 29 cascading disaster events. Their analysis accounted for both unidirectional triggers and feedback loops between one hazard event and another.…”

“…The same analysis demonstrated how historical hazard events have triggered a broad range of natural, technological, and socioeconomic impacts. Analytical challenges remain for estimating the timeframe of event-to-event interactions and for addressing multifactorial dynamics [ 5 ], for example: the dual effects of wind and drought on wildfires.…”

“…To support more comprehensive analyses of rain-related cascading dynamics, the authors have generated a matrix of potential trigger-impact relationships that can be populated with localized data or expert opinion ratings. The resulting data could then be used for a range of analyses, including the network analysis employed by Mignan and Wang [ 5 ], and both multi-level [ 8 ] and multi-system [ 9 ] analyses.…”

“…Dubaniowski and Heinimann [ 9 ] used an elaborate network analysis to more closely analyze the type of business and infrastructure disruptions highlighted by Mignan and Wang [ 5 ]. A set of simulations was used to establish an overall system-of-systems model for a range of hazard events.…”