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One risk, two perspectives. Black Swans and Black Elephants both describe hazard events with high impact, high exposure, and high vulnerability but are viewed from two different vantage points. With knowledge but without acknowledgment, a Black Swan becomes a Black Elephant. Both have the potential for catastrophic impacts.
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Devastating disasters that are predicted but ignored are known as Black Elephants—a cross between a Black Swan event and the proverbial elephant in the room. It’s time we acknowledged the looming natural hazard risks that no one wants to talk about.
Citations
... A 'predictable surprise' is 'an event that leads an organization or nation to react with surprise, despite the fact that the information necessary to anticipate the event and its consequences was available' (Watkins and Bazerman 2003;Bazerman 2006). The predictable-surprise concept originated from a business background, yet in other contexts such as ethics and politics, the same idea has also been referred to as "black elephant", a term that combines Taleb's (2007) idea of 'black swans' and the proverbial 'elephant in the room' (Möller and Wikman-Svahn 2011;Lin et al. 2021). Thus, black elephants are high-impact negative events or chains of events that are not adequately addressed. ...
... Möller and Wikman-Svahn (2011) refer to an Internet blog entry that was apparently written in 2009 by someone named Gupta as their source of the concept, yet we could not open the blog as given in their reference list. A recent paper by Lin et al. (2021) refers to an NY Times piece (Stampeding Black Elephants, 11/23/2014) by T. L. Friedman, which is however obviously not the first mention of the concept. ...
We combine the concepts of ‘black elephants’ and wicked problems with Roy Bhaskar’s critical realist philosophy of science and frame the current state of the coronavirus pandemic as an analogy for impending sustainability challenges. We point out and illustrate that the interaction of different ontological levels of our world as it ‘is’ will likely remain a challenge in addressing the wicked problems of our time.
... Black Elephants have previously been discussed in the context of environmental disasters such as global warming, deforestation, mass extinction, and fresh-water pollution (Friedman, 2014), and in a recent comment piece by the authors, we expanded this conversation to feature the looming natural hazards that fill the risk horizons of Asia (Lin et al., 2021). The present study investigates the drivers of Black Elephants in order to differentiate the extreme events that are not possible to mitigate directly due to their unknown nature (Black Swans) and the extreme events that could be mitigated, if given sufficient attention and resources (Black Elephants). ...
... Low-probability, high consequence (LPHC) An extreme event that is defined to have a low probability of occurrence and a high consequence outcome Cha & Ellingwood, 2012;Mignan et al., 2014 Black Swan A LPHC event that is surprising and previously unknown to the observer Taleb, 2007;Nafday, 2009;Lin et al., 2020 Black Elephant A LPHC event that is known but not broadly acknowledged, and therefore limited or no mitigating action is taken Ho 2017; Lin et al., 2021 The difference between a Black Elephant and a Black Swan is fundamentally an issue of information and initiative, rather than a difference in the risk or components of risk, as indicated by the diagram in Figure 1. As information becomes available on the Black Swan, the opportunity to coax the potential Black Swan disaster into a more manageable hazard event relies first and foremost on acknowledgment that the hazard exists, and an assertion that the current consequences from that hazard are unacceptable to populations that it will affect. ...
... The combination of these processes, drivers, and diversity of risks increases the potential consequences of Black Elephants. This table builds on climate risks described inZscheischler et al. (2018) and includes Black Elephant examples originally compiled inLin et al. (2021). ...
Asia has the fastest growing population and economy, but it is also the most disaster‐prone region in the world. Resilience to disaster impacts from natural hazards will be key to the long‐term sustainability of this rapidly growing region. The first step to building resilience is to identify the key threats that this region faces. We describe these key threats as Black Elephants: a cross between a “black swan” and the proverbial "elephant in the room" — they are extreme events that are known but difficult to address and often ignored. We examine the primary drivers of these looming risks and find that the drivers include underestimated or intensifying hazards, growing exposure, high vulnerability, and unaccounted complexities from multi‐hazard events. In mitigating these key risks, we discuss psychological barriers to action and highlight the importance of information, language, and hope. The known but complex impacts from natural hazards in Asia must be further acknowledged and managed in order to build a more sustainable, resilient future in an increasingly globally connected world.
... Specific to volcanic risk, this is the first effort to provide a large-scale, quantitative basis to estimate the impacts of explosive volcanic eruptions on food production. On a longer timescale and large spatial scale, this is the first step towards tackling the unaddressed black elephant event that is the risk of future large eruptions on food security (Lin et al., 2021). ...
Although the generally high fertility of volcanic soils is often seen as an opportunity, short-term consequences of eruptions on natural and cultivated vegetation are likely to be negative. The empirical knowledge obtained from post-event impact assessments provides crucial insights into the range of parameters controlling impact and recovery of vegetation, but their limited coverage in time and space offers a limited sample of all possible eruptive and environmental conditions. Consequently, vegetation vulnerability remains largely unconstrained, thus impeding quantitative risk analyses.
Here, we explore how cloud-based big Earth observation data, remote sensing and interpretable machine learning (ML) can provide a large-scale alternative to identify the nature of, and infer relationships between, drivers controlling vegetation impact and recovery. We present a methodology developed using Google Earth Engine to systematically revisit the impact of past eruptions and constrain critical hazard and vulnerability parameters. Its application to the impact associated with the tephra fallout from the 2011 eruption of Cordón Caulle volcano (Chile) reveals its ability to capture different impact states as a function of hazard and environmental parameters and highlights feedbacks and thresholds controlling impact and recovery of both natural and cultivated vegetation. We therefore conclude that big Earth observation (EO) data and machine learning complement existing impact datasets and open the way to a new type of dynamic and large-scale vulnerability models.
Loss outcomes from geohazards are compounded by an array of human risk factors. The combination of geohazards and human risk factors can generate multi-risk cascades. In the historical record, disasters arising from such multi-risk cascades are comparatively rare. However, far more common are near-misses, where a disaster tipping point to massive destructive energy release and expanding losses was narrowly averted. What happened historically is only one realization of what might have happened. Due to psychological outcome bias, people pay far less attention to near-misses than to actual losses. A downward counterfactual is a psychological term for a thought about the past, where things turned for the worse. Exploration of downward counterfactuals enhances risk awareness and can contribute to risk preparedness. There are no databases of multi-risk cascade near-misses, but insights can be gained from downward counterfactual analysis. Geohazard examples of multi-risk downward counterfactuals are given, including cases of critical infrastructure damage. A downward counterfactual can drive a minor hazard event beyond the disaster tipping point boundary, and turn a disaster into a major catastrophe. To illustrate the latter, a downward counterfactual analysis is presented of the Fukushima nuclear accident of 11 March 2011, which might have crossed the tipping point boundary into a multi-risk cascade catastrophe.
Although the generally high fertility of volcanic soils is often seen as an opportunity, short-term consequences of eruptions on natural and cultivated vegetation are likely to be negative. The empirical knowledge obtained from post-event impact assessments provides crucial insights into the range of parameters controlling impact and recovery of vegetation, but their limited coverage in time and space offers a limited sample of all possible eruptive and environmental conditions. Consequently, vegetation vulnerability remains largely unconstrained, thus impeding quantitative risk analyses. Here, we explore how cloud-based big Earth Observation data, remote sensing and interpretable machine learning (ML) can provide a large-scale alternative to identify the nature of, and infer relationships between, drivers controlling vegetation impact and recovery. We present a methodology developed using Google Earth Engine to systematically revisit the impact of past eruptions and constrain critical hazard and vulnerability parameters. Its application to the impact associated with the tephra fallout from the 2011 eruption of Cordón Caulle volcano (Chile) reveals its ability to capture different impact states as a function of hazard and environmental parameters and highlights feedbacks and thresholds controlling impact and recovery of both natural and cultivated vegetation. We therefore conclude that big EO data and machine learning complement existing impact datasets open the way to a new type of dynamic and large-scale vulnerability models.
In tropical cyclone (TC) regions, tide gauge or numerical hindcast records are usually of insufficient length to have sampled sufficient cyclones to enable robust estimates of the climate of TC-induced extreme water level events. Synthetically-generated TC populations provide a means to define a broader set of plausible TC events to better define the probabilities associated with extreme water level events. The challenge is to unify the estimates of extremes from synthetically-generated TC populations with the observed records, which include mainly non-TC extremes resulting from tides and more frequently occurring atmospheric-depression weather and climate events. We find that extreme water level measurements in multiple tide gauge records in TC regions, some which span more than 100 years, exhibit a behaviour consistent with the combining of two populations, TC and non-TC. We develop an equation to model the combination of two populations of extremes in a single continuous mixed climate (MC) extreme value distribution (EVD). We then run statistical simulations to show that long term records including both historical and synthetic events can be better explained using MC than heavy-tailed generalised EVDs. This has implications for estimating extreme water levels when combining synthetic cyclone extreme sea levels with hindcast water levels to provide actionable information for coastal protection.
