On “Black Swans” and “Perfect Storms”: Risk Analysis and Management When Statistics Are Not Enough

Two images, “black swans” and “perfect storms,” have struck the public's imagination and are used—at times indiscriminately—to describe the unthinkable or the extremely unlikely. These metaphors have been used as excuses to wait for an accident to happen before taking risk management measures, both in industry and government. These two images represent two distinct types of uncertainties (epistemic and aleatory). Existing statistics are often insufficient to support risk management because the sample may be too small and the system may have changed. Rationality as defined by the von Neumann axioms leads to a combination of both types of uncertainties into a single probability measure—Bayesian probability—and accounts only for risk aversion. Yet, the decisionmaker may also want to be ambiguity averse. This article presents an engineering risk analysis perspective on the problem, using all available information in support of proactive risk management decisions and considering both types of uncertainty. These measures involve monitoring of signals, precursors, and near‐misses, as well as reinforcement of the system and a thoughtful response strategy. It also involves careful examination of organizational factors such as the incentive system, which shape human performance and affect the risk of errors. In all cases, including rare events, risk quantification does not allow “prediction” of accidents and catastrophes. Instead, it is meant to support effective risk management rather than simply reacting to the latest events and headlines.     

Context,Cultural Bias,and Health Risk Perception: The “Everyday” Nature of Pesticide Policy Preferences in London,Calgary, and Halifax

Risk perception and the cultural theory of risk have often been contrasted in relation to risk‐related policy making; however, the local context in which risks are experienced, an important component of everyday decision making, remains understudied. What is unclear is the extent to which localized community beliefs and behaviors depend on larger belief systems about risk (i.e., worldviews). This article reports on a study designed to understand the relative importance of health risk perceptions (threat of harm); risk‐related worldviews (cultural biases); and the experiences of local context (situated risk) for predicting risk‐related policy preferences regarding cosmetic pesticides. Responses to a random telephone questionnaire are used to compare residents’ risk perceptions, cultural biases, and pesticide bylaw preferences in Calgary (Alberta), Halifax (Nova Scotia), and London (Ontario), Canada. Logistic regression shows that the most important determinants of pesticide bylaw preference are risk perception, lack of benefit, and pesticide “abstinence.” Though perception of health risk is the best single predictor of differences in bylaw preferences, social factors such as gender and situated risk factors like conflict over chemical pesticides, are also important. Though cultural biases are not important predictors of pesticide bylaw preference, as in other studies, they are significant predictors of health risk perception. Pesticide bylaw preference is therefore more than just a health risk perception or worldview issue; it is also about how health risk becomes situated—contextually—in the experiences of residents’ everyday lives.     

Risk Analysis: Understanding “How Safe is Safe Enough?“

The basic characteristics of determining acceptable risk are discussed. Technical, political, and social aspects of the problem add much complexity. The appropriate manner to reach responsible decisions regarding acceptable risk is suggested. This explicitly addresses the alternatives, the objectives, the uncertainty, and the values which constitute the information necessary to arrive at any solution. The inappropriateness of many "solutions" currently in use or "suggested" is exposed.     

Media,Risk, and Absence of Blame for “Acts of God”: Attenuation of the European Volcanic Ash Cloud of 2010

This article analyzes the character, extent, and patterns of media coverage of the 2010 volcanic ash cloud, comparing it with coverage of other major natural hazards, such as Deepwater Horizon. It does so drawing upon sociological themes and concludes that the ash cloud was reported largely in its own terms rather than being amplified as a wider, uncertain threat. As well as the absence of major incident and casualties two interrelated factors are highlighted to explain this result. Emphasizing the importance of hazard duration, the unexpected arrival and short‐lived character of the ash cloud was one important factor that limited the potential for sustained media amplification. More broadly, this was an “act of God” with no clear responsible agents. This preliminary study suggests that contemporary media risk narrative requires a focus for institutional blame attribution, and without a plausible candidate amplification may not acquire momentum.     

Some Limitations of “Risk = Threat × Vulnerability × Consequence” for Risk Analysis of Terrorist Attacks

Several important risk analysis methods now used in setting priorities for protecting U.S. infrastructures against terrorist attacks are based on the formula: Risk=Threat×Vulnerability×Consequence. This article identifies potential limitations in such methods that can undermine their ability to guide resource allocations to effectively optimize risk reductions. After considering specific examples for the Risk Analysis and Management for Critical Asset Protection (RAMCAP?) framework used by the Department of Homeland Security, we address more fundamental limitations of the product formula. These include its failure to adjust for correlations among its components, nonadditivity of risks estimated using the formula, inability to use risk‐scoring results to optimally allocate defensive resources, and intrinsic subjectivity and ambiguity of Threat, Vulnerability, and Consequence numbers. Trying to directly assess probabilities for the actions of intelligent antagonists instead of modeling how they adaptively pursue their goals in light of available information and experience can produce ambiguous or mistaken risk estimates. Recent work demonstrates that two‐level (or few‐level) hierarchical optimization models can provide a useful alternative to Risk=Threat×Vulnerability×Consequence scoring rules, and also to probabilistic risk assessment (PRA) techniques that ignore rational planning and adaptation. In such two‐level optimization models, defender predicts attacker's best response to defender's own actions, and then chooses his or her own actions taking into account these best responses. Such models appear valuable as practical approaches to antiterrorism risk analysis.