The science of estimating the chances of specific extreme events occurring and their potential consequences originates in the field of property insurance and the science of natural hazards. In the 1800s, residential insurers managed their risk by "mapping" the structures that they covered, pinning tacks onto a wall map to display the degree of physical concentration of exposure. Although crude, the technique served insurers well at the time and limited their risk. Widespread usage of such "mapping" ended in the 1960s when it finally became too cumbersome and time-consuming to execute. Now, Geographic Information Systems (GIS) software and other digital products achieve the same with much more extensive data and sophisticated technologies.1
Whatever the risk-assessment process method, four basic elements for assessing risk remain the same: hazard, inventory, vulnerability, and loss (see Figure 1.1). The first element focuses on the risk of a hazard. For example, an earthquake hazard is characterized by its likely epicenter location and magnitude, along with other significant parameters. A hurricane is distinguished by its projected path and wind speed. One could also describe the hazard associated with terrorism or a pandemic by characterizing the target of a violent attack or the spread rate of a potentially catastrophic disease such as swine flu or severe acute respiratory syndrome (SARS). The hazard can also be usefully characterized as a range of potential scenarios. For example, what is the likelihood that a hurricane of magnitude 3, 4, or 5 on the Saffir-Simpson scale might strike the Miami, Florida, area in 2010?
Figure 1.1 Elements of the risk-assessment process model
The risk-assessment process model's second element identifies the inventory of properties, humans, and the physical environment at risk. To fully inventory structures, for instance, requires evaluation of their location, physical dimensions, and construction quality. Taken together, the hazard and inventory elements enable calculation of the model's third element, the damage vulnerability of the structures or people at risk. And from the measure of vulnerability, the human and property loss, the fourth element, can be evaluated.
In working with catastrophes in this model, it is also useful to distinguish between direct and indirect losses. Direct losses include injuries, fatalities, financial losses, and the cost to repair or replace a structure, restore a service, or rescue a company. Indirect losses include future foregone income, slower growth, and other longer-term consequences of evacuation costs, disrupted schooling, and company bankruptcies.
Scientists and engineers develop reasonably accurate models for assessing risk with this model and specifying the degree of uncertainty in each of the components. In doing so, analysts take special care to minimize the role of subjective assessments and personal biases in building their estimates. But because such factors still sometimes intrude, it is not uncommon for the public to learn from one expert that there is little about which to be concerned related to a given risk, and from another expert that the alarm bells should be sounding.
Not surprisingly, the public responds in disparate ways to the added uncertainty resulting from conflicting expert forecasts. Some may simply decide to ignore the expert judgments. Others may be drawn to the expert prediction most compatible with the individual's own predispositions. Still others may seek out a host of expert opinions and then draw independent assessments of where the preponderance of informed forecasts are pointing.
Consider the uncertainties inherent in the following natural and unnatural disasters:
- What are the chances that Tokyo will experience an earthquake of magnitude 7 or greater next year, and what will be the resulting property damage, human loss, and interruption of commerce in Japan, East Asia, and beyond?
- What is the prospect of a major terrorist attack in Europe, and what would be the resulting human casualties and economic impacts?
- What is the probability of an African pandemic in the next five years, what type of disease is most likely to spread, where will it start, and how soon will it reach other continents?
- What is the probability that 5 of the 20 largest financial institutions worldwide will fail within the next 24 months and either go bankrupt, as did Lehman Brothers, or enter government receivership, as in the case of the Royal Bank of Scotland?
- What is the chance that the top ten insurance companies and commercial banks will have their credit rating dropped four tiers—say from AAA (almost no credit risk) to A1 or A+ (safe unless unforeseen events arise)—in the coming year?
When expert analysts attempt to answer these questions, they usually ask for more precise information to define the event for their model. Take the question related to the chances of an earthquake of magnitude 7 or greater in Tokyo next year. Experts will want to know how to define Tokyo (the city proper or the entire metropolitan region), whether next year means the calendar or fiscal year, and what should be included among the indirect losses. Because experts often take variant responses to these kinds of questions into account, divergent forecasts for even relatively specific events can leave people and their leaders unclear whether and how to prepare and respond.
For many years, the focus of hazard-loss estimation for natural disasters had been largely confined to property damage and loss of life. And estimations were generally limited to the immediate period of the disaster, just hours or days after the earth shook or floodwaters peaked. Now, risk-assessment models are incorporating longer time periods extending to weeks and even months, and to more diverse measures, such as disrupted commercial flows or post-traumatic stress disorders. As experts have expanded the time periods and range of losses in their models, risk assessment has become much more complex and forecasts are likely to be fraught with uncertainty. That, in turn, has added to public and leadership hesitation on how best to prepare for and react to disasters.