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Despite continuing efforts to make the electric power system robust, some risk remains of widespread and extended power outages due to extreme weather or acts of terrorism. One way to alleviate the most serious effects of a prolonged blackout is to find local means to secure the continued provision of critical social services upon which the health and safety of society depend. This article outlines and estimates the incremental cost of a strategy that uses small distributed generation, distribution automation, and smart meters to keep a set of critical social services operational during a prolonged power outage that lasts for days or weeks and extends over hundreds of kilometers.

The AC electric and magnetic fields associated with high voltage power lines have become a concern as a possible health risk. In most cases the strength of these fields decreases as the inverse square of the distance from the line. In earlier work, we found that laypeople do not understand how rapidly field strength decreases with distance. Most believe that any high voltage power line they can see is exposing them to strong fields. This paper confirms the earlier finding and explores a number of strategies which might be used in risk communications to correct this misperception. We found it relatively easy to provide subjects with a better understanding of the range‐dependency of magnetic field strength. Moreover, the quality of this acquisition was apparently independent of the manner in which they were instructed. Such successful instruction is markedly different from the well‐established difficulty of teaching people about many qualitative domains, such as physics or ideas in probability. Clearly, while some erroneous beliefs are highly resistant to change, others can be altered quite readily. We suspect that an important distinction between knowledge about the range‐dependency of power‐frequency magnetic fields and less tractable topics involves the presence or absence of prior folk‐theories or "mental models" of the domain.

Nine pictorial displays for communicating quantitative information about the value of an uncertain quantity, x, were evaluated for their ability to communicate x̄, p(x > a) and p(b > x > a) to well‐educated semi‐and nontechnical subjects. Different displays performed best in different applications. Cumulative distribution functions alone can severely mislead some subjects in estimating the mean. A "rusty" knowledge of statistics did not improve performance, and even people with a good basic knowledge of statistics did not perform as well as one would like. Until further experiments are performed, the authors recommend the use of a cumulative distribution function plotted directly above a probability density function with the same horizontal scale, and with the location of the mean clearly marked on both curves.

The use of appropriately designed computer aids for policy could improve the standards of risk analysis and other quantitative policy analysis in several important ways. They could make it easier to treat uncertainties more thoroughly and systematically than is now typical. To do this, they should provide a broad variety of techniques for representing uncertain quantities as ranges of alternative values or as probability distributions, for propagating uncertainties through a model, for analyzing and comparing the impacts of different sources of uncertainty, and for displaying results in various numerical and graphic formats. A nonprocedural modeling language allowing interactive editing of input values and model structure could encourage exploration and progressive refinement of models, and comparison of alternative formulations. The integration of model documentation and explanatory text within the computer representation could encourage maintenance of consistency between different versions of the mathematical structure and their descriptions. It could also allow interactive scrutiny of the model assumptions and sensitivities by outside reviewers. We describe a particular system, Demos, designed to provide these facilities and test their usefulness. The use of Demos is illustrated by an analysis of the risks and optimal control level for a hypothetical air pollutant, with uncertainty about the population exposure, health effects, and control costs. This example demonstrates progressive refinement of a model, and various kinds of parametric and probabilistic uncertainty analysis. Demos is now being used by a growing number of risk analysts, students, and policy researchers in a wide variety of tasks.