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AbstractResearch and development (R&D) planners in homeland security agencies would like to be able to prioritize investments in projects based on costs versus future safety and security benefits. While costs are often readily available, estimates of safety and security benefits are fraught with uncertainty. To address these challenges, a benefit–cost model of technological change is adapted to the homeland security context. Data are sparse; therefore, estimation is facilitated by developing a familiar linear welfare model using derivatives of cost and risk reduction functions to estimate areas of costs and benefits. The theoretical model is applied to two homeland security projects involving airport patrols and the assignment of U.S. federal air marshals to international flights. Retrospective data are available for most periods. Welfare-based rates of return are reported for the two cases, each of which is estimated to return large present value net benefits. Extensive sensitivity and Monte Carlo simulation explores uncertainties. Two important findings are that (i) given the rationality assumption, relative increases in security levels can be valued, even if the absolute level of security is not known; and (ii) large uncertainties about risk reduction exist but can be bounded by parametric sensitivity and uncertainty analysis.

Behavioral decision research has demonstrated that judgments and decisions of ordinary people and experts are subject to numerous biases. Decision and risk analysis were designed to improve judgments and decisions and to overcome many of these biases. However, when eliciting model components and parameters from decisionmakers or experts, analysts often face the very biases they are trying to help overcome. When these inputs are biased they can seriously reduce the quality of the model and resulting analysis. Some of these biases are due to faulty cognitive processes; some are due to motivations for preferred analysis outcomes. This article identifies the cognitive and motivational biases that are relevant for decision and risk analysis because they can distort analysis inputs and are difficult to correct. We also review and provide guidance about the existing debiasing techniques to overcome these biases. In addition, we describe some biases that are less relevant because they can be corrected by using logic or decomposing the elicitation task. We conclude the article with an agenda for future research.

This article describes an application of a value-of-information (VOI) analysis to a problem involving information collection and sampling costs of millions of dollars and ultimate consequences of decisions ranging in the hundreds of millions of dollars. The context of this VOI analysis was the determination of the chemical and radiological composition of nuclear waste in 177 storage tanks at the Department of Energy's (DOE) Hanford Site in southeastern Washington State. These tanks include different types of waste and knowing their composition affects important decisions about how to treat these wastes for purposes of processing, stabilization, and ultimate storage. This analysis was conducted as part of a larger "proof-in-principle" study of how VOI concepts could be used to improve the collection of data for the purpose of identifying the composition of nuclear tank wastes and for subsequent decisions about tank-waste management. The analysis showed that a VOI analysis can be very useful and it can also provide specific guidance on the problem at hand. In particular, the analysis showed that it was worth while to collect additional information for only four of 15 tanks in question and eventually, the DOE decided to act on these tanks without the additional information. At the same time the DOE decided to focus on another set of tanks, which involved more imminent risks of flammable gas explosions and focused its effort on collecting information and stabilizing these tanks.

The regulation and management of hazardous industrial activities increasingly rely on formal expert judgment processes to provide wisdom in areas of science and technology where traditional "good science" is, in practice, unable to supply unambiguous "facts." Expert judgment has always played a significant, if often unrecognized, role in analysis; however, recent trends are to make it formal, explicit, and documented so it can be identified and reviewed by others. We propose four categories of expert judgment and present three case studies which illustrate some of the pitfalls commonly encountered in its use. We conclude that there will be an expanding policy role for formal expert judgment and that the openness, transparency, and documentation that it requires have implications for enhanced public involvement in scientific and technical affairs.

Both the issues inherent in regulation of specific risks and the contexts in which such regulatory processes occur are often characterized by confusion and controversy. Tools based on multiattribute utility measurement (MAUT) can help to clarify public values in risk debates and thus to facilitate option invention and decision making. Stakeholder group representatives, in interaction with an analyst, structure their values relevant to the problem into a value tree. The analyst prepares a common tree, iterating until all stakeholder representatives accept it. Stakeholders express their values as weights on the common tree. This provides a basis for option invention and negotiation. The paper presents three illustrative applications.

AbstractThis article describes a methodology for a risk-informed benefit–cost analysis that includes (i) risk analysis to quantify risk reduction benefits and (ii) uncertainty analyses to quantify probability distributions over costs and benefits. It also summarizes the lessons from 25 applications of this methodology to evaluate R&D projects of the Science and Technology Directorate of the Department of Homeland Security. The article then illustrates the methodology with a specific application to evaluate the benefits and costs of the Advanced Personal Protection System (APPS), a new garment system developed to protect wildland firefighters. The goals of the APPS project were to reduce risk and to improve comfort. The cost analysis revealed that the APPS garments are more expensive by about $279 per garment system. Total costs were roughly $7.3 million, including the upfront project cost and the increased 5 year cost of purchasing the APPS. Benefits from reduced injuries and fatalities resulted in 5 year benefits of about $19.3 million, with an NPV of $13.6 million in 2019 dollars. In the base case, the benefit–cost ratio was 2.87 and the return on investment was 187 % over 5 years. Taking the perspective of a decision-maker when the project was first funded in 2011, NPVs are $11,993,728, $10,025,519, and $7,967,479 in 2011 dollars for discount rates of 0, 3, and 7 % respectively. An uncertainty analysis of the NPV showed a large variability, ranging from the 5th percentile of $6.4 million to a median of $19.3 million to the 95th percentile of $43.7 million in 2019 dollars. This large range was primarily due to the uncertainty about the reduction of fatality and injury risks and the market penetration rates of the new garments.

To develop effective counterterrorism strategies, it is important to understand the capabilities and objectives of terrorist groups. Much of the understanding of these groups comes from intelligence collection and analysis of their capabilities. In contrast, the objectives of terrorists are less well understood. In this article, we describe a decision analysis methodology to identify and structure the objectives of terrorists based on the statements and writings of their leaders. This methodology was applied in three case studies, resulting in the three objectives hierarchies of al-Qaeda, Islamic State of Iraq and the Levant (ISIL), and Hezbollah. In this article, we propose a method to compare the three objectives hierarchies, highlight their key differences, and draw conclusions about effective counterterrorism strategies. We find that all three terrorist groups have a wide range of objectives going far beyond the objective of killing and terrorizing people in the non-Muslim world. Among the shared objectives are destroying Israel and expelling Western powers from the Middle East. All three groups share the ambition to become a leader in the Islamic world. Key distinctions are the territorial ambitions of ISIL and Hezbollah versus the large-scale attack objectives of al-Qaeda. Objectives specific to ISIL are the establishment of a caliphate in Iraq and Syria and the re-creation of the power of Sunni Islam. Hezbollah has unique objectives related to the establishment of a Palestine State and to maintain the relationship with and support of Iran and Syria. Al-Qaeda's objectives remain focused on large-scale attacks in the West. We also note a recent shift to provide support for small-scale attacks in the West by both al-Qaeda and ISIL. Our method can be used for comparing objectives hierarchies of different organizations as well as for comparing objectives hierarchies over time of one organization.

One of the most challenging tasks of homeland security policymakers is to allocate their limited resources to reduce terrorism risks cost effectively. To accomplish this task, it is useful to develop a comprehensive set of homeland security objectives, metrics to measure each objective, a utility function, and value tradeoffs relevant for making homeland security investments. Together, these elements form a homeland security value model. This article develops a homeland security value model based on literature reviews, a survey, and experience with building value models. The purposes of the article are to motivate the use of a value model for homeland security decision making and to illustrate its use to assess terrorism risks, assess the benefits of countermeasures, and develop a severity index for terrorism attacks.