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This article discusses the use of quantitative risk assessment (QRA) in decision making regarding the safety of complex technological systems. The insights gained by QRA are compared with those from traditional safety methods and it is argued that the two approaches complement each other. It is argued that peer review is an essential part of the QRA process. The importance of risk‐informed rather than risk‐based decision making is emphasized. Engineering insights derived from QRAs are always used in combination with traditional safety requirements and it is in this context that they should be reviewed and critiqued. Examples from applications in nuclear power, space systems, and an incinerator of chemical agents are given to demonstrate the practical benefits of QRA. Finally, several common criticisms raised against QRA are addressed.

The extreme importance of critical infrastructures to modern society is widely recognized. These infrastructures are complex and interdependent. Protecting the critical infrastructures from terrorism presents an enormous challenge. Recognizing that society cannot afford the costs associated with absolute protection, it is necessary to identify and prioritize the vulnerabilities in these infrastructures. This article presents a methodology for the identification and prioritization of vulnerabilities in infrastructures. We model the infrastructures as interconnected digraphs and employ graph theory to identify the candidate vulnerable scenarios. These scenarios are screened for the susceptibility of their elements to a terrorist attack, and a prioritized list of vulnerabilities is produced. The prioritization methodology is based on multiattribute utility theory. The impact of losing infrastructure services is evaluated using a value tree that reflects the perceptions and values of the decisionmaker and the relevant stakeholders. These results, which are conditional on a specified threat, are provided to the decisionmaker for use in risk management. The methodology is illustrated through the presentation of a portion of the analysis conducted on the campus of the Massachusetts Institute of Technology.

The National Research Council has recommended the use of an analytic/deliberative decision making process in environmental restoration decisions that involve multiple stakeholders. This work investigates the use of the results of risk assessment and multiattribute utility analysis (the "analysis") in guiding the deliberation. These results include the ranking of proposed remedial action alternatives according to each stakeholder's preferences, as well as the identification of the major reasons for these rankings. The stakeholder preferences are over a number of performance measures that include the traditional risk assessment metrics, e.g., individual worker risk, as well as programmatic, cultural, and cost‐related impacts. Based on these results, a number of proposals are prepared for consideration by the stakeholders during the deliberation. These proposals are the starting point for the formulation of actual recommendations by the group. In our case study, these recommendations included new remedial action alternatives that were created by the stakeholders after an extensive discussion of the detailed analytical results.

Natural hazards, human‐induced accidents, and malicious acts have caused great losses and disruptions to society. After September 11, 2001, critical infrastructure protection has become a national focus in the United States and is likely to remain one for the foreseeable future. Damage to the infrastructures and assets could be mitigated through predisaster planning and actions. A systematic methodology was developed to assess and rank the risks from these multiple hazards in a community of 20,000 people. It is an interdisciplinary study that includes probabilistic risk assessment (PRA), decision analysis, and expert judgment. Scenarios are constructed to show how the initiating events evolve into undesirable consequences. A value tree, based on multi‐attribute utility theory (MAUT), is used to capture the decisionmaker's preferences about the impacts on the infrastructures and other assets. The risks from random failures are ranked according to their expected performance index (PI), which is the product of frequency, probabilities, and consequences of a scenario. Risks from malicious acts are ranked according to their PI as the frequency of attack is not available. A deliberative process is used to capture the factors that could not be addressed in the analysis and to scrutinize the results. This methodology provides a framework for the development of a risk‐informed decision strategy. Although this study uses the Massachusetts Institute of Technology campus as a case study of a real project, it is a general methodology that could be used by other similar communities and municipalities.