This paper presents a conceptual framework to define seismic resilience of communities and quantitative measures of resilience that can be useful for a coordinated research effort focusing on enhancing this resilience. This framework relies on the complementary measures of resilience: “Reduced failure probabilities,” “Reduced consequences from failures,” and “Reduced time to recovery.” The framework also includes quantitative measures of the “ends” of robustness and rapidity, and the “means” of resourcefulness and redundancy, and integrates those measures into the four dimensions of community resilience—technical, organizational, social, and economic—all of which can be used to quantify measures of resilience for various types of physical and organizational systems. Systems diagrams then establish the tasks required to achieve these objectives. This framework can be useful in future research to determine the resiliency of different units of analysis and systems, and to develop resiliency targets and detailed analytical procedures to generate these values.

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.

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This paper compares four weighting methods in multiattribute utility measurement: the ratio method, the swing weighting method, the tradeoff method and the pricing out method. 200 subjects used these methods to weight attributes for evaluating nuclear waste repository sites in the United States. The weighting methods were compared with respect to their internal consistency, convergent validity, and external validity. Internal consistency was measured by the degree to which ordinal and cardinal or ratio responses agreed within the same weighting method. Convergent validity was measured by the degree of agreement between the weights elicited with different methods. External validity was determined by the degree to which weights elicited in this experiment agreed with weights that were elicited with managers of the Department of Energy. In terms of internal consistency, the tradeoff method fared worst. In terms of convergent validity, the pricing out method turned out to be an outlier. In terms of external validity, the pricing out method showed the best results. While the ratio and swing methods are quite consistent and show a fair amount of convergent validity, their external validity problems cast doubt on their usefulness. The main recommendation for applications is to improve the internal consistency of the tradeoff method by careful interactive elicitation and to use it in conjunction with the pricing out method to enhance its external validity.tradeoffs, weights, multiattribute utility, nuclear waste disposal, decision analysis