A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

Estimation of Error and Bias in Bayesian Monte Carlo Decision Analysis Using the Bootstrap

Bayesian Monte Carlo (BMC) decision analysis adopts a sampling procedure to estimate likelihoods and distributions of outcomes, and then uses that information to calculate the expected performance of alternative strategies, the value of information, and the value of including uncertainty. These decision analysis outputs are therefore subject to sample error. The standard error of each estimate and its bias, if any, can be estimated by the bootstrap procedure. The bootstrap operates by resampling (with replacement) from the original BMC sample, and redoing the decision analysis. Repeating this procedure yields a distribution of decision analysis outputs. The bootstrap approach to estimating the effect of sample error upon BMC analysis is illustrated with a simple value-of-information calculation along with an analysis of a proposed control structure for Lake Erie. The examples show that the outputs of BMC decision analysis can have high levels of sample error and bias.     

Federal budget helps drive health policy. A close-up look at the budget process and how it affects health care funding

Crafting the budget is an annual process that Congress and the administration use to define the national policy agenda. It is a massive undertaking to decide how more than two trillion dollars is spent by the federal government. Over 20 percent of the budget is used for health care. The budget is a political document that defines the priorities of the federal government and directly influences overall health policy. Its construction deserves to be followed and influenced by physicians to ensure the health of the public.     

Risk–risk governance in a low-carbon future: Exploring institutional,technological, and behavioral tradeoffs in climate geoengineering pathways

Deliberations are underway to utilize increasingly radical technological options to help address climate change and stabilize the climatic system. Collectively, these options are often referred to as “climate geoengineering.” Deployment of such options, however, can create wicked tradeoffs in governance and require adaptive forms of risk management. In this study, we utilize a large and novel set of qualitative expert interview data to more deeply and systematically explore the types of risk–risk tradeoffs that may emerge from the use of 20 different climate geoengineering options, 10 that focus on carbon dioxide or greenhouse gas removal, and 10 that focus on solar radiation management and reflecting sunlight. We specifically consider: What risks does the deployment of these options entail? What types of tradeoffs may emerge through their deployment? We apply a framework that clusters risk–risk tradeoffs into institutional and governance, technological and environmental, and behavioral and temporal dimensions. In doing so, we offer a more complete inventory of risk–risk tradeoffs than those currently available within the respective risk-assessment, energy-systems, and climate-change literatures, and we also point the way toward future research gaps concerning policy, deployment, and risk management.     

Parametric Modeling for Survival with Competing Risks and Masked Failure Causes

We consider a life testing situation in which systems are subject to failure from independent competing risks. Following a failure, immediate (stage-1) procedures are used in an attempt to reach a definitive diagnosis. If these procedures fail to result in a diagnosis, this phenomenon is called masking. Stage-2 procedures, such as failure analysis or autopsy, provide definitive diagnosis for a sample of the masked cases. We show how stage-1 and stage-2 information can be combined to provide statistical inference about (a) survival functions of the individual risks, (b) the proportions of failures associated with individual risks and (c) probability, for a specified masked case, that each of the masked competing risks is responsible for the failure. Our development is based on parametric distributional assumptions and the special case for which the failure times for the competing risks have a Weibull distribution is discussed in detail.