50 research outputs found

    NASA's Understanding of Risk in Apollo and Shuttle

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    Mathematical risk analysis was used in Apollo, but it gave unacceptably pessimistic resultsand was discontinued. Shuttle was designed without using risk analysis, under the assumptionthat good engineering would make it very safe. This approach led to an unnecessarily riskydesign, which directly led to the Shuttle tragedies. Although the Challenger disaster wasdirectly due to a mistaken launch decision, it might have been avoided by a safer design. Theultimate cause of the Shuttle tragedies was the Apollo era decision to abandon risk analysis

    Adaptive Management and the Value of Information: Learning Via Intervention in Epidemiology

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    Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding

    Significance testing as perverse probabilistic reasoning

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    Truth claims in the medical literature rely heavily on statistical significance testing. Unfortunately, most physicians misunderstand the underlying probabilistic logic of significance tests and consequently often misinterpret their results. This near-universal misunderstanding is highlighted by means of a simple quiz which we administered to 246 physicians at two major academic hospitals, on which the proportion of incorrect responses exceeded 90%. A solid understanding of the fundamental concepts of probability theory is becoming essential to the rational interpretation of medical information. This essay provides a technically sound review of these concepts that is accessible to a medical audience. We also briefly review the debate in the cognitive sciences regarding physicians' aptitude for probabilistic inference

    Mind the gap: The role of mindfulness in adapting to increasing risk and climate change

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    A framework to assess quality and uncertainty in disaster loss data

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    There is a growing interest in the systematic and consistent collection of disasterloss data for different applications. Therefore, the collected data must follow a set oftechnical requirements to guarantee its usefulness. One of those requirements is theavailability of a measure of the uncertainty in the collected data to express its quality for agiven purpose. Many of the existing disaster loss databases do not provide such uncertainty/qualitymeasures due to the lack of a simple and consistent approach to expressuncertainty. After reviewing existing literature on the subject, a framework to express theuncertainty in disaster loss data is proposed. This framework builds on an existinguncertainty classification that was updated and combined with an existing method for datacharacterization. The proposed approach is able to establish a global score that reflects theoverall uncertainty in a certain loss indicator and provides a measure of its quality

    Evaluation of Risks in Complex Problems

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    Enabling Accountable Collaboration in Distributed, Autonomous Systems by Intelligent Agents

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    High degree of distribution is one of the leading features in present computer systems. Cloud Computing, Internet of Things and Blockchains are nowadays very hot research topics and we are going to use these architectures in many critical domains, like e-health, conservation of documents and acts in accordance with the law, economic transactions and contracts etc. When dealing with such heterogeneous systems, it is really hard to understand if a distributed collaboration among agents fulfils requirements, rules and current laws. In addition, if something goes wrong during collaboration, assigning accountability is even more complicated. This work aims at introducing a novel methodology and a formal framework able to attribute liability of failures or incorrect design and implementation both to humans and software agents in autonomous, distributed systems
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