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  • Articles  (79)
  • uncertainty  (79)
  • Economics  (53)
  • Energy, Environment Protection, Nuclear Power Engineering  (39)
  • 1
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Radiation Physics and Chemistry 42 (1993), S. 731-738 
    ISSN: 0969-806X
    Keywords: Dosimetry ; radiation ; standards ; sterilization ; traceability ; uncertainty
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Chemistry and Pharmacology , Energy, Environment Protection, Nuclear Power Engineering , Physics
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Environmental Pollution 83 (1994), S. 87-93 
    ISSN: 0269-7491
    Keywords: artificial intelligence ; climate change ; modelling ; potato ; uncertainty
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 1539-6924
    Keywords: compliance certification application ; engineering analysis ; geochemistry ; geohydrology ; performance assessment ; probabilistic systems analysis ; radioactive waste ; scientific validity ; uncertainty ; 40 CFR 191
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract Performance Assessment (PA) is the use of mathematical models to simulate the long-term behavior of engineered and geologic barriers in a nuclear waste repository; methods of uncertainty analysis are used to assess effects of parametric and conceptual uncertainties associated with the model system upon the uncertainty in outcomes of the simulation. PA is required by the U.S. Environmental Protection Agency as part of its certification process for geologic repositories for nuclear waste. This paper is a dialogue to explore the value and limitations of PA. Two “skeptics” acknowledge the utility of PA in organizing the scientific investigations that are necessary for confident siting and licensing of a repository; however, they maintain that the PA process, at least as it is currently implemented, is an essentially unscientific process with shortcomings that may provide results of limited use in evaluating actual effects on public health and safety. Conceptual uncertainties in a PA analysis can be so great that results can be confidently applied only over short time ranges, the antithesis of the purpose behind long-term, geologic disposal. Two “proponents” of PA agree that performance assessment is unscientific, but only in the sense that PA is an engineering analysis that uses existing scientific knowledge to support public policy decisions, rather than an investigation intended to increase fundamental knowledge of nature; PA has different goals and constraints than a typical scientific study. The “proponents” describe an ideal, six-step process for conducting generalized PA, here called probabilistic systems analysis (PSA); they note that virtually all scientific content of a PA is introduced during the model-building steps of a PSA; they contend that a PA based on simple but scientifically acceptable mathematical models can provide useful and objective input to regulatory decision makers. The value of the results of any PA must lie between these two views and will depend on the level of knowledge of the site, the degree to which models capture actual physical and chemical processes, the time over which extrapolations are made, and the proper evaluation of health risks attending implementation of the repository. The challenge is in evaluating whether the quality of the PA matches the needs of decision makers charged with protecting the health and safety of the public.
    Type of Medium: Electronic Resource
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  • 4
    ISSN: 1539-6924
    Keywords: risk assessment ; uncertainty ; formaldehyde ; decision analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract A call for risk assessment approaches that better characterize and quantify uncertainty has been made by the scientific and regulatory community. This paper responds to that call by demonstrating a distributional approach that draws upon human data to derive potency estimates and to identify and quantify important sources of uncertainty. The approach is rooted in the science of decision analysis and employs an influence diagram, a decision tree, probabilistic weights, and a distribution of point estimates of carcinogenic potency. Its results estimate the likelihood of different carcinogenic risks (potencies) for a chemical under a specific scenario. For this exercise, human data on formaldehyde were employed to demonstrate the approach. Sensitivity analyses were performed to determine the relative impact of specific levels and alternatives on the potency distribution. The resulting potency estimates are compared with the results of an exercise using animal data on formaldehyde. The paper demonstrates that distributional risk assessment is readily adapted to situations in which epidemiologic data serve as the basis for potency estimates. Strengths and weaknesses of the distributional approach are discussed. Areas for further application and research are recommended.
    Type of Medium: Electronic Resource
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Risk analysis 19 (1999), S. 585-598 
    ISSN: 1539-6924
    Keywords: uncertainty ; threatened plants ; risk ; conservation ; rule sets ; IUCN
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract Australian state and federal agencies use a broad range of methods for setting conservation priorities for species at risk. Some of these are based on rule sets developed by the International Union for the Conservation of Nature, while others use point scoring protocols to assess threat. All of them ignore uncertainty in the data. In this study, we assessed the conservation status of 29 threatened vascular plants from Tasmania and New South Wales using a variety of methods including point scoring and rule-based approaches. In addition, several methods for dealing with uncertainty in the data were applied to each of the priority-setting schemes. The results indicate that the choice of a protocol for setting priorities and the choice of the way in which uncertainty is treated may make important differences to the resulting assessments of risk. The choice among methods needs to be rationalized within the management context in which it is to be applied. These methods are not a substitute for more formal risk assessment.
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  • 6
    ISSN: 1539-6924
    Keywords: Variability ; uncertainty ; maximum likelihood ; bootstrap simulation ; Monte Carlo simulation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract Variability arises due to differences in the value of a quantity among different members of a population. Uncertainty arises due to lack of knowledge regarding the true value of a quantity for a given member of a population. We describe and evaluate two methods for quantifying both variability and uncertainty. These methods, bootstrap simulation and a likelihood-based method, are applied to three datasets. The datasets include a synthetic sample of 19 values from a Lognormal distribution, a sample of nine values obtained from measurements of the PCB concentration in leafy produce, and a sample of five values for the partitioning of chromium in the flue gas desulfurization system of coal-fired power plants. For each of these datasets, we employ the two methods to characterize uncertainty in the arithmetic mean and standard deviation, cumulative distribution functions based upon fitted parametric distributions, the 95th percentile of variability, and the 63rd percentile of uncertainty for the 81st percentile of variability. The latter is intended to show that it is possible to describe any point within the uncertain frequency distribution by specifying an uncertainty percentile and a variability percentile. Using the bootstrap method, we compare results based upon use of the method of matching moments and the method of maximum likelihood for fitting distributions to data. Our results indicate that with only 5–19 data points as in the datasets we have evaluated, there is substantial uncertainty based upon random sampling error. Both the boostrap and likelihood-based approaches yield comparable uncertainty estimates in most cases.
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Risk analysis 19 (1999), S. 1193-1204 
    ISSN: 1539-6924
    Keywords: multimedia modeling ; uncertainty ; variability ; exposure efficiency ; toxicity scoring ; toxics release inventory (TRI) ; life cycle assessment (LCA)
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract The human toxicity potential, a weighting scheme used to evaluate toxic emissions for life cycle assessment and toxics release inventories, is based on potential dose calculations and toxicity factors. This paper evaluates the variance in potential dose calculations that can be attributed to the uncertainty in chemical-specific input parameters as well as the variability in exposure factors and landscape parameters. A knowledge of the uncertainty allows us to assess the robustness of a decision based on the toxicity potential; a knowledge of the sources of uncertainty allows us to focus our resources if we want to reduce the uncertainty. The potential dose of 236 chemicals was assessed. The chemicals were grouped by dominant exposure route, and a Monte Carlo analysis was conducted for one representative chemical in each group. The variance is typically one to two orders of magnitude. For comparison, the point estimates in potential dose for 236 chemicals span ten orders of magnitude. Most of the variance in the potential dose is due to chemical-specific input parameters, especially half-lives, although exposure factors such as fish intake and the source of drinking water can be important for chemicals whose dominant exposure is through indirect routes. Landscape characteristics are generally of minor importance.
    Type of Medium: Electronic Resource
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  • 8
    Electronic Resource
    Electronic Resource
    Springer
    Risk analysis 19 (1999), S. 995-1002 
    ISSN: 1539-6924
    Keywords: conditional ; uncertainty ; probability ; intervals ; risk analysis ; conservatism ; Waste Isolation Pilot Plant
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract Uncertainty analyses and the reporting of their results can be misinterpreted when these analyses are conditional on a set of assumptions generally intended to bring some conservatism in the decisions. In this paper, two cases of conditional uncertainty analysis are examined. The first case includes studies that result, for instance, in a family of risk curves representing percentiles of the probability distribution of the future frequency of exceeding specified consequence levels conditional on a set of hypotheses. The second case involves analyses that result in an interval of outcomes estimated on the basis of conservative assumptions. Both types of results are difficult to use because they are sometimes misinterpreted as if they represented the output of a full uncertainty analysis. In the first case, the percentiles shown on each risk curve may be taken at face value when in reality (in marginal terms) they are lower if the chosen hypotheses are conservative. In the second case, the fact that some segments of the resulting interval are highly unlikely—or that some more benign segments outside the range of results are quite possible—does not appear. Also, these results are difficult to compare to those of analyses of other risks, possibly competing for the same risk management resources, and the decision criteria have to be adapted to the conservatism of the hypotheses. In this paper, the focus is on the first type (conditional risk curves) more than on the second and the discussion is illustrated by the case of the performance assessment of the Waste Isolation Pilot Plant in New Mexico. For policy-making purposes, however, the problems of interpretation, comparison, and use of the results are similar.
    Type of Medium: Electronic Resource
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  • 9
    Electronic Resource
    Electronic Resource
    Springer
    Risk analysis 19 (1999), S. 135-152 
    ISSN: 1539-6924
    Keywords: Probability ; uncertainty ; data ; risk assessment
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract Risk assessors attempting to use probabilistic approaches to describe uncertainty often find themselves in a data-sparse situation: available data are only partially relevant to the parameter of interest, so one needs to adjust empirical distributions, use explicit judgmental distributions, or collect new data. In determining whether or not to collect additional data, whether by measurement or by elicitation of experts, it is useful to consider the expected value of the additional information. The expected value of information depends on the prior distribution used to represent current information; if the prior distribution is too narrow, in many risk-analytic cases the calculated expected value of information will be biased downward. The well-documented tendency toward overconfidence, including the neglect of potential surprise, suggests this bias may be substantial. We examine the expected value of information, including the role of surprise, test for bias in estimating the expected value of information, and suggest procedures to guard against overconfidence and underestimation of the expected value of information when developing prior distributions and when combining distributions obtained from multiple experts. The methods are illustrated with applications to potential carcinogens in food, commercial energy demand, and global climate change.
    Type of Medium: Electronic Resource
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  • 10
    Electronic Resource
    Electronic Resource
    Springer
    The Geneva risk and insurance review 21 (1996), S. 179-189 
    ISSN: 1554-9658
    Keywords: uncertainty ; investment ; newsboy problem ; increase in risk ; optimal capacity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Economics
    Notes: Abstract In this article we study the effect of uncertainty on an entrepreneur who must choose the capacity of his business before knowing the demand for his product. The unit profit of operation is known with certainty, but there is no flexibility in our one-period framework. We show how the introduction of global uncertainty reduces the investment of the risk-neutral entrepreneur and, even more, that of the risk-averse one. We also show how marginal increases in risk reduce the optimal capacity of both the risk-neutral and the risk-averse entrepreneur, without any restriction on the concave utility function and with limited restrictions on the definition of a mean preserving spread. These general results are explained by the fact that the newsboy has a piecewise-linear, and concave, monetary payoff with a kink endogenously determined at the level of optimal capacity. Our results are compared with those in the two literatures on price uncertainty and demand uncertainty, and particularly, with the recent contributions of Eeckhoudt, Gollier, and Schlesinger [1991, 1995].
    Type of Medium: Electronic Resource
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