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PubMed2005

Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO2 climates.

The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (gamma, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966-1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of gamma near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.

Arachis↗

PubMed2001

Effects of uncertainties on exposure estimates to methylmercury: a Monte Carlo analysis of exposure biomarkers versus dietary recall estimation.

This article presents a general model for estimating population heterogeneity and "lack of knowledge" uncertainty in methylmercury (MeHg) exposure assessments using two-dimensional Monte Carlo analysis. Using data from fish-consuming populations in Bangladesh, Brazil, Sweden, and the United Kingdom, predictive model estimates of dietary MeHg exposures were compared against those derived from biomarkers (i.e., [Hg]hair and [Hg]blood). By disaggregating parameter uncertainty into components (i.e., population heterogeneity, measurement error, recall error, and sampling error) estimates were obtained of the contribution of each component to the overall uncertainty. Steady-state diet:hair and diet:blood MeHg exposure ratios were estimated for each population and were used to develop distributions useful for conducting biomarker-based probabilistic assessments of MeHg exposure. The 5th and 95th percentile modeled MeHg exposure estimates around mean population exposure from each of the four study populations are presented to demonstrate lack of knowledge uncertainty about a best estimate for a true mean. Results from a U.K. study population showed that a predictive dietary model resulted in a 74% lower lack of knowledge uncertainty around a central mean estimate relative to a hair biomarker model, and also in a 31% lower lack of knowledge uncertainty around central mean estimate relative to a blood biomarker model. Similar results were obtained for the Brazil and Bangladesh populations. Such analyses, used here to evaluate alternative models of dietary MeHg exposure, can be used to refine exposure instruments, improve information used in site management and remediation decision making, and identify sources of uncertainty in risk estimates.

Algorithms↗

PubMed2003

Model uncertainty and choices made by modelers: lessons learned from the International Atomic Energy Agency model intercomparisons.

The treatment of uncertainties associated with modeling and risk assessment has recently attracted significant attention. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte Carlo modeling are often recommended. However, the issue of model uncertainty is still rarely addressed in practical applications of risk assessment. The use of several alternative models to derive a range of model outputs or risks is one of a few available techniques. This article addresses the often-overlooked issue of what we call "modeler uncertainty," i.e., difference in problem formulation, model implementation, and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS program (BIOsphere Modeling and ASSessment). Model-model and model-data intercomparisons reviewed in this study were conducted by the working group for a total of three different scenarios. The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that parameter uncertainty (as evaluated by a probabilistic Monte Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in risk characterization.

Journal Article↗

PubMed2004

Accommodating uncertainty in comparative risk.

Comparative risk projects can provide broad policy guidance but they rarely have adequate scientific foundations to support precise risk rankings. Many extant projects report rankings anyway, with limited attention to uncertainty. Stochastic uncertainty, structural uncertainty, and ignorance are types of incertitude that afflict risk comparisons. The recently completed New Jersey Comparative Risk Project was innovative in trying to acknowledge and accommodate some historically ignored uncertainties in a substantive manner. This article examines the methods used and lessons learned from the New Jersey project. Monte Carlo techniques were used to characterize stochastic uncertainty, and sensitivity analysis helped to manage structural uncertainty. A deliberative process and a sorting technique helped manage ignorance. Key findings are that stochastic rankings can be calculated but they reveal such an alarming degree of imprecision that the rankings are no longer useful, whereas sorting techniques are helpful in spite of uncertainty. A deliberative process is helpful to counter analytical overreaching.

Journal Article↗

PubMed1994

Uncertainty and variability in human exposures to soil contaminants through home-grown food: a Monte Carlo assessment.

This paper presents a general model for exposure to homegrown foods that is used with a Monte Carlo analysis to determine the relative contributions of variability (Type A uncertainty) and true uncertainty (Type B uncertainty) to the overall variance in prediction of the dose-to-concentration ratio. Although classification of exposure inputs as uncertain or variable is somewhat subjective, food consumption rates and exposure duration are judged to have a predicted variance that is dominated by variability among individuals by age, income, culture, and geographical region. Whereas, biotransfer factors and partition factors are inputs that, to a large extent, involve uncertainty. Using ingestion of fruits, vegetables, grains, dairy products, and meat and soils assumed to be contaminated by hexachlorobenzene (HCB) and benzo(a)pyrene (BaP) as cases studies, a Monte Carlo analysis is used to explore the relative contribution of uncertainty and variability to overall variance in the estimated distribution of potential dose within the population that consumes home-grown foods. It is found that, when soil concentrations are specified, variances in ratios of dose-to-concentration for HCB are equally attributable to uncertainty and variability, whereas for BaP, variance in these ratios is dominated by true uncertainty.

Algorithms↗

PubMed1995

Presenting uncertainty in health risk assessment: initial studies of its effects on risk perception and trust.

Some analysts suggest that discussing uncertainties in health risk assessments might reduce citizens' perceptions of risk and increase their respect for the risk-assessing agency. We tested this assumption with simulated news stories varying simple displays of uncertainty (e.g., a range of risk estimates, with and without graphics). Subjects from Eugene, Oregon, read one story each, and then answered a questionnaire. Three studies tested between 180 and 272 subjects each. Two focus groups obtained more detailed responses to these stories. The results suggested that (1) people are unfamiliar with uncertainty in risk assessments and in science; (2) people may recognize uncertainty when it is presented simply; (3) graphics may help people recognize uncertainty; (4) reactions to the environmental problems in the stories seemed affected less by presentation of uncertainty than by general risk attitudes and perceptions; (5) agency discussion of uncertainty in risk estimates may signal agency honesty and agency incompetence for some people; and (6) people seem to see lower risk estimates (10(-6), as opposed to 10(-3)) as less credible. These findings, if confirmed, would have important implications for risk communication.

Adult↗

PubMed2005

Quantitative analysis of variability and uncertainty with known measurement error: methodology and case study.

The appearance of measurement error in exposure and risk factor data potentially affects any inferences regarding variability and uncertainty because the distribution representing the observed data set deviates from the distribution that represents an error-free data set. A methodology for improving the characterization of variability and uncertainty with known measurement errors in data is demonstrated in this article based on an observed data set, known measurement error, and a measurement-error model. A practical method for constructing an error-free data set is presented and a numerical method based upon bootstrap pairs, incorporating two-dimensional Monte Carlo simulation, is introduced to address uncertainty arising from measurement error in selected statistics. When measurement error is a large source of uncertainty, substantial differences between the distribution representing variability of the observed data set and the distribution representing variability of the error-free data set will occur. Furthermore, the shape and range of the probability bands for uncertainty differ between the observed and error-free data set. Failure to separately characterize contributions from random sampling error and measurement error will lead to bias in the variability and uncertainty estimates. However, a key finding is that total uncertainty in mean can be properly quantified even if measurement and random sampling errors cannot be separated. An empirical case study is used to illustrate the application of the methodology.

Journal Article↗

PubMed1990

Reconceptualization of the uncertainty in illness theory.

The theory of uncertainty in illness has its strongest support among subjects who are experiencing the acute phase of illness or are in a downward illness trajectory (mishel, 1988a). The theory has not addressed the experience of living with continual, constant uncertainty in either a chronic illness or in an illness with a treatable acute phase and possible eventual recurrence. Since uncertainty characterizes many, most prevalent, long-term illness conditions, there is a need to reconceptualize the theory of uncertainty to include the experience of living with continual uncertainty. A close examination of the theoretical statements and the empirical data reported by Mishel resulted in the identification of areas of the theory that could be expanded and reconceptualized. The reconceptualization effort was primarily fueled by questions about the outcome portion of the uncertainty theory. To provide a contest for the expansion and reconceptualization of uncertainty, applicable parts of the theory are summarized below.

Adaptation, Psychological↗

PubMed1999

Combining uncertainty factors in deriving human exposure levels of noncarcinogenic toxicants.

Acceptable levels of human exposure to noncarcinogenic toxicants in environmental and occupational settings generally are derived by reducing experimental no-observed-adverse-effect levels (NOAELs) or benchmark doses (BDs) by a product of uncertainty factors (Barnes and Dourson, Ref. 1). These factors are presumed to ensure safety by accounting for uncertainty in dose extrapolation, uncertainty in duration extrapolation, differential sensitivity between humans and animals, and differential sensitivity among humans. The common default value for each uncertainty factor is 10. This paper shows how estimates of means and standard deviations of the approximately log-normal distributions of individual uncertainty factors can be used to estimate percentiles of the distribution of the product of uncertainty factors. An appropriately selected upper percentile, for example, 95th or 99th, of the distribution of the product can be used as a combined uncertainty factor to replace the conventional product of default factors.

Benchmarking↗

PubMed2001

Vowel formant discrimination II: Effects of stimulus uncertainty, consonantal context, and training.

This study is one in a series that has examined factors contributing to vowel perception in everyday listening. Four experimental variables have been manipulated to examine systematical differences between optimal laboratory testing conditions and those characterizing everyday listening. These include length of phonetic context, level of stimulus uncertainty, linguistic meaning, and amount of subject training. The present study investigated the effects of stimulus uncertainty from minimal to high uncertainty in two phonetic contexts, /V/ or /bVd/, when listeners had either little or extensive training. Thresholds for discriminating a small change in a formant for synthetic female vowels /I,E,ae,a,inverted v,o/ were obtained using adaptive tracking procedures. Experiment I optimized extensive training for five listeners by beginning under minimal uncertainty (only one formant tested per block) and then increasing uncertainty from 8-to-16-to-22 formants per block. Effects of higher uncertainty were less than expected; performance only decreased by about 30%. Thresholds for CVCs were 25% poorer than for isolated vowels. A previous study using similar stimuli [Kewley-Port and Zheng. J. Acoust. Soc. Am. 106, 2945-2958 (1999)] determined that the ability to discriminate formants was degraded by longer phonetic context. A comparison of those results with the present ones indicates that longer phonetic context degrades formant frequency discrimination more than higher levels of stimulus uncertainty. In experiment 2, performance in the 22-formant condition was tracked over 1 h for 37 typical listeners without formal laboratory training. Performance for typical listeners was initially about 230% worse than for trained listeners. Individual listeners' performance ranged widely with some listeners occasionally achieving performance similar to that of the trained listeners in just one hour.

Adult↗

PubMed2006

Uncertainty analysis in matched-field geoacoustic inversions.

Quantifying uncertainty for parameter estimates obtained from matched-field geoacoustic inversions using a Bayesian approach requires estimation of the uncertainties in the data due to ambient noise as well as modeling errors. In this study, the variance parameter of the Gaussian error model, hereafter called error variance, is assumed to describe the data uncertainty. In practice, this parameter is not known a priori, and choosing a particular value is often difficult. Hence, to account for the uncertainty in error variance, several methods are introduced for implementing both the full and empirical Bayesian approaches. A full Bayesian approach that permits uncertainty of the error variance to propagate through the parameter estimation processes is a natural way of incorporating the uncertainty of error variance. Due to the large number of unknown parameters in the full Bayesian uncertainty analysis, an alternative, the empirical Bayesian approach, is developed, in which the posterior distributions of model parameters are conditioned on a point estimate of the error variance. Comparisons between the full and empirical Bayesian inferences of model parameters are presented using both synthetic and experimental data.

Journal Article↗

PubMed1990

Uncertainty about the correlation among temporal envelopes in two comodulation tasks.

The threshold of a 1250-Hz tonal signal was measured in the presence of five noise bands (each 50 Hz wide, centered at 850, 1050, 1250, 1450, and 1650 Hz) under five conditions of uncertainty about the waveform type ("correlated" or "uncorrelated"), and/or the specific waveform sample to be presented. The waveform type was correlated when the temporal envelopes of all of the noise bands were the same, and was uncorrelated when the temporal envelope of the band centered on the signal differed from the common envelope of the other bands. At the low-uncertainty end of the continuum of conditions, the same waveform type was presented throughout an entire block of trials, and, in addition, the same waveform sample was presented on the two observation intervals of a single trial (but changed across trials). At the high-uncertainty end of the continuum, both the waveform type and the waveform sample were chosen at random for every observation interval. Threshold estimates obtained from trials in which both observation intervals contained the same waveform type were not affected by uncertainty about the waveform sample within a trial, nor by uncertainty about the waveform type introduced across trials. Thus the comodulation masking release, or CMR (the difference in the thresholds obtained with the uncorrelated and correlated waveforms), calculated from these types of trials was robust across all of the uncertainty conditions. However, on those trials in which one correlated interval and one uncorrelated interval were paired, threshold estimates were influenced by a bias for listeners to choose the uncorrelated interval as the signal interval, whether or not it actually contained the signal. This bias reveals the importance of recognizing the contribution of the nonsignal interval in experiments involving masker uncertainty. Parallel results were obtained using the comodulation detection difference (CDD) task. In some conditions, marked individual differences were observed.

Adolescent↗

PubMed2006

Reward value coding distinct from risk attitude-related uncertainty coding in human reward systems.

When deciding between different options, individuals are guided by the expected (mean) value of the different outcomes and by the associated degrees of uncertainty. We used functional magnetic resonance imaging to identify brain activations coding the key decision parameters of expected value (magnitude and probability) separately from uncertainty (statistical variance) of monetary rewards. Participants discriminated behaviorally between stimuli associated with different expected values and uncertainty. Stimuli associated with higher expected values elicited monotonically increasing activations in distinct regions of the striatum, irrespective of different combinations of magnitude and probability. Stimuli associated with higher uncertainty (variance) elicited increasing activations in the lateral orbitofrontal cortex. Uncertainty-related activations covaried with individual risk aversion in lateral orbitofrontal regions and risk-seeking in more medial areas. Furthermore, activations in expected value-coding regions in prefrontal cortex covaried differentially with uncertainty depending on risk attitudes of individual participants, suggesting that separate prefrontal regions are involved in risk aversion and seeking. These data demonstrate the distinct coding in key reward structures of the two basic and crucial decision parameters, expected value, and uncertainty.

Adult↗

PubMedDate not supplied

The association of physician attitudes about uncertainty and risk taking with resource use in a Medicare HMO.

PURPOSE: To explore the association between the attitudes of primary care physicians toward uncertainty and risk taking, as measured by a validated survey, with resource use in a Medicare HMO. DESIGN: All primary-care internists (n=20) in a large, multi-specialty clinic were surveyed to measure their attitudes about uncertainty and risk taking using three previously developed scales. Results were linked with administrative data for 792 consecutive patients in a recently created Medicare HMO. The patients' index visits occurred between April 1, 1995, and November 30, 1995. ANALYSIS: Charges stemming from several claim types (primary care and subspecialty physician, laboratory, radiology, and ambulatory procedures) in the 30 days following the index visit were summed. The physician scales were dichotomized at the median to seek unadjusted associations with charges. Generalized estimation equations were used to account for the correlation of charges resulting from patients' being nested within physicians and adjusted for physician characteristics (age, sex, years in practice) and patient characteristics (age, sex, comorbidity). MAIN RESULTS: The physician response rate was 90%. Most physicians (90%) were male. The mean age of the patients was 74 years, and 69% were female. The mean cost (+/-SD) per patient was $621.61+/-1,737.31. From the unadjusted analysis, high "anxiety due to uncertainty" was associated with higher patient charges ($197.85 vs $158.21, p=0.01). From the multivariable analysis, each standard deviation increase in "anxiety due to uncertainty" (3.5 points) corresponded to a 17% increase in mean charges (p < 0.01) and each similar increase in "reluctance to disclose uncertainty to patients" (1.92 points) corresponded to a 12% increase (p=0.03). However, increasing "reluctance to disclose mistakes to physicians" and increasing physician risk-taking propensity were associated with decreased total charges [-10% per standard deviation (1.34 points), p=0.02, and -8% per standard deviation (3.26 points), p=0.02, respectively]. CONCLUSION: Physician attitudes toward uncertainty were significantly associated with patient charges. Further investigation may improve prediction of patient-care charges, offer insight into the medical decision-making process, and perhaps clarify the relationship between cost, uncertainty, and quality of care.

Aged↗

PubMedDate not supplied

Uncertainty, anxiety, and symptoms of posttraumatic stress in parents of children recently diagnosed with cancer.

The purpose of the study was to describe relationships between uncertainty, anxiety, and symptoms of posttraumatic stress (PTS) in parents of children recently diagnosed with cancer. The study was guided by the Uncertainty in Illness Theory. The setting was a children's hospital. Participants were 15 parents of 12 children recently diagnosed with cancer. Anxiety was measured by the State-Trait Anxiety Inventory. Uncertainty was measured by the Parent Perception of Uncertainty Scale. Symptoms of PTS were measured by the Reaction Index. Total scale scores were calculated and psychometrics estimated. Differences in uncertainty, anxiety, and symptoms of PTS by parent race were assessed using analysis of variance. The correlation matrix was estimated to assess relationships between variables. The level of uncertainty was lower than expected, but was significantly higher for black parents (p < .001). The level of anxiety was comparable to that for hospitalized persons with anxiety disorders. The level of symptoms of PTS was higher than reported for parents of childhood cancer survivors. The relationship between anxiety and symptoms of PTS was significant (r =.56, p < .02). Pediatric oncology nurses should offer parents interventions to relieve high levels of anxiety and symptoms of PTS; some parents could also benefit from interventions that target uncertainty.

Adult↗

PubMed2005

Uncertainties of inherent optical properties obtained from semianalytical inversions of ocean color.

We present a method to quantify the uncertainties in the in-water constituent absorption and backscattering coefficients obtained from an inversion of remotely sensed reflectance (rrs). We first find a set of positive inversion solutions within a given uncertainty range around the values of the inverted rrs. The uncertainties of the solutions are then computed based on the statistics of these solutions. We demonstrate the uncertainty calculation algorithm using a specific semianalytic inversion model applied to both a field and a simulated data set. When the associated uncertainties are taken into account, the inverted parameters are generally within the uncertainties of the measured (or simulated) parameters, highlighting the success of the inversion and the method to obtain uncertainties. The specific inversion we use, however, fails to retrieve two spectral parameters within a usable range. The method presented is general and can be applied to all existing semianalytical inversion algorithms.

Journal Article↗

PubMed2004

Probabilistic uncertainty analysis of the European Union system for the evaluation of substances multimedia regional distribution model.

The European Union System for the Evaluation of Substances (EUSES) is a computerized model system to facilitate and harmonize health and environmental risk assessment of previously notified and new substances. For calculation of regional background exposure, a multimedia distribution model is used. In the present study, the uncertainty of this regional model is analyzed. Environmental parameters were collected for North Rhine Westphalia (Germany), which resembles the standard region of EUSES. Probability distribution functions of various types (uniform, triangular, normal, log normal) depending on data availability were derived for environmental input parameters, including geometric parameters. Generic log-normal distribution functions with fixed standard deviations were chosen for solubility in air, water, and n-octanol as well as for degradation half-lives. Monte Carlo simulations were carried out for 10 reference substances having different properties. Contribution of environmental parameter uncertainty to total output uncertainties is higher than that of substance parameters. Range of output uncertainty, defined as the ratio of the logarithms of the 90th and 10th percentiles of the cumulative probability distribution function, shows an increase from air and water to soil. The highest-occurring range is 1.4 orders of magnitude, which means that total uncertainty of the regional model is relatively low and, usually, is lower than the range of measured values. The median of output probability distributions lies above the point estimate. Influence of input parameters was estimated as their rank correlation coefficients to output uncertainty. Substance and environmental parameters contribute differently to output variance depending on individual substance properties and environmental compartment. Hence, the present study underlines the need to perform uncertainty analyses instead of either using a set of simple rules or just looking at certain parameters.

Environment↗

PubMed2005

Sampling uncertainties for the detection of chemical agents in complex food matrices.

Using uncertainty associated with detection of aflatoxin in shelled corn as a model, the uncertainty associated with detecting chemical agents intentionally added to food products was evaluated. Accuracy and precision are two types of uncertainties generally associated with sampling plans. Sources of variability that affect precision were the primary focus of this investigation. Test procedures used to detect chemical agents generally include sampling, sample preparation, and analytical steps. The uncertainty of each step contributes to the total uncertainty of the test procedure. Using variance as a statistical measure of uncertainty, the variance associated with each step of the test procedure used to detect aflatoxin in shelled corn was determined for both low and high levels of contamination. For example, when using a 1-kg sample, Romer mill, 50-g subsample, and high-performance liquid chromatography to test a lot of shelled corn contaminated with aflatoxin at 10 ng/g, the total variance associated with the test procedure was 149.2 (coefficient of variation of 122.1%). The sampling, sample preparation, and analytical steps accounted for 83.0, 15.6, and 1.4% of the total variance, respectively. A variance of 149.2 suggests that repeated test results will vary from 0 to 33.9 ng/g. Using the same test procedure to detect aflatoxin at 10,000 ng/g, the total variance was 264,719 (coefficient of variation of 5.1%). The sampling, sample preparation, and analytical steps accounted for 41, 57, and 2% of the total variance, respectively. A variance of 264,719 suggests that repeated test results will vary from 8,992 to 11,008 ng/g. Foods contaminated at low levels reflect a situation in which a small percentage of particles is contaminated and sampling becomes the largest source of uncertainty. Large samples are required to overcome the "needle-in-the-haystack" problem. Aflatoxin is easier to detect and identify in foods intentionally contaminated at high levels than in foods with low levels of contamination because the relative standard deviation (coefficient of variation) decreases and the percentage of contaminated kernels increases with an increase in concentration.

Aflatoxins↗