Performance measurement with classification information: an enhanced additive DEA model

This paper presents an improved measurement tool for evaluating performance of branches within a major Canadian bank. While there have been numerous previous studies of performance in the banking industry, particularly at the branch level, this study is different in a very significant way: specifically two kinds of data are used to develop the model. The first type of data is that related to standard transactions, available from any bank; such have formed the basis of numerous previous studies. The second type of data, obtained from the site studied, is classification information, based on branch consultant/expert judgment as to good and poor performance of branches. The purpose herein is to present a modified version of an existing benchmarking model, data envelopment analysis (DEA), and to show how this tool is applied in the banking industry. The mechanism used herein to incorporate expert knowledge within the DEA framework is to first apply a discriminant or classification tool, to quantify the functional relation that best captures the expert's mental model for performance. The outcome of this first phase is an orientation of variables to aid in the definition of inputs and outputs. The resulting orientation then defines the DEA model that makes up the second phase of the model.     

Mixed Levels of Uncertainty in Complex Policy Models

The characterization and treatment of uncertainty poses special challenges when modeling indeterminate or complex coupled systems such as those involved in the interactions between human activity, climate and the ecosystem. Uncertainty about model structure may become as, or more important than, uncertainty about parameter values. When uncertainty grows so large that prediction or optimization no longer makes sense, it may still be possible to use the model as a behavioral test bed to examine the relative robustness of alternative observational and behavioral strategies. When models must be run into portions of their phase space that are not well understood, different submodels may become unreliable at different rates. A common example involves running a time stepped model far into the future. Several strategies can be used to deal with such situations. The probability of model failure can be reported as a function of time. Possible alternative surprises can be assigned probabilities, modeled separately, and combined. Finally, through the use of subjective judgments, one may be able to combine, and over time shift between models, moving from more detailed to progressively simpler order-of-magnitude models, and perhaps ultimately, on to simple bounding analysis.     

A Risk Assessment Method for Biological Introductions

This article describes an application of a method for assessing risks associated with the introduction of an organism into a new environment. The test organism was a binucleate Rhizoctonia fungal isolate that has potential for commercial development as a biological control agent for damping-off diseases in bedding plants. A test sample of host plant species was selected using the centrifugal phylogenetic host range principles, but with an emphasis on economic species. The effect of the fungus on the plant was measured for each species and expressed on a logarithmic scale. The effects on weights of shoots and roots per container were not normally distributed, nor were the effects on the number of plants standing (those which survived). Statements about the effect on the number standing and the shoot weight per container involved using the observed (empirical) distribution. This is illustrated with an example. Problems were encountered in defining the population of species at risk, and in deciding how this population should be formally sampled. The limitations of the method are discussed.     

Health Risk Assessment of a Modern Municipal Waste Incinerator

During the modernization of the municipal waste incinerator (MWI, maximum capacity of 180,000 tons per year) of Metropolitan Grenoble (405,000 inhabitants), in France, a risk assessment was conducted, based on four tracer pollutants: two volatile organic compounds (benzene and 1, 1, 1 trichloroethane) and two heavy metals (nickel and cadmium, measured in particles). A Gaussian plume dispersion model, applied to maximum emissions measured at the MWI stacks, was used to estimate the distribution of these pollutants in the atmosphere throughout the metropolitan area. A random sample telephone survey (570 subjects) gathered data on time-activity patterns, according to demographic characteristics of the population. Life-long exposure was assessed as a time-weighted average of ambient air concentrations. Inhalation alone was considered because, in the Grenoble urban setting, other routes of exposure are not likely. A Monte Carlo simulation was used to describe probability distributions of exposures and risks. The median of the life-long personal exposures distribution to MWI benzene was 3.2·10^–5 g/m³ (20th and 80th percentiles = 1.5·10^–5 and 6.5·10^–5 g/m³), yielding a 2.6·10^–10 carcinogenic risk (1.2·10^–10–5.4·10^–10). For nickel, the corresponding life-time exposure and cancer risk were 1.8·10^–4 g/m³ (0.9.10^–4 – 3.6·10^–4 g/m³) and 8.6·10^–8 (4.3·10^–8–17.3·10^–8); for cadmium they were respectively 8.3·10^–6 g/m³ (4.0·10^–6–17.6·10^–6) and 1.5·10^–8 (7.2·10^–9–3.1·10^–8). Inhalation exposure to cadmium emitted by the MWI represented less than 1% of the WHO Air Quality Guideline (5 ng/m³), while there was a margin of exposure of more than 10⁹ between the NOAEL (150 ppm) and exposure estimates to trichloroethane. Neither dioxins nor mercury, a volatile metal, were measured. This could lessen the attributable life-long risks estimated. The minute (VOCs and cadmium) to moderate (nickel) exposure and risk estimates are in accord with other studies on modern MWIs meeting recent emission regulations, however.     

On the Effect of Probability Distributions of Input Variables in Public Health Risk Assessment

A central part of probabilistic public health risk assessment is the selection of probability distributions for the uncertain input variables. In this paper, we apply the first-order reliability method (FORM)^(1–3) as a probabilistic tool to assess the effect of probability distributions of the input random variables on the probability that risk exceeds a threshold level (termed the probability of failure) and on the relevant probabilistic sensitivities. The analysis was applied to a case study given by Thompson et al. ⁽⁴⁾ on cancer risk caused by the ingestion of benzene contaminated soil. Normal, lognormal, and uniform distributions were used in the analysis. The results show that the selection of a probability distribution function for the uncertain variables in this case study had a moderate impact on the probability that values would fall above a given threshold risk when the threshold risk is at the 50th percentile of the original distribution given by Thompson et al. ⁽⁴⁾ The impact was much greater when the threshold risk level was at the 95th percentile. The impact on uncertainty sensitivity, however, showed a reversed trend, where the impact was more appreciable for the 50th percentile of the original distribution of risk given by Thompson et al. ⁴ than for the 95th percentile. Nevertheless, the choice of distribution shape did not alter the order of probabilistic sensitivity of the basic uncertain variables.     

Perception of Ecological Risk to Water Environments

This paper examines lay and expert perceptions of the ecological risks associated with a range of human activities that could adversely affect water resource environments. It employs the psychometric paradigm pioneered in characterizing perceptions of human health risks, which involves surveys to obtain judgments from subjects about risk items in terms of several important characteristics of the risks. The paper builds on a previous study that introduced ecological risk perception. This second study employs a larger, more diverse sample, a more focused topic area, and comparisons between lay and expert judgments. The results confirm that a small set of underlying factors explain a great deal of variability in lay judgments about ecological risks. These have been termed Ecological Impact, Human Benefits, Controllability , and Knowledge. The results are useful in explaining subjects' judgments of the general riskiness of, and need for regulation of, various risk items. The results also indicate several differences and areas of agreement among the lay and expert samples that point to potential key issues in future ecological risk management efforts for water resources.     

ATSDR Science Panel on the Bioavailability of Mercury in Soils: Lessons Learned

On the basis of discussion and analysis during and following an ATSDR science panel on the bioavailability of mercury in soils, it is apparent that the default assumption of 100% relative bioavailability for mercury-contaminated soils is excessively conservative. However, current knowledge does not allow the development of default assumptions or guidelines for determining relative bioavailability of mercury in soils. Until such default assumptions or guidelines can be developed, site-specific assays of bioavailability, preferably using either animal bioassays or validated in vitro techniques, may provide the best approach for estimating soil-mercury bioavailability.     

Sensitivity,uncertainty, and decision analyses in the prioritization of research

The evaluation of hazards from complex, large scale, technologically advanced systems often requires the construction of computer implemented mathematical models. These models are used to evaluate the safety of the systems and to evaluate the consequences of modifications to the systems. These evaluations, however, are normally surrounded by significant uncertainties related to the uncertainty inherent in natural phenomena such as the weather and those related to uncertainties in the parameters and models used in the evaluation.

Another use of these models is to evaluate strategies for improving information used in the modeling process itself. While sensitivity analysis is useful in defining variables in the model that are important, uncertainty analysis provides a tool for assessing the importance of uncertainty about these variables. A third complementary technique, is decision analysis. It provides a methodology for explicitly evaluating and ranking potential improvements to the model. Its use in the development of information gathering strategies for a nuclear waste repository are discussed in this paper.     

论人口普查质量评估统计量

人口统计学家通常用人口普查误差来评估人口普查质量。然而,确切的人口普查误差是无法计算的,能够计算的仅仅是它的估计量。甚至,在有些时候所估计的,其实并不是人口普查误差,而仅仅是人口普查的平均差。详细解析这方面的基本理论和基本概念,分析人们在应用人口普查质量评估统计量时的常见误区,指出这种错误所引发的连带问题。     

中国高技术产业技术创新能力分行业评价研究——基于微粒群算法的实证分析

首先通过表征化、形象化和抽象化三个步骤构建技术创新能力评价的杠杆模型,然后基于杠杆模型建立了中国高技术产业技术创新能力的评价指标体系。在此基础上,通过构建基于微粒群算法的评价模型,对中国高技术产业5大行业的17个细分行业技术创新能力进行评价分析。结果表明：17个细分行业的技术创新能力存在着明显差异,排名前3的行业依次为：通信设备制造业、家用视听设备制造业和飞机制造业及修理业;并且根据各细分行业技术创新能力的3个组成能力的分值和排名,将17个细分行业划分为“基本匹配型”、“弱匹配型”和“不匹配型”3种类型,其中,只有化学药品原药制造业、仪器仪表制造业2个行业处于“基本匹配型”。