Ellsberg Revisited: An Experimental Study

An extension to Ellsberg's experiment demonstrates that attitudes to ambiguity and compound objective lotteries are tightly associated. The sample is decomposed into three main groups: subjective expected utility subjects, who reduce compound objective lotteries and are ambiguity neutral, and two groups that exhibit different forms of association between preferences over compound lotteries and ambiguity, corresponding to alternative theoretical models that account for ambiguity averse or seeking behavior.     

A Probabilistic Approach to Assess External Doses to the Public Considering Spatial Variability of Radioactive Contamination and Interpopulation Differences in Behavior Pattern

Dose assessment is an important issue from the viewpoints of protecting people from radiation exposure and managing postaccident situations adequately. However, the radiation doses received by people cannot be determined with complete accuracy because of the uncertainties and the variability associated with any process of defining individual characteristics and in the dose assessment process itself. In this study, a dose assessment model was developed based on measurements and surveys of individual doses and relevant contributors (i.e., ambient dose rates and behavior patterns) in Fukushima City for four population groups: Fukushima City Office staff, Senior Citizens’ Club, Contractors’ Association, and Agricultural Cooperative. In addition, probabilistic assessments were performed for these population groups by considering the spatial variability of contamination and interpopulation differences resulting from behavior patterns. As a result of comparison with the actual measurements, the assessment results for participants from the Fukushima City Office agreed with the measured values, thereby validating the model and the approach. Although the assessment results obtained for the Senior Citizens’ Club and the Agricultural Cooperative differ partly from the measured values, by addressing further considerations in terms of dose reduction effects due to decontamination and the impact of additional exposure sources in agricultural fields, these results can be improved. By contrast, the measurements obtained for the participants from the Contractors’ Association were not reproduced well in the present study. To assess the doses to this group, further investigations of association members’ work activities and the related dose reduction effects are needed.     

Sensitivity Analysis and Estimation of Extreme Tail Behavior in Two‐Dimensional Monte Carlo Simulation

Two‐dimensional Monte Carlo simulation is frequently used to implement probabilistic risk models, as it allows for uncertainty and variability to be quantified separately. In many cases, we are interested in the proportion of individuals from a variable population exceeding a critical threshold, together with uncertainty about this proportion. In this article we introduce a new method that can accurately estimate these quantities much more efficiently than conventional algorithms. We also show how those model parameters having the greatest impact on the probabilities of rare events can be quickly identified via this method. The algorithm combines elements from well‐established statistical techniques in extreme value theory and Bayesian analysis of computer models. We demonstrate the practical application of these methods with a simple example, in which the true distributions are known exactly, and also with a more realistic model of microbial contamination of milk with seven parameters. For the latter, sensitivity analysis (SA) is shown to identify the two inputs explaining the majority of variation in distribution tail behavior. In the subsequent prediction of probabilities of large contamination events, similar results are obtained using the new approach taking 43 seconds or the conventional simulation that requires more than 3 days.     

A Probabilistic Transmission Dynamic Model to Assess Indoor Airborne Infection Risks

The purpose of this article is to quantify the public health risk associated with inhalation of indoor airborne infection based on a probabilistic transmission dynamic modeling approach. We used the Wells-Riley mathematical model to estimate (1) the CO2 exposure concentrations in indoor environments where cases of inhalation airborne infection occurred based on reported epidemiological data and epidemic curves for influenza and severe acute respiratory syndrome (SARS), (2) the basic reproductive number, R0 (i.e., expected number of secondary cases on the introduction of a single infected individual in a completely susceptible population) and its variability in a shared indoor airspace, and (3) the risk for infection in various scenarios of exposure in a susceptible population for a range of R0. We also employ a standard susceptible-infectious-recovered (SIR) structure to relate Wells-Riley model derived R0 to a transmission parameter to implicate the relationships between indoor carbon dioxide concentration and contact rate. We estimate that a single case of SARS will infect 2.6 secondary cases on average in a population from nosocomial transmission, whereas less than 1 secondary infection was generated per case among school children. We also obtained an estimate of the basic reproductive number for influenza in a commercial airliner: the median value is 10.4. We suggest that improving the building air cleaning rate to lower the critical rebreathed fraction of indoor air can decrease transmission rate. Here, we show that virulence of the organism factors, infectious quantum generation rates (quanta/s by an infected person), and host factors determine the risk for inhalation of indoor airborne infection.     

Epistemic Uncertainty in the Ranking and Categorization of Probabilistic Safety Assessment Model Elements: Issues and Findings

In this work, we study the effect of epistemic uncertainty in the ranking and categorization of elements of probabilistic safety assessment (PSA) models. We show that, while in a deterministic setting a PSA element belongs to a given category univocally, in the presence of epistemic uncertainty, a PSA element belongs to a given category only with a certain probability. We propose an approach to estimate these probabilities, showing that their knowledge allows to appreciate " the sensitivity of component categorizations to uncertainties in the parameter values " (U.S. NRC Regulatory Guide 1.174). We investigate the meaning and utilization of an assignment method based on the expected value of importance measures. We discuss the problem of evaluating changes in quality assurance, maintenance activities prioritization, etc. in the presence of epistemic uncertainty. We show that the inclusion of epistemic uncertainly in the evaluation makes it necessary to evaluate changes through their effect on PSA model parameters. We propose a categorization of parameters based on the Fussell-Vesely and differential importance (DIM) measures. In addition, issues in the calculation of the expected value of the joint importance measure are present when evaluating changes affecting groups of components. We illustrate that the problem can be solved using DIM. A numerical application to a case study concludes the work.     

Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches

Uncertainty importance measures are quantitative tools aiming at identifying the contribution of uncertain inputs to output uncertainty. Their application ranges from food safety (Frey & Patil (2002)) to hurricane losses (Iman et al. (2005a, 2005b)). Results and indications an analyst derives depend on the method selected for the study. In this work, we investigate the assumptions at the basis of various indicator families to discuss the information they convey to the analyst/decisionmaker. We start with nonparametric techniques, and then present variance-based methods. By means of an example we show that output variance does not always reflect a decisionmaker state of knowledge of the inputs. We then examine the use of moment-independent approaches to global sensitivity analysis, i.e., techniques that look at the entire output distribution without a specific reference to its moments. Numerical results demonstrate that both moment-independent and variance-based indicators agree in identifying noninfluential parameters. However, differences in the ranking of the most relevant factors show that inputs that influence variance the most are not necessarily the ones that influence the output uncertainty distribution the most.     

Antecedents and Consequences of Words

As instances of behavior, words interact with environments. But they also interact with each other and with other kinds of behavior. Because of the interlocking nature of the contingencies into which words enter, their behavioral properties may become increasingly removed from nonverbal contingencies, and their relationship to those contingencies may become distorted by the social contingencies that maintain verbal behavior. Verbal behavior is an exceedingly efficient way in which one organism can change the behavior of another. All other functions of verbal behavior derive from this most basic function, sometimes called verbal governance. Functional verbal antecedents in verbal governance may be extended across time and space when individuals replicate the verbal behavior of others or their own verbal behavior. Differential contact with different verbal antecedents may follow from differential attention to verbal stimuli correlated with consequential events. Once in place, verbal behavior can be shaped by (usually social) consequences. Because these four verbal processes (verbal governance, replication, differential attention, and verbal shaping) share common stimulus and response terms, they produce interlocking contingencies in which extensive classes of behavior come to be dominated by verbal antecedents. Very different consequences follow from verbal behavior depending on whether it is anchored to environmental events, as in scientific verbal practices, or becomes independent of it, as in religious fundamentalism.     

Importance of Uncertainty and Variability to Predicted Risks from Trophic Transfer of PCBs in Dredged Sediments

Biomagnification of organochlorine and other persistent organic contaminants by higher trophic level organisms represents one of the most significant sources of uncertainty and variability in evaluating potential risks associated with disposal of dredged materials. While it is important to distinguish between population variability (e.g., true population heterogeneity in fish weight, and lipid content) and uncertainty (e.g., measurement error), they can be operationally difficult to define separately in probabilistic estimates of human health and ecological risk. We propose a disaggregation of uncertain and variable parameters based on: (1) availability of supporting data; (2) the specific management and regulatory context (in this case, of the U.S. Army Corps of Engineers/U.S. Environmental Protection Agency tiered approach to dredged material management); and (3) professional judgment and experience in conducting probabilistic risk assessments. We describe and quantitatively evaluate several sources of uncertainty and variability in estimating risk to human health from trophic transfer of polychlorinated biphenyls (PCBs) using a case study of sediments obtained from the New York-New Jersey Harbor and being evaluated for disposal at an open water off-shore disposal site within the northeast region. The estimates of PCB concentrations in fish and dietary doses of PCBs to humans ingesting fish are expressed as distributions of values, of which the arithmetic mean or mode represents a particular fractile. The distribution of risk values is obtained using a food chain biomagnification model developed by Gobas by specifying distributions for input parameters disaggregated to represent either uncertainty or variability. Only those sources of uncertainty that could be quantified were included in the analysis. Results for several different two-dimensional Latin Hypercube analyses are provided to evaluate the influence of the uncertain versus variable disaggregation of model parameters. The analysis suggests that variability in human exposure parameters is greater than the uncertainty bounds on any particular fractile, given the described assumptions.     

AGAPE-ET: A Methodology for Human Error Analysis of Emergency Tasks

This article introduces a human error analysis or human reliability analysis methodology, AGAPE-ET (A Guidance And Procedure for Human Error Analysis for Emergency Tasks), for analyzing emergency tasks in nuclear power plants. The AGAPE-ET method is based on a simplified cognitive model and a set of performance-influencing factors (PIFs). At each cognitive function, error-causing factors (ECF) or error-likely situations have been identified considering the characteristics of the performance of each cognitive function and the influencing mechanism of the PIFs on the cognitive function. Then, a human error analysis procedure based on the error analysis factors is organized to cue or guide the analyst in conducting the human error analysis. The method can be characterized by the structured identification of weak points of the task required to be performed and by the efficient analysis process such that the analyst has only to carry out the analysis with the necessary cognitive functions. Through the application, AGAPE-ET showed its usefulness, which effectively identifies the vulnerabilities with respect to cognitive performance as well as task execution, and that helps the analyst directly draw specific error reduction measures through the analysis.     

Behavior Analytic Concepts and Change in a Large Metropolitan Research University: The Graduation Success Initiative

The Graduation Success Initiative is a complex, organization-wide application of behavior analytic concepts to improving undergraduate student retention and on-time graduation at a large metropolitan research university. The behavior analytic concepts discussed here include culture, supraorganismic phenomena, selecting environments, macrobehaviors, macrocontingencies, interlocking behavioral contingencies, metacontingencies, and rule-governed behavior. We introduce a change template that includes all pertinent agents in the university system and that focuses change efforts specifically on desired behaviors, targeted behaviors, strategic interventions, and reinforcing contingencies for each of the categories of agents. The Graduation Success Initiative produced a 16-point increase in on-time graduation in 4 years.