Agent‐Based Modeling to Simulate Contamination Events and Evaluate Threat Management Strategies in Water Distribution Systems

In the event of contamination of a water distribution system, decisions must be made to mitigate the impact of the contamination and to protect public health. Making threat management decisions while a contaminant spreads through the network is a dynamic and interactive process. Response actions taken by the utility managers and water consumption choices made by the consumers will affect the hydraulics, and thus the spread of the contaminant plume, in the network. A modeling framework that allows the simulation of a contamination event under the effects of actions taken by utility managers and consumers will be a useful tool for the analysis of alternative threat mitigation and management strategies. This article presents a multiagent modeling framework that combines agent‐based, mechanistic, and dynamic methods. Agents select actions based on a set of rules that represent an individual's autonomy, goal‐based desires, and reaction to the environment and the actions of other agents. Consumer behaviors including ingestion, mobility, reduction of water demands, and word‐of‐mouth communication are simulated. Management strategies are evaluated, including opening hydrants to flush the contaminant and broadcasts. As actions taken by consumer agents and utility operators affect demands and flows in the system, the mechanistic model is updated. Management strategies are evaluated based on the exposure of the population to the contaminant. The framework is designed to consider the typical issues involved in water distribution threat management and provides valuable analysis of threat containment strategies for water distribution system contamination events.     

A Hybrid Methodology for Modeling Risk of Adverse Events in Complex Health‐Care Settings

In spite of increased attention to quality and efforts to provide safe medical care, adverse events (AEs) are still frequent in clinical practice. Reports from various sources indicate that a substantial number of hospitalized patients suffer treatment‐caused injuries while in the hospital. While risk cannot be entirely eliminated from health‐care activities, an important goal is to develop effective and durable mitigation strategies to render the system “safer.” In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the health‐care domain, this can be extremely challenging due to the wide variability in the way that health‐care processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study, we have developed a generic methodology for evaluating dynamic changes in AE risk in acute care hospitals as a function of organizational and nonorganizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational‐level and policy‐level contributions to risk evolve over time, and how policies and decisions may affect the general system‐level contribution to AE risk. It also captures the feedback of organizational factors and decisions over time and the nonlinearities in these feedback effects. SD is a popular approach to understanding the behavior of complex social and economic systems. It is a simulation‐based, differential equation modeling tool that is widely used in situations where the formal model is complex and an analytical solution is very difficult to obtain. Second, a Bayesian belief network (BBN) framework is used to represent patient‐level factors and also physician‐level decisions and factors in the management of an individual patient, which contribute to the risk of hospital‐acquired AE. BBNs are networks of probabilities that can capture probabilistic relations between variables and contain historical information about their relationship, and are powerful tools for modeling causes and effects in many domains. The model is intended to support hospital decisions with regard to staffing, length of stay, and investments in safety, which evolve dynamically over time. The methodology has been applied in modeling the two types of common AEs: pressure ulcers and vascular‐catheter‐associated infection, and the models have been validated with eight years of clinical data and use of expert opinion.     

Application of a QMRA Framework to Inform Selection of Drinking Water Interventions in the Developing Context

The aim of this study was to develop a modified quantitative microbial risk assessment (QMRA) framework that could be applied as a decision support tool to choose between alternative drinking water interventions in the developing context. The impact of different household water treatment (HWT) interventions on the overall incidence of diarrheal disease and disability adjusted life years (DALYs) was estimated, without relying on source water pathogen concentration as the starting point for the analysis. A framework was developed and a software tool constructed and then implemented for an illustrative case study for Nepal based on published scientific data. Coagulation combined with free chlorine disinfection provided the greatest estimated health gains in the short term; however, when long‐term compliance was incorporated into the calculations, the preferred intervention was porous ceramic filtration. The model demonstrates how the QMRA framework can be used to integrate evidence from different studies to inform management decisions, and in particular to prioritize the next best intervention with respect to estimated reduction in diarrheal incidence. This study only considered HWT interventions; it is recognized that a systematic consideration of sanitation, recreation, and drinking water pathways is important for effective management of waterborne transmission of pathogens, and the approach could be expanded to consider the broader water‐related context.     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

A General Framework for the Assessment of Power System Vulnerability to Malicious Attacks

The protection and safe operations of power systems heavily rely on the identification of the causes of damage and service disruption. This article presents a general framework for the assessment of power system vulnerability to malicious attacks. The concept of susceptibility to an attack is employed to quantitatively evaluate the degree of exposure of the system and its components to intentional offensive actions. A scenario with two agents having opposing objectives is proposed, i.e., a defender having multiple alternatives of protection strategies for system elements, and an attacker having multiple alternatives of attack strategies against different combinations of system elements. The defender aims to minimize the system susceptibility to the attack, subject to budget constraints; on the other hand, the attacker aims to maximize the susceptibility. The problem is defined as a zero‐sum game between the defender and the attacker. The assumption that the interests of the attacker and the defender are opposite makes it irrelevant whether or not the defender shows the strategy he/she will use. Thus, the approaches “leader–follower game” or “simultaneous game” do not provide differences as far as the results are concerned. The results show an example of such a situation, and the von Neumann theorem is applied to find the (mixed) equilibrium strategies of the attacker and of the defender.     

Review and Evaluation of the J100-10 Risk and Resilience Management Standard for Water and Wastewater Systems

Risk analysis standards are often employed to protect critical infrastructures, which are vital to a nation's security, economy, and safety of its citizens. We present an analysis framework for evaluating such standards and apply it to the J100-10 risk analysis standard for water and wastewater systems. In doing so, we identify gaps between practices recommended in the standard and the state of the art. While individual processes found within infrastructure risk analysis standards have been evaluated in the past, we present a foundational review and focus specifically on water systems. By highlighting both the conceptual shortcomings and practical limitations, we aim to prioritize the shortcomings needed to be addressed. Key findings from this study include (1) risk definitions fail to address notions of uncertainty, (2) the sole use of “worst reasonable case” assumptions can lead to mischaracterizations of risk, (3) analysis of risk and resilience at the threat-asset resolution ignores dependencies within the system, and (4) stakeholder values need to be assessed when balancing the tradeoffs between risk reduction and resilience enhancement.     

混合分销渠道结构下短生命周期产品供应链库存策略分析

随着竞争的加剧,产品生命周期日渐缩短。信息和网络技术的不断进步,使得网络作为一种特殊的分销渠道出现。传统分销渠道和网上直销渠道并存的混合分销渠道结构给理论研究和管理实践提出了新的挑战,本文针对混合分销渠道结构下短生命周期产品供应链,运用报童问题的框架,分析了两种不同运作模式下生产商和零售商库存策略,并通过数值实验研究了需求不确定性对生产商和零售商最优库存策略的影响。最后,根据数值实验的计算结果,总结了本研究的管理启示。     

Communication About Micropollutants in Drinking Water: Effects of the Presentation and Psychological Processes

This study investigates how people change their risk perception, trust, and behavior as a consequence of being informed about the occurrence of micropollutants in drinking water. Micropollutants are substances present in extremely low concentrations that might be dangerous in higher concentrations. Data were gathered in the city of Zurich, Switzerland in 2013 using a questionnaire in which the information on micropollutants was presented differently to 12 experimental groups. Data of the key constructs were gathered before and after this information, so that causal effects could be quantified by regression analyses. Affective reactions to the information turned out to be the critical mediator of changes in risk perception (operationalized as the perceived change of quality due to pollution), which is an important determinant of changes in behavior and trust. Also, direct effects of affective reactions on behavior and trust were observed. Trust before appraising risks reduces negative affective reactions; however, it also reduces perceived quality (i.e., increases risk perception) and trust after risks are appraised. The different forms of information mainly influenced the participants’ affective reactions, but they also influenced perceived quality. The presentation with the least negative effects was a comparison of the intake of the substance by water with intake by food. The experimental design with repeated measurement that considers trust as a determinant and consequence of risk perception uncovered positive and negative effects of trust before appraising risks on changes of risk perception and trust due to appraising risks.     

An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems

The end states reached by an engineered system during an accident scenario depend not only on the sequences of the events composing the scenario, but also on their timing and magnitudes. Including these additional features within an overarching framework can render the analysis infeasible in practical cases, due to the high dimension of the system state‐space and the computational effort correspondingly needed to explore the possible system evolutions in search of the interesting (and very rare) ones of failure. To tackle this hurdle, in this article we introduce a framework for efficiently probing the space of event sequences of a dynamic system by means of a guided Monte Carlo simulation. Such framework is semi‐automatic and allows embedding the analyst's prior knowledge about the system and his/her objectives of analysis. Specifically, the framework allows adaptively and intelligently allocating the simulation efforts preferably on those sequences leading to outcomes of interest for the objectives of the analysis, e.g., typically those that are more safety‐critical (and/or rare). The emerging diversification in the filling of the state‐space by the preference‐guided exploration allows also the retrieval of critical system features, which can be useful to analysts and designers for taking appropriate means of prevention and mitigation of dangerous and/or unexpected consequences. A dynamic system for gas transmission is considered as a case study to demonstrate the application of the method.     

非均匀分布下区间数排序可能度计算模型及其应用

区间数排序的可能度计算模型是学术界一直在不断探索的基础性问题之一。区间数刻画着事物属性特征的取值范围,以往学术界都假设在区间内的取值服从均匀分布。本文将均匀分布推广到一般分布,运用概率论的方法,构建了一个新的区间数排序的可能度计算模型,由此修正了以往关于两个区间数全等的定义,提出了区间数形等的概念,同时进一步修正了可能度的自反性条件和区间数的综合排序方法,并将理论应用于多属性决策问题,给出了基本的决策过程,通过实例决策问题的计算,呈现了新理论和新方法的可行性和合理性,具有很好的推广应用价值。     

A Framework for Understanding Risk Perception,Explored from the Perspective of the Water Practitioner

Sustainable urban water systems are likely to be hybrids of centralized and decentralized infrastructure, managed as an integrated system in water‐sensitive cities. The technology for many of these systems is available. However, social and institutional barriers, which can be understood as deeply embedded risk perceptions, have impeded their implementation. Risk perceptions within the water sector are often unrecognized or unacknowledged, despite their role in risk management generally in informing value judgments and specifically in ranking risks to achieve management objectives. There has been very little examination of the role of these risk perceptions in advancing more sustainable water supply management through the adoption of alternative sources. To address this gap, this article presents a framework that can be used as a tool for understanding risk perceptions. The framework is built on the relational theory of risk and presents the range of human phenomena that might influence the perception of an “object at risk” in relation to a “risk object.” It has been synthesized from a critical review of theoretical, conceptual, and empirical studies of perception broadly and risk perception specifically, and interpreted in relation to water practitioners. For a water practitioner, the risk object might be an alternative water system, a component, a process, or a technology, and the object at risk could be public or environmental health, profitability, or professional reputation. This framework has two important functions: to allow practitioners to understand their own and others’ risk perceptions, which might differ, and to inform further empirical research.     

An Upper‐Bound Assessment of the Benefits of Reducing Perchlorate in Drinking Water

The Environmental Protection Agency plans to issue new federal regulations to limit drinking water concentrations of perchlorate, which occurs naturally and results from the combustion of rocket fuel. This article presents an upper‐bound estimate of the potential benefits of alternative maximum contaminant levels for perchlorate in drinking water. The results suggest that the economic benefits of reducing perchlorate concentrations in drinking water are likely to be low, i.e., under $2.9 million per year nationally, for several reasons. First, the prevalence of detectable perchlorate in public drinking water systems is low. Second, the population especially sensitive to effects of perchlorate, pregnant women who are moderately iodide deficient, represents a minority of all pregnant women. Third, and perhaps most importantly, reducing exposure to perchlorate in drinking water is a relatively ineffective way of increasing iodide uptake, a crucial step linking perchlorate to health effects of concern.     

The Effect of Neglecting Correlations When Propagating Uncertainty and Estimating the Population Distribution of Risk

Interest in examining both the uncertainty and variability in environmental health risk assessments has led to increased use of methods for propagating uncertainty. While a variety of approaches have been described, the advent of both powerful personal computers and commercially available simulation software have led to increased use of Monte Carlo simulation. Although most analysts and regulators are encouraged by these developments, some are concerned that Monte Carlo analysis is being applied uncritically. The validity of any analysis is contingent on the validity of the inputs to the analysis. In the propagation of uncertainty or variability, it is essential that the statistical distribution of input variables are properly specified. Furthermore, any dependencies among the input variables must be considered in the analysis. In light of the potential difficulty in specifying dependencies among input variables, it is useful to consider whether there exist rules of thumb as to when correlations can be safely ignored (i.e., when little overall precision is gained by an additional effort to improve upon an estimation of correlation). We make use of well-known error propagation formulas to develop expressions intended to aid the analyst in situations wherein normally and lognormally distributed variables are linearly correlated.     

Treating and Drinking Well Water in the Presence of Health Risks from Arsenic Contamination: Results from a U.S. Hot Spot

The Safe Drinking Water Act of 1974 regulates water quality in public drinking water supply systems but does not pertain to private domestic wells, often found in rural areas throughout the country. The recent decision to tighten the drinking water standard for arsenic from 50 parts per billion (ppb) to 10 ppb may therefore affect some households in rural communities, but may not directly reduce health risks for those on private wells. The article reports results from a survey conducted in a U.S. arsenic hot spot, the rural area of Churchill County, Nevada. This area has elevated levels of arsenic in groundwater. We find that a significant proportion of households on private wells are consuming drinking water with arsenic levels that pose a health risk. The decision to treat tap water for those on private wells in this area is modeled, and the predicted probability of treatment is used to help explain drinking water consumption. This probability represents behaviors relating to the household's perception of risk.     

Methodology for the Calculation of Annualized Incremental Risks in Systems of Dams

In the past few years, the field of dam safety has approached risk informed methodologies throughout the world and several methodologies and programs are appearing to aid in the systematization of the calculations. The most common way of implementing these calculations is through the use of event trees, computing event probabilities, and incremental consequences. This methodology is flexible enough for several situations, but its generalization to the case of systems of several dams is complex and its implementation in a completely general calculation methodology presents some problems. Retaining the event tree framework, a new methodology is proposed to calculate incremental risks. The main advantage of this proposed methodology is the ease with which it can be applied to systems of several dams: with a single risk model that describes the complete system and with a single calculation the incremental risks of the system can be obtained, being able to allocate the risk of each dam and of each failure mode. The article shows how both methodologies are equivalent and also applies them to a case study.     

The Effects of Memory Structure on Using Rule-Based Expert Systems for Training: A Framework and an Empirical Test*

One justification for eliciting and representing the judgment strategy of an expert in a rule-based expert system (RBES) is to facilitate knowledge transfer to individuals with less expertise. However, prior research suggests complexities and potential problems when using RBESs for training. In this paper, a conceptual framework of user learning from RBESs is presented. It is proposed that learning may be ineffective when the problem representation of the RBES is inconsistent with the user's mental representation of the task-domain knowledge. An experiment was conducted to examine the effects of consistency (inconsistency) between the problem-solving strategy of RBESs and individuals' memory structures. Groups of subjects whose memory structure either matched or did not match two RBESs' problem-solving strategies were examined using an internal control evaluation task. The results suggest that learning was facilitated only for groups with congruence between the RBES's problem-solving strategy and a subject's memory structure.     

A Framework for Examining the Interface between Operations and Information Systems: Implications for Research in the New Millennium*

Even though much research has been published in operations and information systems, both functional areas find their roots in other disciplines. While operations management evolved from operations research in the 1960s, the field of information systems is of more recent vintage and traces its original roots to computer science. Both disciplines now naturally have come closer together as information and process-technology-based changes force manufacturing firms to become more efficient and customer focused. Market and technology-driven e-commerce initiatives that are likely to dominate business strategies in the future cannot be successfully achieved without a successful integration of operations and information systems. In this paper, we present a unifying framework that can be used to better understand the management of the functional interface between operations and information systems. We also categorize and highlight the contributions of the articles that appear in this special research focus issue. Finally, research directions that emerge from our understanding of this interface are outlined in an effort to stimulate further thinking and research that can advance our knowledge of this interface area.     

A Comparative Cancer Risk Evaluation of MTBE and Other Compounds (Including Naturally Occurring Compounds) in Drinking Water in New Hampshire

Methyl tert-butyl ether (MTBE) was added to gasoline in New Hampshire (NH) between 1995 and 2006 to comply with the oxygenate requirements of the 1990 Amendments to the Clean Air Act. Leaking tanks and spills released MTBE into groundwater, and as a result, MTBE has been detected in drinking water in NH. We conducted a comparative cancer risk assessment and a margin-of-safety (MOS) analysis for several constituents, including MTBE, detected in NH drinking water. Using standard risk assessment methods, we calculated cancer risks from exposure to 12 detected volatile organic compounds (VOCs), including MTBE, and to four naturally occurring compounds (i.e., arsenic, radium-226, radium-228, and radon-222) detected in NH public water supplies. We evaluated exposures to a hypothetical resident ingesting the water, dermally contacting the water while showering, and inhaling compounds volatilizing from water in the home. We then compared risk estimates for MTBE to those of the other 15 compounds. From our analysis, we concluded that the high-end cancer risk from exposure to MTBE in drinking water is lower than the risks from all the other VOCs evaluated and several thousand times lower than the risks from exposure to naturally occurring constituents, including arsenic, radium, and radon. We also conducted an MOS analysis in which we compared toxicological points of departure to the NH maximum contaminant level (MCL) of 13 µg/L. All of the MOSs were greater than or equal to 160,000, indicating a large margin of safety and demonstrating the health-protectiveness of the NH MCL for MTBE.     

A Methodology for Estimating Time-of-Day Variations in the Size of a Population Exposed to Risk

Consequence models for the risk assessment of man-made or natural disasters do not ordinarily take into account time-of-day variations in the size of the exposed population. Residential census population statistics are used instead. This paper proposes and illustrates a methodology for using metropolitan travel survey data to estimate the variations in question. Variations are computed from the Washington, D.C. area sample survey statistics on the number of trips taken in and out of different census tracts throughout each workday. Four principal patterns of population variation are identified, corresponding to four types of land use: commercial, residential, shopping/entertainment, and mixed use. Some general implications for consequence analysis are discussed.