Parenting,Uncertainty, and Expert Advice: How Privileged American Families Work with Private Counselors in Their Children's College Race

This article uses privileged families who hire Independent Educational Consultants (IECs) as an instance to examine how privileged parents collaborate with individuals whom they consider educational experts to support their children in the college race. We argue that advantaged parents' anxieties about their children have created a market for IECs who provide expert advice in order to mitigate the uncertainties that these parents experience and to manage various goals that they want to achieve at an important turning point in their children's lives. Drawing primarily on interviews with parents who work with IECs, we introduce the concept of “collaborative cultivation” to analyze the processes whereby advantaged parents rely on the expertise and expert status of private counselors to cope with their and their children's vulnerability in the college race while at the same time preparing their children for the unknown future. The parental method of “concerted cultivation” reveals how elite parents rely on individuals they perceive as experts to establish “bridges” between their own social worlds and the academic worlds that appear to beyond their control. This bridging labor points to the myriad cultural beliefs enacted to justify the child‐rearing goals that privileged parents wish to accomplish by working with IECs.     

Modeling Correlated Discrete Uncertainties in Event Trees with Copulas

Modeling the dependence between uncertainties in decision and risk analyses is an important part of the problem structuring process. We focus on situations where correlated uncertainties are discrete, and extend the concept of the copula‐based approach for modeling correlated continuous uncertainties to the representation of correlated discrete uncertainties. This approach reduces the required number of probability assessments significantly compared to approaches requiring direct estimates of conditional probabilities. It also allows the use of multiple dependence measures, including product moment correlation, rank order correlation and tail dependence, and parametric families of copulas such as normal copulas, t‐copulas, and Archimedean copulas. This approach can be extended to model the dependence between discrete and continuous uncertainties in the same event tree.     

On the Need for Restricting the Probabilistic Analysis in Risk Assessments to Variability

It is common perspective in risk analysis that there are two kinds of uncertainties: i) variability as resulting from heterogeneity and stochasticity (aleatory uncertainty) and ii) partial ignorance or epistemic uncertainties resulting from systematic measurement error and lack of knowledge. Probability theory is recognized as the proper tool for treating the aleatory uncertainties, but there are different views on what is the best approach for describing partial ignorance and epistemic uncertainties. Subjective probabilities are often used for representing this type of ignorance and uncertainties, but several alternative approaches have been suggested, including interval analysis, probability bound analysis, and bounds based on evidence theory. It is argued that probability theory generates too precise results when the background knowledge of the probabilities is poor. In this article, we look more closely into this issue. We argue that this critique of probability theory is based on a conception of risk assessment being a tool to objectively report on the true risk and variabilities. If risk assessment is seen instead as a method for describing the analysts’ (and possibly other stakeholders’) uncertainties about unknown quantities, the alternative approaches (such as the interval analysis) often fail in providing the necessary decision support.     

On Some Recent Definitions and Analysis Frameworks for Risk,Vulnerability, and Resilience

Recently, considerable attention has been paid to a systems‐based approach to risk, vulnerability, and resilience analysis. It is argued that risk, vulnerability, and resilience are inherently and fundamentally functions of the states of the system and its environment. Vulnerability is defined as the manifestation of the inherent states of the system that can be subjected to a natural hazard or be exploited to adversely affect that system, whereas resilience is defined as the ability of the system to withstand a major disruption within acceptable degradation parameters and to recover within an acceptable time, and composite costs, and risks. Risk, on the other hand, is probability based, defined by the probability and severity of adverse effects (i.e., the consequences). In this article, we look more closely into this approach. It is observed that the key concepts are inconsistent in the sense that the uncertainty (probability) dimension is included for the risk definition but not for vulnerability and resilience. In the article, we question the rationale for this inconsistency. The suggested approach is compared with an alternative framework that provides a logically defined structure for risk, vulnerability, and resilience, where all three concepts are incorporating the uncertainty (probability) dimension.     

Integration of Stochastic Effects and Data Uncertainties into the Design of Process Equipment

Stochastic effects and data uncertainties are present in any engineering calculation. Their impact may be particularly important if they concern the design of process equipment. A calculation model for the dynamic behavior of a heat exchanger and procedures to deal with the related uncertainties are presented. Their propagation through the calculation by means of a Monte Carlo approach is shown. The temperature at the heat exchanger outlet and the step response of a sudden variation in the heat exchanger inlet temperature are simulated and evaluated by way of example. It is demonstrated that the inclusion of stochastic effects and uncertainties provides a more reliable basis for design decisions and hence reduces the probability of errors.     

基于不确定因素的食品安全问题频发的伦理分析

不良食品生产行为给社会带来的危害应由食品生产者及相关的政府职能部门负担起相应的伦理及法律责任。政府职能部门责任伦理意识缺失、消费者生命健康意识淡薄及社会道德监督机制缺失,造成了政府监管、公众安全认知、食品生产行业的道德与法律风险等方面的不确定性,成为食品安全事故频发的深层根源。只有通过健全食品安全监管制度、建构政府信用问责机制,提高政府监管者的责任伦理素养,培养公众的生命健康意识和食品安全问题防范意识,建立食品供应链道德信用跟踪系统,才能真正解决食品安全问题。     

Risk‐Cost‐Benefit Analysis for Transportation Corridors with Interval Uncertainties of Heterogeneous Data

Access management, which systematically limits opportunities for egress and ingress of vehicles to highway lanes, is critical to protect trillions of dollars of current investment in transportation. This article addresses allocating resources for access management with incomplete and partially relevant data on crash rates, travel speeds, and other factors. While access management can be effective to avoid crashes, reduce travel times, and increase route capacities, the literature suggests a need for performance metrics to guide investments in resource allocation across large corridor networks and several time horizons. In this article, we describe a quantitative decision model to support an access management program via risk‐cost‐benefit analysis under data uncertainties from diverse sources of data and expertise. The approach quantifies potential benefits, including safety improvement and travel time savings, and costs of access management through functional relationships of input parameters including crash rates, corridor access point densities, and traffic volumes. Parameter uncertainties, which vary across locales and experts, are addressed via numerical interval analyses. This approach is demonstrated at several geographic scales across 7,000 kilometers of highways in a geographic region and several subregions. The demonstration prioritizes route segments that would benefit from risk management, including (i) additional data or elicitation, (ii) right‐of‐way purchases, (iii) restriction or closing of access points, (iv) new alignments, (v) developer proffers, and (vi) etc. The approach ought to be of wide interest to analysts, planners, policymakers, and stakeholders who rely on heterogeneous data and expertise for risk management.     

技术不确定条件下的技术创新投资决策分析

技术不确定性是技术创新投资的主要特点。在技术创新投资模型的基础上引入技术不确定性,从而得到双头垄断企业在技术不确定条件下的最优投资时点。同时,在此基础上分析了技术不确定性及技术不确定的相关度对投资时点的影响。     

（妾子）（次虫）壶铭文集释

剥夺政治权利的内容是不确定的,这种不确定性表现在三个方面,即学界对《刑法》规定的剥夺政治权利内容的阐释存在诸多差异甚至是针锋相对;历史上不同时期的规范性文件规定的剥夺政治权利或相类似刑罚方法的内容不一致;被剥夺政治权利者除在刑罚执行期间丧失《刑法》规定的权利之外,还将在刑罚执行期间及之后一定时间内甚至终身丧失其他一些内容广泛、性质多样的非政治权利。     

价格与技术不确定条件下的发电商碳捕获投资模型及分析

碳捕获与储存技术是降低碳排放的有效方法之一。论文针对碳价和碳捕获技术不确定的情况,构建双重不确定条件下的碳捕获技术投资模型,并在模型求解基础上进行了数值仿真分析,其分析结果表明:1)碳价的波动性将延迟碳捕获技术投资,若碳价的波动性足够大,发电商会选择不投资;2)碳捕获技术进步也将延迟投资,但政策性补贴将抵消该投资延迟。