Communicating uncertainty in national security intelligence: Expert and nonexpert interpretations of and preferences for verbal and numeric formats

Organizations in several domains including national security intelligence communicate judgments under uncertainty using verbal probabilities (e.g., likely) instead of numeric probabilities (e.g., 75% chance), despite research indicating that the former have variable meanings across individuals. In the intelligence domain, uncertainty is also communicated using terms such as low, moderate, or high to describe the analyst's confidence level. However, little research has examined how intelligence professionals interpret these terms and whether they prefer them to numeric uncertainty quantifiers. In two experiments (N = 481 and 624, respectively), uncertainty communication preferences of expert (n = 41 intelligence analysts in Experiment 1) and nonexpert intelligence consumers were elicited. We examined which format participants judged to be more informative and simpler to process. We further tested whether participants treated verbal probability and confidence terms as independent constructs and whether participants provided coherent numeric probability translations of verbal probabilities. Results showed that although most nonexperts favored the numeric format, experts were about equally split, and most participants in both samples regarded the numeric format as more informative. Experts and nonexperts consistently conflated probability and confidence. For instance, confidence intervals inferred from verbal confidence terms had a greater effect on the location of the estimate than the width of the estimate, contrary to normative expectation. Approximately one-fourth of experts and over one-half of nonexperts provided incoherent numeric probability translations for the terms likely and unlikely when the elicitation of best estimates and lower and upper bounds were briefly spaced by intervening tasks.     

Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model

Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention [CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations.     

Cases of real-life policies related to risk: How can they enhance risk analysis and risk science?

Policies on risk constitute a core topic of risk analysis and risk science, and it is common at risk conferences to present real-life cases of such policies, for example related to the handling of climate change and pandemics. Although these are of broad interest, showing how important issues in society are dealt with, it can be questioned to what extent and how these cases contribute to enhancing risk analysis and risk science. The present paper addresses this concern. It is argued that, in order to learn from the cases, they need in general to be more thoroughly followed up with discussions of concepts, principles, approaches, and methods for assessing, characterizing, communicating and handling risk. Describing a governmental policy on, for example, the handling of COVID-19 is a point of departure for interesting discussions concerning its justification and performance, in particular in relation to risk and the most updated knowledge from the risk analysis field. Such discussions are, however, often lacking. The paper points to some key obstacles and challenges for the learning process, including the difficulty of distinguishing between policies, policy analysis, and politics.     

企业效益评价因子分析模型及应用

本文将因子分析方法应用于社会科学领域的经济评价系统。从一些错综复杂的经济现象中找出少数几个主因子,每一个主因子代表经济变量之间相互依赖的一种经济作用,抓住这些主因子就可以帮助我们对复杂的经济问题进行分析和解释。在简要介绍因子模型以后,从实证分析的角度,选取深沪股市高科技板块 2 0家上市公司1998年中期的一系列经济评价指标,采用因子分析方法,并借助社会科学统计软件SPSS,进行企业的综合经济效益分析评价,得出因子分析是一种进行企业综合效益评价的有效方法。     

企业信息化指数测算方法研究

应用模糊集合论和主成分分析的方法对企业信息化进行了综合评判,引进了一种新的隶属函数对数据进行了无量纲化处理并确定了各指标权重,建立了山东省企业信息化指数模型。利用此方法对 5家企业信息化进行综合评价,得到了满意的结果。     

水力发电企业整体效益评价及实例

针对水力发电企业的整体经济效益难以进行评价的问题,提出了以灰色关联度技术为支撑,选择数量模型评价方法,应用层次分析法的有关技术和思想,建立起具有可操作性的评价模型及评价体系。     

我国股票市场指数及指数证券投资组合

运用主成分分析法分析我国上海和深圳两个交易所几个市场指数对市场变化的反应情 况, 结果表明两个股票市场的综合指数和A 股指数可以反应市场的变化, 而其他指数不能反 映各自代表的股票市场变化. 由于这4 个指数都不是一个好的投资组合, 要得到与指数一致的 投资收益需要构造指数投资组合. 本文利用多因子定价模型, 结合统计分析和优化方法, 从每 个股票市场上选取20 余支股票, 经过适当的组合就可以得到与指数一致的收益.     

基于多智能体的复杂信息系统开发方法研究

提出一种将组织学与多智能主体技术相结合建造复杂信息系统的开发方法. 该方法的基 本思想是首先将复杂信息系统看成一个多主体组织, 依据组织学原理分析多主体系统组织特 性, 建立多主体组织模型, 从而形成多主体系统宏观结构和功能需求的严格规范; 其次, 根据现 有技术和可用资源确定承担各类组织职能的多个主体, 由此确定了各主体的职责和粒度; 然 后, 依据组织模型, 采用信念2愿望2意图(BD I) 结构设计各类主体的概念模型, 为系统的机上实 现建立清晰的可操作的开发模式; 最后, 将所设计的概念模型在协同工作平台(MBO S) 上编程 实现, 快速获得复杂信息系统的原型系     

Measurement error, skewness, and risk analysis: coping with the long tail of the distribution.

Probabilistic risk analyses often construct multistage chance trees to estimate the joint probability of compound events. If random measurement error is associated with some or all of the estimates, we show that resulting estimates of joint probability may be highly skewed. Joint probability estimates based on the analysis of multistage chance trees are more likely than not to be below the true probability of adverse events, but will sometimes substantially overestimate them. In contexts such as insurance markets for environmental risks, skewed distributions of risk estimates amplify the "winner's curse" so that the estimated risk premium for low-probability events is likely to be lower than the normative value. Skewness may result even in unbiased estimators of expected value from simple lotteries, if measurement error is associated with both the probability and pay-off terms. Further, skewness may occur even if the error associated with these two estimates is symmetrically distributed. Under certain circumstances, skewed estimates of expected value may result in risk-neutral decisionmakers exhibiting a tendency to choose a certainty equivalent over a lottery of equal expected value, or vice versa. We show that when distributions of estimates of expected value are, positively skewed, under certain circumstances it will be optimal to choose lotteries with nominal values lower than the value of apparently superior certainty equivalents. Extending the previous work of Goodman (1960), we provide an exact formula for the skewness of products.     

Sensitivity Analysis for Importance Assessment

We review briefly some examples that would support an extended role for quantitative sensitivity analysis in the context of model-based analysis (Section 1). We then review what features a quantitative sensitivity analysis needs to have to play such a role (Section 2). The methods that meet these requirements are described in Section 3; an example is provided in Section 4. Some pointers to further research are set out in Section 5.