Fault and Event Tree Analyses for Process Systems Risk Analysis: Uncertainty Handling Formulations

Quantitative risk analysis (QRA) is a systematic approach for evaluating likelihood, consequences, and risk of adverse events. QRA based on event (ETA) and fault tree analyses (FTA) employs two basic assumptions. The first assumption is related to likelihood values of input events, and the second assumption is regarding interdependence among the events (for ETA) or basic events (for FTA). Traditionally, FTA and ETA both use crisp probabilities; however, to deal with uncertainties, the probability distributions of input event likelihoods are assumed. These probability distributions are often hard to come by and even if available, they are subject to incompleteness (partial ignorance) and imprecision. Furthermore, both FTA and ETA assume that events (or basic events) are independent. In practice, these two assumptions are often unrealistic. This article focuses on handling uncertainty in a QRA framework of a process system. Fuzzy set theory and evidence theory are used to describe the uncertainties in the input event likelihoods. A method based on a dependency coefficient is used to express interdependencies of events (or basic events) in ETA and FTA. To demonstrate the approach, two case studies are discussed.     

The Event: An Underexamined Risk Concept

Some of the terms used in risk assessment and management are poorly and even contradictorily defined. One such term is “event,” which arguably describes the most basic of all risk‐related concepts. The author cites two contemporary textbook interpretations of “event” that he contends are incorrect and misleading. He then examines the concept of an event in A. N. Kolmogorov's probability axioms and in several more‐current textbooks. Those concepts are found to be too narrow for risk assessments and inconsistent with the actual usage of “event” by risk analysts. The author goes on to define and advocate linguistic definitions of events (as opposed to mathematical definitions)—definitions constructed from natural language. He argues that they should be recognized for what they are: the de facto primary method of defining events.     

A joint model for longitudinal data profiles and associated event risks with application to a depression study

Summary.  In many longitudinal studies, a subject's response profile is closely associated with his or her risk of experiencing a related event. Examples of such event risks include recurrence of disease, relapse, drop-out and non-compliance. When evaluating the effect of a treatment, it is sometimes of interest to consider the joint process consisting of both the response and the risk of an associated event. Motivated by a prevention of depression study among patients with malignant melanoma, we examine a joint model that incorporates the risk of discontinuation into the analysis of serial depression measures. We present a maximum likelihood estimator for the mean response and event risk vectors. We test hypotheses about functions of mean depression and withdrawal risk profiles from our joint model, predict depression from updated patient histories, characterize associations between components of the joint process and estimate the probability that a patient's depression and risk of withdrawal exceed specified levels. We illustrate the application of our joint model by using the depression data.     

The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models

Summary.  We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.     

日本天皇裕仁与卢沟桥事变

日本天皇裕仁并没有参与策划卢沟桥事变的阴谋,但他清楚地知道日本政府和军部侵略华北的行动计划,而且客观上支持了这些计划的实施;事变发生后,他又积极参与了事变处理,推动事态不断扩大。     

Using Incident Response Trees as a Tool for Risk Management of Online Financial Services

The article introduces the use of probabilistic risk assessment for modeling the incident response process of online financial services. The main contribution is the creation of incident response trees, using event tree analysis, which provides us with a visual tool and a systematic way to estimate the probability of a successful incident response process against the currently known risk landscape, making it possible to measure the balance between front‐end and back‐end security measures. The model is presented using an illustrative example, and is then applied to the incident response process of a Swedish bank. Access to relevant data is verified and the applicability and usability of the proposed model is verified using one year of historical data. Potential advantages and possible shortcomings are discussed, referring to both the design phase and the operational phase, and future work is presented.     

Knowledge Management Using Information Technology: Determinants of Short‐Term Impact on Firm Value*

The importance of knowledge management (KM) processes for organizational performance is now well recognized. Seeking to better understand the short‐term impact of KM on firm value, this article focuses on public announcements of information technology (IT)‐based KM efforts, and uses cumulative abnormal return (CAR) associated with an announcement as the dependent variable. This article employs a contingency approach, arguing that the KM announcement would have a positive short‐term impact on firm value in some conditions but not in others. Thus, it pursues the following research question: What are the effects of contextual factors on the CAR associated with the announcement of an IT‐based KM effort? Specific hypotheses are proposed based on information‐processing theory, organizational learning theory, the knowledge‐based theory of the firm, and the theory of knowledge creation. These hypotheses link CARs to alignment between industry innovativeness and the KM process, alignment between firm efficiency and the KM process, firm‐specific instability, and firm diversification. The empirical study utilizes secondary data on 89 KM announcements from 1995 to 2002. The results largely support the hypotheses. Overall, this article provides empirical support for the theory‐based arguments, and helps develop a contingency framework of the effectiveness of KM efforts.     

Tightening the leash after a threat: A multi-level event study on leadership behavior following the financial crisis

This paper presents the results of a multi-level event study of the effects of the 2008 financial crisis on leadership behavior. Following assumptions from the threat-rigidity hypothesis, we expect that across firms and countries, this crisis led to an increase in directive leadership. In line with this hypothesis, we also anticipate that this change is context-specific. The impact of the 2008 financial crisis on the change in directive leadership is analyzed for over 20,000 managers in 980 organizations across 36 countries. We find that the financial crisis went along with a significant increase in directive leadership, and that this effect was stronger in the manufacturing sector, and in countries with a high degree of power distance. Our results support the threat-rigidity hypothesis, and contribute to leadership research by showing that the context is not only a moderator but actually shapes leadership behavior. This opens up a new avenue of leadership research where context is an antecedent of leadership behavior more generally, and where the methodological set-up allows for causal inference.     

Do all roads lead to Rome? Effect of educational trajectories on educational transitions

“Non-traditional” educational trajectories are increasingly common among American students. This study assesses the implications of this phenomenon for inequality in educational attainment. A proper account of educational trajectories requires simultaneous consideration of qualitatively different types of destinations within educational transitions, of the timing at which different transitions occur, and of the sequence of events within educational levels. To examine “traditional” and “non-traditional” pathways through post-secondary education, this study relies on detailed educational histories from the National Longitudinal Survey of Youth 1979–2002. Findings reveal that deviations from a traditional trajectory are widespread, are more frequent among students who enrolled in less selective colleges, and also among socioeconomically and academically disadvantaged students. Results show that following a “non-traditional” pathway reduces students’ chances to enroll in college and to complete a post-secondary degree. In the case of bachelor's degree completion, most of the observed gap among students from different socioeconomic backgrounds is accounted for the different trajectories students follow. This study demonstrates that a fine-grained analysis of students’ trajectories improves our understanding of the persistent socioeconomic disparities in educational attainment.     

利率政策对股市个人投资者行为影响的实证检验

以中国部分基本利率调整为研究对象,采用事件研究分析法,对相关时间段内的股市个人投资者交易频率和交易额度的累积差异情况进行了计算。结果显示利率变动前后投资者的交易频率和交易额度均有显著变化,但变化时期短,从而验证了利率政策对个体投资者行为有显著影响,同时表明了投资者行为存在非理性特征,揭示出中国证券市场存在非有效性,为中国宏观货币政策的调整提供借鉴。