A conceptual Object-Oriented Bayesian Network (OOBN) for modeling aircraft carrier-based UAS safety risk

This paper illustrates the conceptual development of a demonstration Object-Oriented Bayesian Network (OOBN) to integrate the hazards associated with an experimental Unmanned Aircraft System (UAS) planned for deployment from an aircraft carrier. The final Air/Ship Integration (A/SI) demonstration model is characterized by a top-level Bayesian network model with nine sub-nets comprising 70 causal factors with 15 mitigations. With the creation of a probabilistic model, inferences about changes to the states of the causal factors given the presence or absence of controls or mitigations can be ascertained. These inferences build on qualitative reasoning and enable an analyst to identify the most prominent causal factor groupings leading to a prioritization of the most influential causal factors. Mitigation effects can be systematically studied and assessed. The A/SI OOBN demonstration model illustrates the construction of an integrative safety risk model that may be used to compute a higher-order system mishap probability for an experimental UAS that interacts with ship operations in a highly severe, dynamic sea environment. In addition to computing mishap probabilities, the Bayesian approach may also be used to support control contingency management for possible mitigation implementation.     

An inductive reasoning approach for building system safety risk models of aviation accidents

An inductive reasoning approach is employed to develop a prototype hybrid decision support tool whose main objective is to build probabilistic causal models representing the safety risk involved in aviation accidents. In this context, 15 aircraft accidents representative of five major accident types are selected to build an initial seed for the case‐base of the prototype tool. Consequently, within each individual accident model, main clusters of causal factors are identified for inclusion in the initial seed, thereby improving, both quantitatively and qualitatively, the case‐base of the prototype tool. A new methodology developed specifically for indexing aviation accidents into databases is used for indexing the initial seed into the case‐base of the tool. The resulting product is a highly customized conversational decision support tool that provides solution possibilities in the form of probabilistic causal models of accident scenarios retrieved and ranked according to their similarity to the current accident that the intended user investigates.     

Aircraft safety analysis using clustering algorithms

In recent years, there have been many cost-benefit studies on aviation safety, which deal mainly with economic issues, omitting some strictly technical aspects. This study compares aircraft accidents in relation to the characteristics of the aircraft, environmental conditions, route, and traffic type. The study was conducted using a database of over 1500 aircraft accidents worldwide, occurring between 1985 and 2010. The data were processed and then aggregated into groups, using cluster analysis based on an algorithm of partition binary ‘Hard c means.’ For each cluster, the ‘cluster representative’ accident was identified as the average of all the different characteristics of the accident. Moreover, a ‘hazard index’ was defined for each cluster (according to annual movements); using this index, it was possible to establish the dangerousness of each ‘cluster’ in terms of aviation accidents. Obtained results allowed the construction of an easy-to-use predictive model for accidents using multivariate analysis.     

A decision analytic approach for technology portfolio prioritization: aviation safety applications

A decision analytic approach for evaluating new aviation safety products and technologies is developed and demonstrated to consolidate five existing program assessment metrics to develop a unified metric that simultaneously considers the relative importance and contribution of each. This allows for a meaningful and objective evaluation and comparison of the National Aeronautics and Space Administration (NASA) Aviation Safety Program (AvSP) advanced aeronautical products and technologies. The resulting decision model is referred to as the Composite Program Assessment Score (CPAS). The CPAS includes the five existing metrics; technical development risk, implementation risk, fatal accident rate reduction, safety benefits and cost, and safety risk reduction, which are each defined and quantified by different sources. The CPAS involves the scaling and combination of these individual metrics. In this paper, two alternative combinatorial modeling approaches to calculate the CPAS are presented. The weighted sum model and an additive value theory model are compared and contrasted. The resulting CPAS metrics allow an overall comparison of all 48 of the NASA AvSP products and technologies. Currently CPAS is based on preliminary weight measures from subject matter experts to reflect the relative importance of each metric. Actual case studies of both linear and non‐linear value functions are demonstrated.     

Mapas cognitivos difusos para la selección de proyectos de tecnologías de la información

The evaluation and selection of information technology projects is one of the most complex decision-making processes. To achieve alignment between projects and strategy is very important to analyze the goals and processes inside the organization. This paper presents a model for the selection of IT projects, based on the analysis of enterprise architecture. The proposal makes use of causal relations modelling using fuzzy cognitive maps. This approach facilitates the evaluation and selection of a project portfolio in order to achieve the desired future state of enterprise architecture. The model represents an attractive alternative or complement to existing methods in the evaluation of IT initiatives. A case study showing the application of the model to a professional services organization for the transformation of the enterprise architecture is shown.     

A Collaborative Multi‐criteria Decision Making Technique for Risk Factor Prioritization

Organizations are a collection of individuals, and often a disastrous organizational accident involves contributions from several technical/environmental factors and actors throughout the system over time. This paper illustrates the efficiency of a new collaborative decision making technique that could assist a group of executive decision makers in identifying, analyzing, evaluating and prioritizing significant organizational system risks. The results from the collaborative technique when applied to real world risk intensive situations, such as aviation safety risk management, are compared with those obtained by using existing notable techniques. For the case examples shown, the number of expert judgments is reduced by up to 80%. Advantages and limitations of the proposed modeling approach for collaborative decision making are discussed.     

The Deepwater Horizon explosion: non-technical skills,safety culture,and system complexity

The explosion and destruction of the Deepwater Horizon (DH) was a watershed moment for safety management in the US oil and gas industry. The 2011 National Oil Spill Commission investigation identified a range of operational behaviours and underlying safety management problems that were causal to the mishap. Yet, to date these have not been systematically considered within a human factors framework. To achieve this, we draw upon two applied psychology domains that are highly influential within safety research. First, we apply non-technical skills (NTS) (social and cognitive skills that underpin safe performance in complex work environments) theorem to understand operational activities in the lead-up and occurrence of the well blowout. NTS research is used to develop interventions for training and observing safety behaviours (e.g. decision-making, teamwork). Second, we apply safety culture theory to understand how the organisational and industry environment shaped the management of risk. Safety culture research is used to understand and change the socio-technical constraints and enablers of safety activity in high-risk workplaces. Finally, to integrate these perspectives, we take a systems-thinking perspective to understand the mishap. A common critique of accident narratives is their failure to systematically consider how the components of an incident interact together to escalate risk. From a systems-thinking perspective, understanding the interactions leading to the DH mishap is crucial for ensuring interventions are effective in preventing future mishaps. We develop an accident model that captures the various interactions and system factors leading to the blowout.     

Recent Developments in the Aviation Insurance Industry

The aviation industry has been hard hit in recent years. While there are numerous factors that have contributed to the industry's dilemma, rising and volatile insurance premiums—particularly after the events of 9/11—have posed a particular problem for many airline managers. Despite a general trend for accident rates involving commercial passenger airplanes to decrease as aviation technology has advanced over the years and airplanes have become safer, the aviation insurance market has been far from stable. This article provides an overview of how the aviation insurance industry works and how it has changed in recent years. We take a look at how the risk is spread between insurers, how insurers treat deliberate acts of violence, and lastly, how insurers price the risk. Our article shows that the aviation insurance market has undergone considerable changes in recent years and that it has adjusted to the post-9/11 aviation insurance realities being reasonably ready to handle events of an even more catastrophic magnitude.     

卖空威胁、公司融资扩张战略与违规行为

利用中国推出融资融券业务的准自然实验机会,本文以2010—2016年中国A股上市公司为样本,使用双重差分模型研究放松卖空限制对企业违规行为的影响。研究发现,融券业务允许市场中的知情投资者挖掘企业负面信息进行卖空,形成有效的卖空威胁,从而震慑企业,显著降低其发生违规行为的概率。具体而言,卖空交易行为跟企业违规行为的严重程度正相关;卖空交易行为能显著降低企业的超额收益率;当企业有进一步融资需求或并购扩张战略需求时,卖空威胁对企业的震慑作用更为显著。研究表明,融券业务带来的卖空威胁是企业外部监管的重要机制。     

Participatory safety barrier analysis: a case from the offshore maritime industry

This paper argues that a participatory approach directly involving employees in safety barrier analysis can provide ‘added value’ to traditional barrier analyses. Employee participation (EP) could motivate employees to use their knowledge, suggest improvement measures and express their concerns. EP has not received much attention from safety researchers, although one may find several indirect arguments for EP informing the influential safety theoretical perspectives. An example of how participatory safety barrier analysis can be completed and what can be accomplished through such an approach is illustrated via a case study from an offshore logistics chain, and by an analysis of barriers that should prevent collisions between supply vessels and offshore installations. Such collisions could be the initiating event for a major accident. The empirical foundation for the paper is a hazard identification technique session, group and individual interviews, document studies and two search conferences involving approximately 150 participants. It is argued that a participatory approach to safety barrier analysis can reveal ‘holes’ in the defences that otherwise could have gone overlooked, and contribute to the generation of contextualized, definite measures that could strengthen a safety barrier system.     

Adaptive Bayesian Networks for quantitative risk assessment of foreign body injuries in children

Injuries due to foreign body (FB) aspiration/ingestion/insertion represent a common public health issue in paediatric patients, which causes significant morbidity and mortality. The aim of this study is to present a Bayesian Network (BN) model for the identification of risk factors for FB injuries in children and provide their quantitative assessment. Combining a priori knowledge and observed data, a BN learning algorithm was used to generate the pattern of the relationships between possible causal factors of FB injuries. Finally, the BN was used for making inference on scenarios of interest, providing, for instance, the risk that an accident caused by a spherical object swallowed by a male child aged five while playing leads to hospitalization. BNs as a tool for quantitative risk assessment may assist in determining the hazard of consumer products giving an insight into their most influential specific features on the risk of experiencing severe injuries.     

Lévy flight-based real-time bird strike risk assessment for airports

Bird strike is a terrible but common incident in aviation. There is, however, a lack of systematic approaches for real-time bird strike risk assessment at present. This paper provides a novel method for bird strike risk assessment at airports with the detected data (e.g. data from radar systems), including the estimation of bird strike probability and collision severity. The Lévy flight model, an influential random walk model in bird foraging behaviour research, is adopted for the bird strike probability estimation. After dividing the area around the airport into a square matrix, the Lévy flight model is modified by the Chapman Kolmogorov equation. Meanwhile, the estimation of collision severity is based on the bird mass. The proposed method is applied to Dalian Zhoushuizi Airport with simulated bird data. The simulated results demonstrate the efficiency and real-time performance of our method.     

One size fits all? Safety management regulation of ship accidents and personal injuries

Safety management regulation is an important supplement to market forces to establish a sufficient safety level in high-risk industries. The accident statistics in Norwegian maritime passenger transportation display a paradox: personal injuries have decreased while ship accidents have increased in the period during which safety management has been regulated (the International Safety Management Code was effectuated in the late 1990s). We interview regulators, shipping company management, and crewmembers about their practices and opinions regarding safety management regulation and use these data to explore how this regulation influences safety management practices to prevent different types of accidents. This study underlines earlier research showing that regulation serves to ‘raise the bar’ by heightening the industry levels of safety investments and organizational safety awareness. In addition, our results suggest that safety management regulation in maritime transportation is mostly effective for preventing personal injuries in cases in which the personal have sufficient time and resources available, and the procedures are consistent with seafarers’ professional values. For ship accidents, such as groundings, other causal factors come into play. We find that the negative consequences of regulation (proceduralization) in particular influence the performance of safety-critical tasks, such as navigation. This may explain why personal injuries have decreased while ship accident frequencies have continued to increase in spite of the regulations aimed at improving safety.     

基于季节时间序列模型的民航客运需求预测分析

随着经济的不断发展，人们的生活水平不断提升，消费理念也有很大改变，民用航空运输业也获得了飞速发展，越来越多的人选用航空这一交通方式出行。文章主要介绍了季节时间序列模型，并使用1998年1月至2013年5月的月度数据建立SARIMA模型，并对2013年的民航客运量进行预测分析。通过使用SARIMA模型对我国民航客运量进行预测，以期对民航企业制定合理的运营决策提供一些参考。     

Professional judgment and accounting standards

This essay explores relationships between accounting standards and people's inferences and judgments. Acknowledging that Demski's impossibility theorem implies that the standards will be “social-preference incomplete”, the paper shows that they are also “decision-procedure incomplete” on three levels. These levels correspond to three kinds of professional judgment: semantic, pragmatic and institutional. The investigation facilitates understanding of (1) how accountants exercise judgment and deduction in applying incomplete standards; (2) how financial statement readers use the incomplete standards to draw deductive inferences from financial reports which are based on accountants' judgments; (3) the special kinds of judgment required of standard setters; and (4) the meaning and extent of professional liability, given the relationships identified between accounting standards, inferences and judgments.     

Risk management; a behavioural perspective

The risk contributor is usually regarded as responsible for risk mitigation and accident compensation, especially when the risk is due to the operation of a commercial company. The culpability of risk has resulted in several approaches to safety management. Risk management based on quantitative risk analysis (QRA) emerged in the defence and nuclear industry during and after Second World War and is by now introduced in almost every industry with high-risk potential. During this period, risk analysis and management as a profession has evolved considerably. Technical failures and operator errors used to be considered as the prime causes of accidents in the early days of risk analysis. Based on investigations of major accidents in the latter half of the last century, poor safety culture and mismanagement were introduced as possible additional causes of major accidents. Human error in decision-making is, however, rarely quantified and thus not included in QRA. Knowledge from the experimental analysis of behaviour is absent in practical or operational risk management. This paper advocates an approach to risk management where the decision part of the chain of events is explicitly included. The behavioural perspective introduced implies that the application of experimentally based behaviour science and QRA both should be pursued, mainly because QRA is a strong, and probably the best, defence against decision errors. In an operational situation, management must do trade-offs between objectives where safety is but one of several considerations. When the risk is not quantified, safety loses out to other more easily quantified objectives of a company. The fatal decision error that led to the Challenger accident is used as an example.     

Addressing unobserved endogeneity bias in accounting studies: control and sensitivity methods by variable type

Together with their associated statistical routines, this paper describes the control and sensitivity methods that can be employed by accounting researchers to address the important issue of unobserved (omitted) variable bias in regression and matching models according to the types of variables employed. As with other social science disciplines, an important and pervasive issue in observational (non-experimental) accounting research is omitted variable bias (endogeneity). Causal inferences for endogenous explanatory variables are biased. This occurs in regression models where an unobserved (confounding) variable is correlated with both the dependent (outcome) variable in a regression model and the causal explanatory (often a selection) variable of interest. The Heckman treatment effect model has been widely employed to control for hidden bias for continuous outcomes and endogenous binary selection variables. However, in accounting studies, limited (categorical) dependent variables are a common feature and endogenous explanatory variables may be other than binary in nature. The purpose of this paper is to provide an overview of contemporary control methods, together with the statistical routines to implement them, which extend the Heckman approach to binary, multinomial, ordinal, count and percentile outcomes and to where endogenous variables take various forms. These contemporary methods aim to improve causal estimates by controlling for hidden bias, though at the price of increased complexity. A simpler approach is to conduct sensitivity analysis. This paper also presents a synopsis of a number of sensitivity techniques and their associated statistical routines which accounting researchers can employ routinely to appraise the vulnerability of causal effects to potential (simulated) unobserved bias when estimated with conventional regression and propensity score matching estimators.     

Bayesian comparison of several continuous time models of the Australian short rate

This paper provides an empirical analysis of a range of alternative single‐factor continuous time models for the Australian short‐term interest rate. The models are nested in a general single‐factor diffusion process for the short rate, with each alternative model indexed by the level effect parameter for the volatility. The inferential approach adopted is Bayesian, with estimation of the models proceeding through a Markov chain Monte Carlo simulation scheme. Discrimination between the alternative models is based on Bayes factors. A data augmentation approach is used to improve the accuracy of the discrete time approximation of the continuous time models. An empirical investigation is conducted using weekly observations on the Australian 90 day interest rate from January 1990 to July 2000. The Bayes factors indicate that the square root diffusion model has the highest posterior probability of all models considered.