Incident Command Skills in the Management of an Oil Industry Drilling Incident: a Case Study

The successful management of a complex, hazardous event in many domains demands a high level of incident command skills. In the oil and gas exploration and production industry, these skills were required by members of an Incident Management Team (IMT) established to respond the failure of a drilling riser in the Gulf of Mexico. When an incident occurs, members of an industrial IMT form an interdisciplinary, interdependent, but ad‐hoc team. As actual experience of dealing with major incidents of this nature is relatively rare, IMT members have to rely on emergency exercises in training, along with existing domain‐specific knowledge. Following a serious incident on an offshore drilling rig, semi‐structured interviews with the on‐shore strategic and tactical level IMT members (n=7) were conducted. These interviews have resulted in the identification and definition of incident command skills for members of an industrial IMT, namely decision making, situation awareness, communication, leadership, and teamwork, all of which can be affected by stress, as well as organisational factors that influenced the outcome of the incident. Limitations in current incident management training were identified, namely the need for specific incident command skills training. A framework is suggested around which specific incident command skills training can be structured. Key learnings from this case study are also presented which can provide guidance for the training and preparation of industrial incident management teams.     

Book Reviews

Books Reviewed: Christopher Hood, Henry Rothstein and Robert Baldwin, The Government of Risk: Understanding Risk Regulation Regimes Kathleen J. Thierney, Michael K. Lindell, and Ronald W. Perry, Facing the Unexpected: Disaster Preparedness and Response in the United States Frank‐Jürgen Richter, Redesigning Asian Business: In the Aftermath of Crisis Rhona Flin and Kevin Arbuthnot (Eds), Incident Command: Tales From the Hot Seat Rachel Briggs (Ed.), The Unlikely Counter‐Terrorists     

Goal programming to model human decision making in ultimatum games

Goal Programming (GP) is applied to modelling the decision making processes in the well‐known Ultimatum Game and some of its variations. The decision model for a player is a Chebychev GP model that balances her individual desires with the mental model she has of the desires of other relevant players. Fairness is modelled as a universal mechanism, allowing players to differ in their belief of what a fair solution should be in any particular game. The model's conceptual framework draws upon elements considered of importance in the field of cognitive neuroscience, and results from the field of psychology are used to further specify the types of goals in the model. Computer simulations of the GP models, testing a number of Ultimatum, Dictator and Double‐Blind Dictator Games, lead to distributions of proposals made and accepted that correspond reasonably well with experimental findings.     

NDM-Based Cognitive Agents for Supporting Decision-Making Teams

Naturalistic decision making (NDM) focuses on how people actually make decisions in realistic settings that typically involve ill-structured problems. Taking an experimental approach, we investigate the impacts of using an NDM-based software agent (R-CAST) on the performance of human decision-making teams in a simulated C3I (Communications, Command, Control and Intelligence) environment. We examined four types of decision-making teams with mixed human and agent members playing the roles of intelligence collection and command selection. The experiment also involved two within-group control variables: task complexity and context switching frequency. The result indicates that the use of an R-CAST agent in intelligence collection allows its team member to consider the latest situational information in decision making but might increase the team member's cognitive load. It also indicates that a human member playing the role of command selection should not rely too much on the agent serving as his or her decision aid. Together, it is suggested that the roles of both humans and cognitive agents are critical for achieving the best possible performance of C3I decision-making teams: Whereas agents are superior in computation-intensive activities such as information seeking and filtering, humans are superior in projecting and reasoning about dynamic situations and more adaptable to teammates' cognitive capacities. This study has demonstrated that cognitive agents empowered with NDM models can serve as the teammates and decision aids of human decision makers. Advanced decision support systems built upon such team-aware agents could help achieve reduced cognitive load and effective human-agent collaboration.     

From Intercrisis to Intracrisis Learning

This article focuses on collective learning during crises, i.e., intracrisis learning. Intracrisis learning is challenging because decisionmakers must act in a short time frame under conditions of cognitive limitations and political pressure. Intracrisis learning is also complicated by the fact that it usually involves a network of responders, rather than a single organization. The article examines the role of a structural framework, the Incident Command System, in mediating this challenge. The article also describes an example of intracrisis learning, where responders learned from one another, and via virtual experiences, information systems, learning forums, and standard operating procedures.     

直觉模糊S-粗决策模型及应用

基于直觉模糊S-粗集理论,提出双向直觉模糊S-粗决策模型.首先给出指标值的量化方法,将目标矩阵进行标准化处理.其次,建立直觉模糊S-粗决策的上一决策和下一决策优化模型,并给出直觉模糊S-粗决策算法及详细步骤.最后,以空袭目标为例,详细研究了目标威胁程度评估过程.结果表明,直觉模糊S-粗决策模型能够综合处理决策因素的定性与定量因素,得到的决策结果综合性能最优.所得的排序结果真实、准确地反映了实际情况.     

Strategies for designing distributed systems: case studies in the design of an air traffic management system

The air traffic management system in the USA is an example of a distributed problem-solving system. It has elements of both cooperative and competitive problem-solving. It includes complex organizations such as Flight Operations Centers, the FAA Air Traffic Control Systems Command Center (ATCSCC), and traffic management units at en route centers that focus on daily strategic planning, as well as individuals concerned more with immediate tactical decisions (such as air traffic controllers and pilots). The design of this system has evolved over time to rely heavily on the distribution of tasks and control authority in order to keep cognitive complexity manageable for any one individual operator, and to provide redundancy (both human and technological) to serve as a safety net to catch the slips or mistakes that any one person or entity might make. Within this distributed architecture, a number of different conceptual approaches have been applied to deal with cognitive complexity and to provide redundancy. These approaches can be characterized in terms of the strategy for distributing: (1) control or responsibility, (2) knowledge or expertise, (3) access to data, (4) processing capacity, and (5) goals and priorities. This paper will provide an abstract characterization of these alternative strategies for distributing work in terms of these 5 dimensions, and will illustrate and evaluate their effectiveness in terms of concrete realizations found within the National Airspace System.     

Adaptively Rational Learning

Research on adaptive rationality has focused principally on inference, judgment, and decision-making that lead to behaviors and actions. These processes typically require cognitive representations as input, and these representations must presumably be acquired via learning. Nonetheless, there has been little work on the nature of, and justification for, adaptively rational learning processes. In this paper, we argue that there are strong reasons to believe that some learning is adaptively rational in the same way as judgment and decision-making. Indeed, overall adaptive rationality can only properly be assessed for pairs of learning and decision processes. We thus present a formal framework for modeling such pairs of cognitive processes, and thereby assessing their adaptive rationality relative to the environment and the agent’s goals. We then use this high-level formal framework on specific cases by (a) demonstrating how natural formal constraints on decision-making can lead to substantive predictions about adaptively rational learning and representation; and (b) characterizing adaptively rational learning for fast-and-frugal one-reason decision-making.     

Perceptual control architecture for cyber–physical systems in traffic incident management

Based on Perceptual Control Theory, we study the problem of unified modeling for incompatible approaches of Cyber–Physical Systems (CPSs). Inspired by the effective organization of living systems structure accommodating heterogeneous information processing and environmental interaction, we propose Perceptual Control Architecture of CPSs, and take Traffic Incident Management systems as the modeling research carrier. Throughout the structure of Traffic Incident Management systems, the hierarchical negative feedback is constituted by perceptual and behavioral loops to ensure a mechanism of intelligence behavior. The internal representation is categorized into two intelligent spaces: physical-reflex space and cyber-virtual space. In physical-reflex space, the sensing-actuation mapping of objective world is built, through four levels of distributed traffic infrastructure. In cyber-virtual space, subjective decision using Bayesian reasoning network is defined by three levels: principles, interrelated factors and situation assessment. Through evaluation of field operation in Dalian, the Traffic Incident Management under the developed architecture shows a considerable reduction in response time as well as assessment inaccuracy. The test results explicated the effectiveness of the architecture on integrating complex Cyber–Physical functions. Besides transportation systems, the modeling approach could be a well-defined unified architecture applied to other CPSs.     

编队协同对地攻击指挥决策系统体系结构研究

对有人机/UCAV编队对地攻击指挥决策系统体系结构进行了研究。首先分析了有人机/UCAV编队协同对地攻击作战体系结构,建立了编队协同作战指挥决策模型,对人机功能进行了划分,提出了有限中央控制下的分布式有人机/UCAV编队协同指挥决策体系结构。在此基础上,基于MAS理论,建立了编队协同对地攻击MAS体系结构,给出了UCAV Agent的详细结构。最后,采用HLA开发环境建立了系统仿真结构模型,仿真实验证明了所建立系统体系结构的有效性。研究内容对提高编队复杂作战环境适应能力和整体作战效能至关重要。     

Investigating the Manual View Specification and Visualization by Demonstration Paradigms for Visualization Construction

Interactivity plays an important role in data visualization. Therefore, understanding how people create visualizations given different interaction paradigms provides empirical evidence to inform interaction design. We present a two‐phase study comparing people's visualization construction processes using two visualization tools: one implementing the manual view specification paradigm (Polestar) and another implementing visualization by demonstration (VisExemplar). Findings of our study indicate that the choice of interaction paradigm influences the visualization construction in terms of: 1) the overall effectiveness, 2) how participants phrase their goals, and 3) their perceived control and engagement. Based on our findings, we discuss trade‐offs and open challenges with these interaction paradigms.     

Lost in space? Cognitive fit and cognitive load in 3D virtual environments

In this paper, we explore how visual representations of information in 3D virtual environments (3DVEs) supports both individual and shared understanding, and consequently contribute to group decision making in tasks with a strong visual component. We integrate insights from cognitive fit theory and cognitive load theory in order to formulate hypotheses about how 3DVEs can contribute to individual understanding, shared understanding, and group decision making. We discuss the results of an experiment in which 192 participants, in 3-person teams, were asked to select an apartment. As proposed by cognitive fit theory, our results indicate that 3DVEs are indeed more effective in supporting individual understanding than 2D information presentations. Next, in line with cognitive load theory, the static presentation of 3D information turns out to be more effective in supporting shared understanding and group decision making than an immersive 3DVE. Our results suggest that although the 3DVE capabilities of realism, immersion and interactivity contribute to individual understanding, these capabilities combined with the interaction and negotiation processes required for reaching a shared understanding (and group decision), increases cognitive load and makes group processes inefficient. The implications of this paper for research and practice are discussed.     

Imprinting of founders' entrepreneurial motivations on enterprises' practices and processes: The context of creative industries

This paper explores the imprinting of entrepreneurs' motivations on the practices and processes of enterprises. We investigate the question in the context of creative industries (CIs) as an extreme case of entrepreneurial motivations (EMs) prevalence. We analyse the EM of 14 founders of design consultancies. Three EMs emerge: self‐fulfilment, freedom and financial motivation. The qualitative analysis reveals that the founders' EMs at the time of a venture's founding has a lasting impact on the characteristics of the venture (name, processes formalization, decision‐making processes, performance measures and growth strategies). Specifically, founders driven by self‐fulfilment tend to build ventures named after them. In these ventures, processes are informal, decision‐making is centralized, performance measures are based on personal satisfaction and recognition and enterprise growth is restrained. Founders who seek freedom tend to run enterprises with semiformal processes, semicentralized decision‐making, a client satisfaction focus, and slow growth strategies. In contrast, founders with financial motivations tend to create enterprises with formal processes, decentralized decision‐making, financial performance metrics and growth ambitions. By focusing on EM and adopting a holistic approach beyond some characteristics of the venture, we complement the imprinting literature.     

An architecture for an intelligent support system for design validation and manufacturing of aerospace components

This paper presents the architecture of a multi‐agent system that aims to support the design and manufacturing processes of composite material components for the aerospace, automobile, ship and other industries. The major goals are to accelerate the design process while minimising cost; support users in selecting the optimum material in terms of its properties, cost and environmental impact; and enhance the efficiency of the component manufacturing phase by extending the decision support capabilities of the existing real‐time monitoring software. An important requirement for achieving these objectives is to enable the more efficient utilisation of existing distributed information on composite materials. To this end, state‐of‐the‐art advances in metadata concepts were adopted and extended. By implementing metadata structures for each data category available, the searching and retrieval mechanisms are improved both in terms of speed and usage. In addition, large amounts of knowledge about the design and manufacturing processes themselves are incorporated into a multi‐agent environment in order to enhance the ability to support the user groups involved in the various stages of component design and manufacturing. In order to present the system architecture here, an example from the aerospace industry has been used.     

A framework for developing intelligent real‐time scheduling systems

The authors describe the adaptation of a hierarchical management‐control framework to the design of intelligent real‐time scheduling systems. Originally developed for the analysis of organizations where dynamics are governed solely by the reactions of human decision makers, the control framework provides insights concerning design requirements for decision systems that react in human‐like fashion within narrow domains of expertise. The framework emphasizes the dynamic balance of autonomy and decentralization required to achieve planned goals within a hierarchical organization. Applying this framework, design specifications for knowledge‐based scheduling systems are developed, which are responsive to organizational dynamics as required for schedule control and replanning. An illustration is given that addresses kitchen scheduling in full‐service restaurants. © 2006 Wiley Periodicals, Inc. Hum Factors Man 16: 385–408, 2006.     

C4KISR测试与评估应用框架的研究与实现

基于HLA/RTI和分布式数据库初步构建了一个开放的测试和评估应用框架,为战术C4KISR系统中的指挥、控制、通信(C3)以及典型飞行器武器系统的作战效能(K)提供一种有效的可扩展的测试评估环境.系统采用了基于HLA/RTI的分布式体系结构,与被测系统相连.建立了典型的层次化战术CAKISR系统效能指标体系,并实现相应的评估算法模型.基于Oracle数据库建立了通用的战术C4KISR系统试验数据库/用例库/算法库和相应的管理工具,并开发了通用的分析评估工具,支持评估指标的在线/离线计算和显示.     

Examining the effects of cognitive consistency between training anddisplays

An experiment was performed testing the “display cognitive consistency hypothesis” proposed in Adelman et al. (1996). This hypothesis states that the effectiveness of a display format for decision-aiding systems depends on the consistency between how the system displays its reasoning process and how the person is processing the information. Results of an experiment using a simulated Army air defense task and college students found support for the hypothesis, but only at a situation-specific, not global level. Although unexpected, these results were consistent with other research indicating the importance of situation-specific context for understanding judgment and decision processes in individual and group settings     

Belief‐rule‐based expert systems for evaluation of e‐government: a case study

Little knowledge exists on the impact and results associated with e‐government projects in many specific‐use domains. Therefore, it is necessary to evaluate the efficiency and effectiveness of e‐government systems. Because the development of e‐government is a continuous process of improvement, it requires continuous evaluation of the overall e‐government system as well as evaluation of its various dimensions such as determinants, characteristics and results. E‐government development is often complex, with multiple stakeholders, large user bases and complex goals. Consequently, even experts have difficulties in evaluating these systems, especially in an integrated and comprehensive way, as well as on an aggregate level. Expert systems are a candidate solution to evaluate such complex e‐government systems. However, it is difficult for expert systems to cope with uncertain evaluation data that are vague, inconsistent, highly subjective or in other ways, challenging to formalize. This paper presents an approach that can handle uncertainty in e‐government evaluation: the combination of Belief Rule Base knowledge representation and Evidential Reasoning. This approach is illustrated with a concrete prototype, known as the Belief Rule Based Expert System (BRBES) and implemented in the local e‐government of Bangladesh. The results have been compared with a recently developed method of evaluating e‐government, and it is demonstrated that the results of the BRBES are more accurate and reliable. The BRBES can be used to identify the factors that need to be improved to achieve the overall aim of an e‐government project. In addition, various ‘what if’ scenarios can be generated, and developers and managers can obtain a foretaste of the outcomes. Thus, the system can be used to facilitate decision‐making processes under uncertainty.     

THE DECISION PROCESS: A PERSPECTIVE†

Abstract

A decision process consists of all the subprocesses pertaining to the selection of a course of action, or a plan, and to its execution. Most of these subprocesses except some well formalized ones like decision analysis are covert processes taking place exclusively in someone's mind, and are hitherto paid relatively little attention. In order to have a better understanding of the organization of the whole process, and thus to upgrade it, these neglected aspects should be brought to attention. With that principle in mind, the topics like the following are discussed in this paper: hierarchical structure of multiple decision processes, learning mechanism to dispense with deliberate analytical information processing for minor decisions, cognitive simulators to generate decision trees, goal-setting and decision scope.