Flying personal planes: modeling the airport choices of general aviation pilots using stated preference methodology

This study employed stated preference (SP) models to determine why general aviation pilots choose to base and operate their aircraft at some airports and not others. Thirteen decision variables identified in pilot focus groups and in the general aviation literature were incorporated into a series of hypothetical choice tasks or scenarios. The scenarios were offered within a fractional factorial design to establish orthogonality and to preclude dominance in any combination of variables. Data from 113 pilots were analyzed for individual differences across pilots using conditional logit regression with and without controls. The results demonstrate that some airport attributes (e.g., full-range hospitality services, paved parallel taxiway, and specific types of runway lighting and landing aids) increase pilot utility. Heavy airport congestion and airport landing fees, on the other hand, decrease pilot utility. The importance of SP methodology as a vehicle for modeling choice behavior and as an input into the planning and prioritization process is discussed. Actual or potential applications include the development of structured decision-making instruments in the behavioral sciences and in human service programs.     

Remembrance of cases past: who remembers what, when confronting critical flight events?

Recent "naturalistic" theories of decision making emphasize the role of stored prior experiences or cases as a guide to current action. However, there is little empirical evidence on the role that case-based remindings play in real-life decision making. The present study utilized a Web-based survey to collect data about the role of prior cases in pilot decision making about critical flight events. Results showed that more than half of the 1081 pilots who responded could provide details about utilizing a previous case in responding to a critical flight event. These events were most likely to involve weather or equipment failure. The cases were found to be useful in situational assessment rather than option evaluation. The use of cases increased with age and experience. Data obtained from a concurrent conventional survey showed broadly similar results. The implications of these results are that case-based remindings play an important role in expert pilot decision making and that a training system that incorporates case-based learning would be a potentially useful means of improving pilot decision making. Actual or potential applications of this research include the development of case-based training systems to enhance flight training.     

What you don't know can hurt you: factors impacting diagnosis in the automated cockpit

OBJECTIVE: We examined the impact of operational variables on diagnosis and decision-making processes, focusing on information search. BACKGROUND: Arguably, the "best" decision-making processes in high-technology cockpits would be those that are both correspondent (objectively accurate) and coherent (rationally sound). In the electronic world, coherence in terms of identification and incorporation of all relevant information is both a prerequisite to and a limiting factor for accurate diagnosis and decision making. METHOD: Regional carrier pilots (N=93) responded to six scenarios by accessing information to determine a diagnosis and decision. RESULTS: Time pressure, a common operational variable, had a strong negative effect on information search and diagnosis accuracy, and the presence of noncongruent information heightened these negative effects. Unexpectedly, source of initial information (automated or other) did not impact any of the dependent variables. Diagnosis confidence was unrelated to accuracy and was negatively related to amount of information accessed. CONCLUSION: Results confirm both the need for coherence in diagnostic processes and the difficulty of maintaining it under time pressure. APPLICATION: One implication of the results of this study is that pilots in high-technology cockpits must be trained to utilize coherent diagnostic processes as standard operating procedure. Additionally, because thorough information search for diagnosis in an automated environment is essential, automated systems must be designed to foster coherent, and thus accurate, diagnostic processes.     

Heuristic-based information acquisition and decision making among pilots

OBJECTIVE: This research was designed to examine the impact of heuristic-based approaches to the acquisition of task-related information on the selection of an optimal alternative during simulated in-flight decision making. BACKGROUND: The work integrated features of naturalistic and normative decision making and strategies of information acquisition within a computer-based, decision support framework. METHOD: The study comprised two phases, the first of which involved familiarizing pilots with three different heuristic-based strategies of information acquisition: frequency, elimination by aspects, and majority of confirming decisions. The second stage enabled participants to choose one of the three strategies of information acquisition to resolve a fourth (choice) scenario. RESULTS: The results indicated that task-oriented experience, rather than the information acquisition strategies, predicted the selection of the optimal alternative. It was also evident that of the three strategies available, the elimination by aspects information acquisition strategy was preferred by most participants. CONCLUSION: It was concluded that task-oriented experience, rather than the process of information acquisition, predicted task accuracy during the decision-making task. It was also concluded that pilots have a preference for one particular approach to information acquisition. APPLICATION: Applications of outcomes of this research include the development of decision support systems that adapt to the information-processing capabilities and preferences of users.     

Brittleness in the design of cooperative problem-solving systems:the effects on user performance

One of the critical problems in the design and use of advanced decision-support systems is their potential “brittleness”. This brittleness can arise because of the inability of the designer to anticipate and design for all of the scenarios that could arise during the use of the system. The typical “safety valve” to deal with this problem is to keep a person “in the loop”, requiring that person to apply his/her expertise in making the final decision on what actions to take. This paper provides empirical data on how the role of the decision support system can have a major impact on the effectiveness of this design strategy. Using flight planning for commercial airlines as a testbed, three alternative designs for a graphical flight planning tool were evaluated, using 27 dispatchers and 30 pilots as subjects. The results show that the presentation of a suggestion or recommendation by the computer early in the person's own problem evaluation can have a significant impact on that person's decision processes, influencing situation assessment and the evaluation of alternative solutions     

Machine learning advice in managerial decision-making: The overlooked role of decision makers’ advice utilization

Machine learning (ML) analyses offer great potential to craft profound advice for augmenting managerial decision-making. Yet, even the most promising ML advice cannot improve decision-making if it is not utilized by decision makers. We therefore investigate how ML analyses influence decision makers’ utilization of advice and resulting decision-making performance. By analyzing data from 239 ML-supported decisions in real-world organizational scenarios, we demonstrate that decision makers’ utilization of ML advice depends on the information quality and transparency of ML advice as well as decision makers’ trust in data scientists’ competence. Furthermore, we find that decision makers’ utilization of ML advice can lead to improved decision-making performance, which is, however, moderated by the decision makers’ management level. The study’s results can help organizations leverage ML advice to improve decision-making and promote the mutual consideration of technical and social aspects behind ML advice in research and practice as a basic requirement.     

ADAPT: A Predictive Cognitive Model of User Visual Attention and Action Planning

We present a computational cognitive model of novice and expert aviation pilot action planning called ADAPT that models performance in a dynamically changing simulated flight environment. We perform rigorous tests of ADAPT's predictive validity by comparing the performance of individual human pilots to that of their respective models. Individual pilots were asked to execute a series of flight maneuvers using a flight simulator, and their eye fixations and control movements were recorded in a time-synched database. Computational models of each of the 25 individual pilots were constructed, and the individual models simulated execution of the same flight maneuvers performed by the human pilots. The time-synched eye fixations and control movements of individual pilots and their respective models were compared, and rigorous tests of ADAPT's predictive validity were performed. The model explains and predicts a significant portion of pilot visual attention and control movements during flight as a function of piloting expertise. Implications for adaptive training systems are discussed.     

Dominant Learning Processes in Emergency Response Organizations: A Case Study of a Joint Rescue Coordination Centre

Research on decision‐making and expert performance in operational settings generally considers extensive personal experience and deliberate practice to be decisive for development of expertise. Learning and development of decision‐making abilities therefore tend to be explained in terms of human cognition. This study examines the dominant learning processes in a Joint Rescue Coordination Centre, revealing that personal experience, individual problem‐solving, knowledge accumulation, and collective reflection are the key processes in the individual development of decision‐making abilities. These findings show that both individual and contextual (including organisational) aspects need to be considered if we are to fully understand the development of decision‐making abilities and consequently be better able to explain and analyse decision‐making and expert performance in natural settings.     

The influence of training level on manual flight in connection to performance,scan pattern,and task load

This work focuses on the analysis of pilots’ performance during manual flight operations in different stages of training and their influence on gaze strategy. The secure and safe operation of air traffic is highly dependent on the individual performances of the pilots. Before becoming a pilot, he/she has to acquire a broad set of skills by training to pass all the necessary qualification and licensing standards. A basic skill for every pilot is manual control operations, which is a closed-loop control process with several cross-coupled variables. Even with increased automation in the cockpit, the manual control operations are essential for every pilot as a last resort in the event of automation failure. A key element in the analysis of manual flight operations is the development over time in relation to performance and visual perception. An experiment with 28 participants (including 11 certified pilots) was conducted in a Boeing 737 simulator. For defined flight phases, the dynamic time warping method was applied to evaluate the performance for selected criteria, and eye-tracking methodology was utilized to analyze the gaze-pattern development. The manipulation of workload and individual experience influences the performance and the gaze pattern at the same time. Findings suggest that the increase of workload has an increased influence on pilots depending on the flight phase. Gaze patterns from experienced pilots provide insights into the training requirements of both novices and experts. The connection between workload, performance and gaze pattern is complex and needs to be analyzed under as many differing conditions. The results imply the necessity to evaluate manual flight operations with respect to more flight phases and a detailed selection of performance indications.

     

Towards safe and collaborative aerodrome operations: Assessing shared situational awareness for adverse weather detection with EEG-enabled Bayesian neural networks

Teams formulated by aviation professionals are essential in maintaining a safe and efficient aerodrome environment. Nonetheless, the shared situational awareness between the flight crews under adverse weather conditions might be impaired. This research aims to evaluate the impact of a proposed enhancement in communication protocol on cognitive workload and develop a human-centred classification model to identify hazardous meteorological conditions. Thirty groups of subjects completed four post-landing taxiing tasks under two visibility conditions (CAVOK/CAT IIIA) while two different communication protocols (presence/absence of turning direction information) were adopted by the air traffic control officer (ATCOs). Electroencephalography (EEG) and the NASA Task Load Index were respectively used to reflect the pilot’s mental state and to evaluate the pilot’s mental workload subjectively. Results indicated that impaired visibility increases the subjective workload significantly, while the inclusion of turning direction information in the ATCO’s instruction would not significantly intensify their cognitive workload. Mutual information was used to quantitatively assess the shared situational awareness between the pilot flying and the pilot monitoring. Finally, this research proposes a human-centred approach to identify potentially hazardous weather conditions from EEG power spectral densities with Bayesian neural networks (BNN). The classification model has outperformed other baseline algorithms with an accuracy of 66.5%, an F1 score of 61.4%, and an area under the ROC of 0.749. Using the concept of explainable AI with Shapley Additive Explanations (SHAP) values, the exploration of latent mental patterns formulates novel knowledge to gain insights into the vital physiological indicators of the pilots in response to different scenarios from the BNN model. In the long term, the model facilitates the decision regarding the necessity of providing automation and decision-making aids to pilots.     

Media usage and velocity in executive information acquisition: an exploratory study

This paper reports on a field study that addresses three important dimensions of information use in decision making: (1) how combinations of information source and medium change over time as executive decision-making processes unfold; (2) how the rate of information acquisition affects media selection; and (3) how media usage patterns feed into and out of meetings. The findings extend the integrated literature of information acquisition and decision making, introduce the notion that the velocity of information acquisition influences media selection during the decision-making process, and expand the understanding of executive information gathering in the practice of business. The authors identify potential applications for information systems practice and raise critical issues to help focus future research.     

Integration of reinforcement learning and optimal decision-making theories of the basal ganglia

This article seeks to integrate two sets of theories describing action selection in the basal ganglia: reinforcement learning theories describing learning which actions to select to maximize reward and decision-making theories proposing that the basal ganglia selects actions on the basis of sensory evidence accumulated in the cortex. In particular, we present a model that integrates the actor-critic model of reinforcement learning and a model assuming that the cortico-basal-ganglia circuit implements a statistically optimal decision-making procedure. The values of cortico-striatal weights required for optimal decision making in our model differ from those provided by standard reinforcement learning models. Nevertheless, we show that an actor-critic model converges to the weights required for optimal decision making when biologically realistic limits on synaptic weights are introduced. We also describe the model's predictions concerning reaction times and neural responses during learning, and we discuss directions required for further integration of reinforcement learning and optimal decision-making theories.     

Hybrid Supervised-Unsupervised Channel Estimation Scheme with Dynamic Transmission of Pilots

Multiple-Input Multiple-Output (MIMO) digital communications standards typically include pilot symbols in the definition of the transmit signals with the purpose of acquiring the Channel State Information (CSI) using supervised algorithms at the receiver side. Such pilot symbols convey no information and, therefore, system throughput, spectral efficiency and transmit energy consumption are all penalized. In this article, we propose to acquire the CSI combining supervised and unsupervised algorithms. Our strategy avoids the periodical transmission of unnecessary pilots by using a simple decision criterion to determine the time instants when the performance obtained with an unsupervised algorithm degrades or, equivalently, the time instants when pilots are required. We show the performance of this scheme for MIMO systems with Decision-feedback equalizers at the receiver.     

Common set of weights in data envelopment analysis under prospect theory

Data envelopment analysis (DEA) is a data‐driven tool for performance evaluation, measuring decision‐making units (DMUs) and designating them with specific weightings. The standard DEA model typically sets up that decision‐makers (DMs) are wholly rational to select the most favourable weights to obtain the maximum performance score, but does not take into account their attitude toward risk during the assessment. The prospect theory generally matches humans' psychological behaviours. Thus, our study captures the non‐rational behaviours of DMs, performing under risk scenarios, in order to construct a novel common‐weights DEA model that maximizes the total prospect value, which can vary more steeply for losses than for gains, hence obtaining a more realistic common weight scheme. Our proposed model not only generates DMUs, with higher total prospect values, but also greater degrees of satisfaction. The current study shows that the prospect theory can be aptly extended to the DEA research area, supplying a proper guideline for future DEA research.     

Prediction of product design decision Making: An investigation of eye movements and EEG features

Design decision making is happened in every design node and iteration, and the expert decision-making bias and personal preference will ultimately affect the success or failure of the product reaching the market. In this paper, we try to predict the design decision making by investigating the relations between design decision making and subjects’ eye movements and Electroencephalogram(EEG) response. Four different methods were applied and compared to classify the different EEG features and two methods were used for EEG feature selection to correspond the design decision making results. In this study, the authors applied a multimodal fusion strategy for design decision making recognition where the authors used eye tracking and EEG response data as input dataset. According to the experiment results, the performance of the fusion strategy combined with EEG signals and eye movement characteristics is well in fitting the expert decision making results. The multimodal fusion combining eye tracking data and EEG has a strong potential to be a new design decision method to guide the design practice and provide supportive and objective data to reduce the effects of subjectivity, one-sidedness and superficiality in decision making. These results show that it is possible to create a classifier based on features extracted from eye movements and EEG response for the design decision making behaviour.     

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.     

Using the Perceptual Cycle Model and Schema World Action Research Method to generate design requirements for new avionic systems

The Schema World Action Research Method (SWARM) has previously been used as a means to explore the underlying decision‐making processes involved in retrospective incident reports. The approach has been fruitful in capturing all interacting processes involved in managing incidents. This paper proposes that SWARM may also be used prospectively within the early phases of the design lifecycle for new behavioral‐based intervention strategies. Six pilot interviews were conducted to explore pilots' perceptual cycle processing when faced with a suspected engine oil leak. The aim was to explore whether there may be any deficiencies within current practise and explore ways in which pilots may be better supported in dealing with abnormal system parameters such as this. A number of design recommendations are proposed for a new avionic system capable of supporting and guiding pilots through the decision‐making process.     

Situational influences on ethical decision-making in an IT context

Processes of ethical decision-making are thought to depend on the issue faced when making the decision. We examined the processes by examining student's reactions to five scenarios involving IT use. Data were collected using a questionnaire following a group discussion. The results showed that ethical decision-making processes did indeed vary by scenario, suggesting that a single-issue approach is inadequate for studying ethical decision-making. Perceived importance of the ethical issue was a factor in the scenarios, but it did not have an all-inclusive influence on the decision-making of the participants. The results were considered in the context of theories and Mason's ethical issues of the information age. We offer advice to managers on how to limit unethical behavior.     

An AHP-based approach toward enterprise architecture analysis based on enterprise architecture quality attributes

Enterprise Architecture (EA) as a discipline that manages large amount of models and information about different aspects of the enterprise, can support decision making on enterprise-wide issues. In order to provide such support, EA information should be amenable to analysis of various utilities and quality attributes. In this regard, we have proposed the idea of characterizing and using enterprise architecture quality attributes. And this paper provides a quantitative AHP-based method toward expert-based EA analysis. Our method proposes a step-by-step process of assessing quality attribute achievement of different scenarios using AHP. By this method, most suitable EA scenarios are selected according to prioritized enterprise utilities and this selection has an important affect on decision making in enterprises. The proposed method also introduces a data structure that contains required information about quality attribute achievement of different EA scenarios in enterprises. The stored asset can be used for further decision making and progress assessment in future. Sensitivity analysis is also part of the process to identify sensitive points in the decision process. The applicability of the proposed method is demonstrated using a practical case study.