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Examining Social, Information, and Task Networks in Submarine Command and Control
- Source :
- IEEE Transactions on Human-Machine Systems. 48:252-265
- Publication Year :
- 2018
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2018.
-
Abstract
- Submarine control room operations have not changed much over the past 50 years, despite introduction of new technologies. This study sought to catalog current operations as a baseline for comparing new ways of working. Three scenarios were selected to be examined in both high and low demand: returning to periscope depth, dived tracking, and inshore operations. The scenarios were run in a submarine simulator with currently serving submariners from the Royal Navy. The flow of throughout the submarine command team was examined using event analysis for systemic teamwork (EAST). EAST models collaborative teamwork via three networks: task, social, and information. Results show that the social interactions, information transition, and focus of tasks are different depending on the particular operation being completed and the work demand placed on the command team. There are particular information elements that are fundamental across all scenario types. Task and communication load is not evenly distributed across the team, with potential bottlenecks identified between the sonar controller and operations officer roles. Implications of the results are discussed alongside recommendations for future research.
- Subjects :
- Teamwork
Operations research
Computer Networks and Communications
Computer science
media_common.quotation_subject
Distributed computing
05 social sciences
Submarine
Human Factors and Ergonomics
Sonar
Control room
050105 experimental psychology
Computer Science Applications
Task (project management)
Human-Computer Interaction
Navy
Artificial Intelligence
Control and Systems Engineering
Signal Processing
Command and control
0501 psychology and cognitive sciences
Baseline (configuration management)
050107 human factors
media_common
Subjects
Details
- ISSN :
- 21682305 and 21682291
- Volume :
- 48
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Human-Machine Systems
- Accession number :
- edsair.doi...........2f779e8565a69e314ba75b7cbf722298
- Full Text :
- https://doi.org/10.1109/thms.2017.2720659