Advanced modelling for team collaborative decision making analysis on maritime autonomous surface ships using team cognitive work analysis

Tao, J, Liu, Z, Wang, X ORCID: 0000-0002-7469-6237, Cao, Y, Matthews, C ORCID: 0000-0002-4126-6484 and Yang, Z ORCID: 0000-0003-1385-493X (2025) Advanced modelling for team collaborative decision making analysis on maritime autonomous surface ships using team cognitive work analysis. Regional Studies in Marine Science, 90. p. 104477. ISSN 2352-4855

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Abstract

The emerging Maritime Autonomous Surface Ships (MASS) significantly challenges team collaboration in the maritime sector. Although significant progress has been made, current research lacks a holistic analytical approach to MASS operational teams, with most studies focusing on isolated aspects. To address this gap, a two-step framework is developed to model and analyse MASS team tasks from a system-wide perspective. Firstly, a team cognitive work analysis and an improved hierarchical task analysis are conducted, clarifying the division of responsibilities and information transmission paths. Secondly, a task network is constructed using complex network theory, and key topological characteristics are extracted. Thirdly, eight types of node importance ranking methods are employed, including three based on individual indicators and five based on hybrid algorithms, along with robustness analysis based on deliberate attacks to quantitatively identify critical nodes from different perspectives and analyse their roles in team tasks. Finally, Boolean algebra is applied to integrate the results of the node rankings, and a susceptible infected model is utilised to validate the validity of ranking results, allowing for prioritisation of critical nodes. The results demonstrate that targeted attacks based on betweenness centrality cause the network to collapse rapidly, with reachability dropping sharply once 16.7 % nodes fail. The entire system becomes nearly non-functional when 54.2 % nodes fail. The decline in reachability slows after 25 % nodes fail, indicating diminishing marginal impact. This study contributes to the development of a holistic framework for analysing team tasks of MASS, with future work exploring dynamic modelling and weighted interdependencies across broader maritime scenarios.

Item Type:	Article
Uncontrolled Keywords:	37 Earth Sciences; 3708 Oceanography; 31 Biological Sciences; 3103 Ecology; 0405 Oceanography; 3103 Ecology; 3708 Oceanography
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions:	Civil Engineering and Built Environment Engineering
Publisher:	Elsevier BV
Date of acceptance:	4 September 2025
Date of first compliant Open Access:	13 October 2025
Date Deposited:	13 Oct 2025 14:25
Last Modified:	13 Oct 2025 14:30
DOI or ID number:	10.1016/j.rsma.2025.104477
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27321

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