Evolutionary Digital Twin-Oriented Complex Networked Systems driven by node features and the mutation of feature preferences.

Wen, J; Gabrys, B; Musial, K

Evolutionary Digital Twin-Oriented Complex Networked Systems driven by node features and the mutation of feature preferences.

Wen, J Gabrys, B

Musial, K

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Publisher:: Public Library of Science (PLoS)
Publication Type:: Journal Article
Citation:: PLoS One, 2024, 19, (5), pp. e0303571
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Wen, J
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.contributor.author	Musial, K
dc.contributor.editor	Gallos, LK
dc.date.accessioned	2024-08-06T03:38:58Z
dc.date.available	2024-04-26
dc.date.available	2024-08-06T03:38:58Z
dc.date.issued	2024
dc.identifier.citation	PLoS One, 2024, 19, (5), pp. e0303571
dc.identifier.issn	1932-6203
dc.identifier.issn	1932-6203
dc.identifier.uri	http://hdl.handle.net/10453/180146
dc.description.abstract	Accurate modelling of complex social systems, where people interact with each other and those interactions change over time, has been a research challenge for many years. This study proposes an evolutionary Digital Twin-Oriented Complex Networked System (DT-CNS) framework that considers heterogeneous node features and changeable connection preferences. We create heterogeneous preference mutation mechanisms to characterise nodes' adaptive decisions on preference mutation in response to interaction patterns and epidemic risks. In this space, we use nodes' interaction utilities to characterise the positive feedback from interactions and negative impact of epidemic risks. We also introduce social capital constraint to harness the density of social connections better. The nodes' heterogeneous preference mutation styles include the (i)inactive style that keeps initial social preferences, (ii) ignorant style that randomly mutates preferences, (iii) egocentric style that optimises individual interaction utility, (iv) cooperative style that optimises the total interaction utilities by group decisions and (v) collaborative style that further allows the cooperative nodes to transfer social capital. Our simulation experiments on evolutionary DT-CNSs reveal that heterogeneous preference mutation styles lead to various interaction and infection patterns. The results also show that (i) increasing social capital enables higher interactions but higher infection risks and uncertainty in decision-making; (ii) group decisions outperform individual decisions by eliminating the unawareness of the decisions of other nodes; (iii) the collaborative nodes under a strict social capital limit can promote interactions, reduce infection risks and achieve higher overall interaction utilities.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Public Library of Science (PLoS)
dc.relation	http://purl.org/au-research/grants/arc/DP190101087
dc.relation.ispartof	PLoS One
dc.relation.isbasedon	10.1371/journal.pone.0303571
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	General Science & Technology
dc.subject.mesh	Humans
dc.subject.mesh	Mutation
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Biological Evolution
dc.subject.mesh	Social Networking
dc.subject.mesh	Humans
dc.subject.mesh	Mutation
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Biological Evolution
dc.subject.mesh	Social Networking
dc.subject.mesh	Humans
dc.subject.mesh	Mutation
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Biological Evolution
dc.subject.mesh	Social Networking
dc.title	Evolutionary Digital Twin-Oriented Complex Networked Systems driven by node features and the mutation of feature preferences.
dc.type	Journal Article
utslib.citation.volume	19
utslib.location.activity	United States
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Health Technologies (CHT)
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-08-06T03:38:48Z
pubs.issue	5
pubs.publication-status	Published online
pubs.volume	19
utslib.citation.issue	5

Abstract:

Accurate modelling of complex social systems, where people interact with each other and those interactions change over time, has been a research challenge for many years. This study proposes an evolutionary Digital Twin-Oriented Complex Networked System (DT-CNS) framework that considers heterogeneous node features and changeable connection preferences. We create heterogeneous preference mutation mechanisms to characterise nodes' adaptive decisions on preference mutation in response to interaction patterns and epidemic risks. In this space, we use nodes' interaction utilities to characterise the positive feedback from interactions and negative impact of epidemic risks. We also introduce social capital constraint to harness the density of social connections better. The nodes' heterogeneous preference mutation styles include the (i)inactive style that keeps initial social preferences, (ii) ignorant style that randomly mutates preferences, (iii) egocentric style that optimises individual interaction utility, (iv) cooperative style that optimises the total interaction utilities by group decisions and (v) collaborative style that further allows the cooperative nodes to transfer social capital. Our simulation experiments on evolutionary DT-CNSs reveal that heterogeneous preference mutation styles lead to various interaction and infection patterns. The results also show that (i) increasing social capital enables higher interactions but higher infection risks and uncertainty in decision-making; (ii) group decisions outperform individual decisions by eliminating the unawareness of the decisions of other nodes; (iii) the collaborative nodes under a strict social capital limit can promote interactions, reduce infection risks and achieve higher overall interaction utilities.

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http://hdl.handle.net/10453/180146