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A hybrid modeling approach to simulate complex systems and classify behaviors
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0002-3416-4535
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0001-8218-4306
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0003-1126-3781
2024 (English)In: NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS, ISSN 2192-6662, Vol. 13, no 1, article id 9Article in journal (Refereed) Published
Abstract [en]

Many important systems, both natural and artificial, are complex in nature, and models and simulations are one of the main instruments to study them. In this paper, we present an approach where a complex social system is represented at a high level of abstraction as a network, thereby addressing several challenges such as quantification, intervention, adaptation and validation. The network represents the factors that influence the mental health and wellbeing in children and young people. In this article, we present an approach that links a system dynamics simulation to simulate the network and ranking algorithms to measure the vertices' behaviors. The network is enhanced by adding edge strengths in the form of correlations between vertices (established through literature). Such an approach allows us to exploit the network structure to qualify and quantify the vertices of the network, to overlay different processes over the network topology, to add and remove new vertices, and therefore interact dynamically. This in turn allows for the qualification of vertices' importance and network resilience. System dynamics simulation allows for policy analysis, where different scenarios are analyzed by stimulating a set of vertices and the effect over the network is observed. This approach allows for an abstract, flexible, yet comprehensive way of analyzing a complex social network at any scale.

Place, publisher, year, edition, pages
Springer Nature , 2024. Vol. 13, no 1, article id 9
Keywords [en]
Simulation and modeling, Ranking algorithm, Complex systems, Mental wellbeing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-345028DOI: 10.1007/s13721-024-00446-5ISI: 001186407300001Scopus ID: 2-s2.0-85188097928OAI: oai:DiVA.org:kth-345028DiVA, id: diva2:1849553
Note

QC 20240408

Available from: 2024-04-08 Created: 2024-04-08 Last updated: 2024-04-08Bibliographically approved

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Boodaghian Asl, ArsinehRaghothama, JayanthDarwich, Adam S.Meijer, Sebastiaan

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