Simulation of Burnout Processes by a Multi-Order Adaptive Network Model

L. Weyland, W. Jelsma, J. Treur

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In this paper, an adaptive network model for the development of and recovery from burnout was designed and analysed. The current literature lacks adequate adaptive models to describe the processes involved in burnout. In this research, the informal conceptual models from Golembiewski and Leiter-Maslach model were combined with additional first-and second-order adaptive components and used to design a computational network model based on them. Four different scenarios were simulated and compared, where the importance of the therapy and the ability to learn from it was emphasised. The results show that if there was no therapy present, the emotional regulation was too poor to have an effect. However, at the moment therapy was applied, the emotional regulation would start to help against burnout. Another finding was that one long therapy session has a greater effect than several shorter sessions. Lastly, therapy only had a significant long-lasting effect when adaquate neuro-plasticity occurred.
Original languageEnglish
Title of host publicationComputational Science – ICCS 2021
Subtitle of host publication21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part III
EditorsMaciej Paszynski, Dieter Kranzlmüller, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M.A. Sloot
PublisherSpringer Nature Switzerland AG
Number of pages14
ISBN (Electronic)9783030779672
ISBN (Print)9783030779665
Publication statusPublished - 2021
Event21st International Conference on Computational Science, ICCS 2021 - Virtual, Online
Duration: 16 Jun 202118 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12744 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Computational Science, ICCS 2021
CityVirtual, Online


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