A reified network model for adaptive decision making based on the disconnect-reconnect adaptation principle

Jan Treur

doi:10.1007/978-3-030-31445-3_5

A reified network model for adaptive decision making based on the disconnect-reconnect adaptation principle

Jan Treur^*

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

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Abstract

In recent literature from Neuroscience, the adaptive role of the effects of stress on decision making is highlighted. In this chapter, it is addressed how that role can be modelled computationally using a reified adaptive temporal-causal network architecture. The presented network model addresses the so-called disconnect-reconnect adaptation principle. In the first phase of the acute stress suppression of the existing network connections takes place (disconnect), and then in a second phase after some time there is a relaxation of the suppression. This gives room to quickly get rid of old habits that are not applicable anymore in the new stressful situation and start new learning (reconnect) of better decision making, more adapted to this new stress-triggering context.

Original language	English
Title of host publication	Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models
Editors	Jan Treur
Publisher	Springer International Publishing AG
Chapter	5
Pages	123-142
Number of pages	20
ISBN (Electronic)	9783030314453
ISBN (Print)	9783030314446, 9783030314477
DOIs	https://doi.org/10.1007/978-3-030-31445-3_5
Publication status	Published - 2020

Publication series

Name	Studies in Systems, Decision and Control
Volume	251
ISSN (Print)	2198-4182
ISSN (Electronic)	2198-4190

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Adaptive temporal-causal network model
Decision making
Hebbian learning
Network reification
Stress

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10.1007/978-3-030-31445-3_5

Treur2020_Chapter_AReifiedNetworkModelForAdaptiveDEcisionMakingFinal published version, 1.05 MBLicence: Article 25fa Dutch Copyright Act

https://www.researchgate.net/publication/334576350

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Treur, J. (2020). A reified network model for adaptive decision making based on the disconnect-reconnect adaptation principle. In J. Treur (Ed.), Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models (pp. 123-142). (Studies in Systems, Decision and Control; Vol. 251). Springer International Publishing AG. https://doi.org/10.1007/978-3-030-31445-3_5

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Treur, J 2020, A reified network model for adaptive decision making based on the disconnect-reconnect adaptation principle. in J Treur (ed.), Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models. Studies in Systems, Decision and Control, vol. 251, Springer International Publishing AG, pp. 123-142. https://doi.org/10.1007/978-3-030-31445-3_5

A reified network model for adaptive decision making based on the disconnect-reconnect adaptation principle. / Treur, Jan.
Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models. ed. / Jan Treur. Springer International Publishing AG, 2020. p. 123-142 (Studies in Systems, Decision and Control; Vol. 251).

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

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AB - In recent literature from Neuroscience, the adaptive role of the effects of stress on decision making is highlighted. In this chapter, it is addressed how that role can be modelled computationally using a reified adaptive temporal-causal network architecture. The presented network model addresses the so-called disconnect-reconnect adaptation principle. In the first phase of the acute stress suppression of the existing network connections takes place (disconnect), and then in a second phase after some time there is a relaxation of the suppression. This gives room to quickly get rid of old habits that are not applicable anymore in the new stressful situation and start new learning (reconnect) of better decision making, more adapted to this new stress-triggering context.

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KW - Hebbian learning

KW - Network reification

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Treur J. A reified network model for adaptive decision making based on the disconnect-reconnect adaptation principle. In Treur J, editor, Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models. Springer International Publishing AG. 2020. p. 123-142. (Studies in Systems, Decision and Control). Epub 2019 Nov 2. doi: 10.1007/978-3-030-31445-3_5

A reified network model for adaptive decision making based on the disconnect-reconnect adaptation principle

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