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

Jan Treur*

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-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 languageEnglish
Title of host publicationNetwork-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models
EditorsJan Treur
PublisherSpringer International Publishing AG
Chapter5
Pages123-142
Number of pages20
ISBN (Electronic)9783030314453
ISBN (Print)9783030314446, 9783030314477
DOIs
Publication statusPublished - 2020

Publication series

NameStudies in Systems, Decision and Control
Volume251
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190

Keywords

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

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