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A Reified Network Model for Adaptive Decision Making Based on the Disconnect-Reconnect Adaptation Principle

  • Jan TreurEmail author
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 251)

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.

Keywords

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

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Social AI Group, Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands

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