Abstract
In this chapter, equilibrium analysis for network models is addressed and applied to a network model of multilevel organisational learning. Equilibrium analysis can consider both properties of aggregation characteristics and properties of connectivity characteristics of a network. For connectivity characteristics, it is shown by introducing a form of stratification how for acyclic networks the equilibrium values of all nodes can be directly computed (by any functions used for aggregation) from those of the (independent) nodes without incoming connections. Moreover, by introducing a form of stratification for the network’s strongly connected components, it is also shown for any type of connectivity, similar equilibrium analysis results can be obtained relating equilibrium values in any component to equilibrium values in (independent) components without incoming connections. For aggregation characteristics, it is shown how certain classes of nonlinear functions used for aggregation in network models enable equilibrium analysis of the emerging dynamics within the network like linear functions do. All these results are illustrated by applying them to an example network model for organisational learning.
Original language | English |
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Title of host publication | Computational Modeling of Multilevel Organisational Learning and its Control Using Self-Modeling Network Models |
Editors | Gülay Canbaloğlu, Jan Treur, Anna Wiewiora |
Publisher | Springer Science and Business Media Deutschland GmbH |
Chapter | 17 |
Pages | 473-502 |
Number of pages | 30 |
ISBN (Electronic) | 9783031287350 |
ISBN (Print) | 9783031287343, 9783031287374 |
DOIs | |
Publication status | Published - 2023 |
Publication series
Name | Studies in Systems, Decision and Control |
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Volume | 468 |
ISSN (Print) | 2198-4182 |
ISSN (Electronic) | 2198-4190 |
Bibliographical note
Publisher Copyright:© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Equilibrium analysis
- Multilevel organisational learning