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Complexity synchronization in emergent intelligence.
In: Scientific Reports, Jg. 14 (2024-03-21), Heft 1, S. 1-18
Online
academicJournal
Zugriff:
In this work, we use a simple multi-agent-based-model (MABM) of a social network, implementing selfish algorithm (SA) agents, to create an adaptive environment and show, using a modified diffusion entropy analysis (DEA), that the mutual-adaptive interaction between the parts of such a network manifests complexity synchronization (CS). CS has been shown to exist by processing simultaneously measured time series from among organ-networks (ONs) of the brain (neurophysiology), lungs (respiration), and heart (cardiovascular reactivity) and to be explained theoretically as a synchronization of the multifractal dimension (MFD) scaling parameters characterizing each time series. Herein, we find the same kind of CS in the emergent intelligence of groups formed in a self-organized social interaction without macroscopic control but with biased self-interest between two groups of agents playing an anti-coordination game. This computational result strongly suggests the existence of the same CS in real-world social phenomena and in human–machine interactions as that found empirically in ONs. [ABSTRACT FROM AUTHOR]
Titel: |
Complexity synchronization in emergent intelligence.
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Autor/in / Beteiligte Person: | Mahmoodi, Korosh ; Kerick, Scott E. ; Franaszczuk, Piotr J. ; Parsons, Thomas D. ; Grigolini, Paolo ; West, Bruce J. |
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Zeitschrift: | Scientific Reports, Jg. 14 (2024-03-21), Heft 1, S. 1-18 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 2045-2322 (print) |
DOI: | 10.1038/s41598-024-57384-5 |
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