TY - JOUR
T1 - Complexity synchronization in emergent intelligence
AU - Mahmoodi, Korosh
AU - Kerick, Scott E.
AU - Franaszczuk, Piotr J.
AU - Parsons, Thomas D.
AU - Grigolini, Paolo
AU - West, Bruce J.
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - Adaptive environment
KW - Complexity synchronization
KW - Emergent intelligence
KW - Modified diffusion entropy analysis
KW - Multi-agent-based-modeling
KW - Reinforcement learning
KW - Selfish algorithm
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U2 - 10.1038/s41598-024-57384-5
DO - 10.1038/s41598-024-57384-5
M3 - Article
C2 - 38514808
AN - SCOPUS:85188336831
SN - 2045-2322
VL - 14
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 6758
ER -