Complexity Synchronization in Emergent Intelligence

Korosh Mahmoodi1, Scott E. Kerick, Piotr J. Franaszczuk1, Thomas D. Parsons, Paolo Grigolini, Bruce J. West
Nonlinear Sciences, Adaptation and Self-Organizing Systems, Adaptation and Self-Organizing Systems (nlin.AO)
2023-11-19 00:00:00
In this work, we use a simple multi-agent-based model (MABM), implementing selfish algorithm (SA) agents, to create an adaptive environment and show, using modified diffusion entropy analysis (MDEA), that the mutual-adaptive interac- tion between the parts of such a network manifests complexity synchronization (CS). CS has been experimentally shown to exist among organ-networks (ONs) of the brain (neurophysiology), lungs (respiration), and heart (cardiovascular reac- tivity) and to be explained theoretically as a synchronization of the multifractal scaling parameters characterizing each time series. Herein, we find the same kind of CS in the emergent intelligence (i.e., without macroscopic control and based on self-interest) between two groups of agents playing an anti-coordination game, thereby suggesting the potential for the same CS in real-world social phenomena and in human-machine interactions.
PDF: Complexity Synchronization in Emergent Intelligence.pdf
Empowered by ChatGPT