  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
interaction 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 reactivity) 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.
