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Complexity synchronization in emergent intelligence.

Korosh Mahmoodi1, Scott E Kerick2, Piotr J Franaszczuk2,3

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Summary
This summary is machine-generated.

This study reveals complexity synchronization (CS) in social networks using a multi-agent-based model. This finding suggests CS is a fundamental property across biological and social systems.

Keywords:
Adaptive environmentComplexity synchronizationEmergent intelligenceModified diffusion entropy analysisMulti-agent-based-modelingReinforcement learningSelfish algorithm

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Area of Science:

  • Complex Systems Science
  • Computational Social Science
  • Network Science

Background:

  • Complexity synchronization (CS) is a phenomenon observed in biological organ-networks (ONs), such as the brain, lungs, and heart.
  • CS is theoretically explained by the synchronization of multifractal dimension (MFD) scaling parameters in simultaneously measured time series.
  • Previous research established CS in neurophysiology, respiration, and cardiovascular reactivity.

Purpose of the Study:

  • To investigate the emergence of complexity synchronization (CS) in a simulated social network.
  • To determine if CS can arise from self-organized interactions without external control.
  • To explore the potential for CS in social phenomena and human-machine interactions.

Main Methods:

  • Utilized a multi-agent-based model (MABM) with selfish algorithm (SA) agents to simulate a social network.
  • Employed a modified diffusion entropy analysis (DEA) to detect complexity synchronization (CS).
  • Simulated biased self-interest between two groups of agents engaged in an anti-coordination game.

Main Results:

  • Observed complexity synchronization (CS) in the emergent intelligence of self-organized groups within the social network model.
  • Demonstrated that CS arises from mutual-adaptive interactions between agents, even with biased self-interest.
  • The simulation successfully replicated CS patterns similar to those found in biological organ-networks (ONs).

Conclusions:

  • Complexity synchronization (CS) is a plausible emergent property of self-organized social interactions.
  • The findings suggest CS is a unifying principle applicable to both biological systems and artificial/social networks.
  • This research supports the potential for CS in understanding real-world social dynamics and human-machine systems.