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Updated: Feb 8, 2026

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Adaptation and survivors in a random Boolean network.

Ikuo Nakamura1

  • 1Sony Corporation, 2-10-14 Osaki, Shinagawa, Tokyo, Japan.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|May 15, 2002
PubMed
Summary

In random Boolean networks, agents using imitation strategies evolve from chaos to a frozen state. Low-connectivity agents win minority games, outperforming high-connectivity ones.

Area of Science:

  • Complex Systems
  • Computational Biology
  • Game Theory

Background:

  • Random Boolean networks (RBNs) are models for complex systems.
  • Agent-based modeling is used to study emergent behavior.
  • Minority games explore strategies in competitive environments.

Purpose of the Study:

  • To investigate the dynamics of a competitive agent with an imitation strategy in an RBN.
  • To analyze the impact of agent connectivity on game outcomes.
  • To compare imitation strategies with mutation strategies.

Main Methods:

  • Simulated a competitive agent with imitation strategy in an RBN.
  • Implemented a learning rule where worst performers adopt best performers' strategies.
  • Analyzed system evolution from chaotic to intermittent and frozen states.

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  • Examined time series of agent strategies based on network connectivity.

    • Main Results:

    • The RBN transitions from a chaotic to a frozen state through an intermittent phase.
    • Agent connectivity significantly influences the success of imitation strategies.
    • Low-connectivity agents consistently outperform high-connectivity agents in the minority game.
    • Imitation strategies lead to different network dynamics compared to mutation strategies.

    Conclusions:

    • Imitation strategies in RBNs can lead to stable, frozen states.
    • Network topology, specifically low connectivity, confers an advantage in competitive minority games.
    • The agent-based imitation model provides insights into evolutionary game dynamics in complex systems.