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The HoneyComb Paradigm for Research on Collective Human Behavior
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Cooperation resonance based on link strategy reinforcement learning and conformity.

Bo Gao1,2, Pengfei Zuo3, Xiangfeng Dai4,5

  • 1School of Information Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China.

Chaos (Woodbury, N.Y.)
|December 2, 2024
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Summary

This study introduces a game model combining reinforcement learning (RL) and conformity to explore cooperation. It reveals resonance-like cooperation and defection phenomena due to complex strategy interactions.

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Area of Science:

  • Evolutionary Game Theory
  • Artificial Intelligence
  • Complex Systems

Background:

  • Cooperation is crucial for social and artificial systems.
  • Understanding the emergence and maintenance of cooperation remains a challenge.
  • Existing models often simplify agent strategies and interactions.

Purpose of the Study:

  • To investigate the emergence and maintenance of cooperation using a novel game model.
  • To explore the interplay between reinforcement learning (RL) agents with link strategies and conformist agents.
  • To analyze the nonlinear dynamics of cooperation under varying conditions.

Main Methods:

  • Development of a game model on a lattice network with periodic boundaries.
  • Integration of RL nodes employing link strategies and conformist nodes.
  • Numerical simulations and mathematical analysis to study system behavior.

Main Results:

  • Identification of a critical mass range for cooperative behavior.
  • Observation of nonlinear responses between cooperation rate and temptation to betray.
  • Discovery of resonance-like cooperation and resonance-like defection phenomena.

Conclusions:

  • The interaction between RL and conformist strategies leads to complex, nonlinear cooperative dynamics.
  • The study provides new insights into promoting cooperation in AI and human interactions.
  • Findings highlight the importance of strategy interactions in emergent system behavior.