Co-Evolution of Complex Network Public Goods Game under the Edges Rules

Area of Science:

• Game Theory
• Network Science
• Computational Social Science

Background:

• Public goods games often suffer from free-riding, leading to the tragedy of the commons.
• Reconnecting network components is a known strategy to mitigate common resource depletion.

Purpose of the Study:

• To propose a novel public goods game model incorporating edge reconnection rules.
• To investigate the impact of edge weight based on node payoffs on cooperation levels.
• To identify predictive indicators for cooperation in complex networks.

Main Methods:

• Development of a public goods game model with edge reconnection dynamics.
• Evaluation of edge weights using node payoffs.
• Validation through Monte Carlo simulations (MCS) and real-world experiments.
• Analysis of distribution entropy of the excess average degree.

Main Results:

• The proposed model significantly expands the range of cooperation, especially with small gain factors.
• The model demonstrates effectiveness in promoting cooperative behaviors in public goods games.
• Distribution entropy of the excess average degree was identified as a reliable predictor of cooperation.

Conclusions:

• Edge reconnection based on node payoffs is an effective mechanism to foster cooperation in public goods games.
• The proposed model offers a robust framework for studying cooperation in networked systems.
• Distribution entropy of excess average degree serves as a key metric for understanding and predicting cooperative outcomes.