Adaptive willingness resolves social dilemma in network populations

Area of Science:

• Network Science
• Evolutionary Game Theory
• Computational Social Science

Background:

• Cooperation is essential for complex networks to function effectively.
• Social dilemmas arise from conflicts between individual and collective interests.

Purpose of the Study:

• To propose a novel coevolutionary mechanism for learning willingness in network populations.
• To investigate how learning willingness impacts cooperation levels, especially under temptation to defect.

Main Methods:

• Agent-based modeling of a coevolutionary mechanism based on payoff comparison.
• Microscopic analysis to understand the emergence of cooperative clusters.
• Pair approximation theory for theoretical validation.

Main Results:

• Higher learning willingness significantly promotes cooperation, even when defecting offers a substantial advantage.
• The emergence of large-sized clusters of cooperators effectively protects them.
• The proposed mechanism demonstrates robustness in promoting cooperation.

Conclusions:

• The coevolutionary mechanism of learning willingness provides a feasible solution to social dilemmas in complex networks.
• Adaptive imitation strategies can foster cooperation and stability.
• Findings offer insights for designing systems that encourage pro-social behavior.