Network rewiring promotes cooperation in an aspirational learning model.

Area of Science:

• * Game theory
• * Network science
• * Agent-based modeling

Background:

• * Analyzes cooperative decision-making using individual aspiration levels within public goods games.
• * Investigates behavior in both static and dynamic network structures.

Purpose of the Study:

• * To understand how payoff sensitivity and dynamic aspiration levels influence satisfaction and behavior.
• * To determine the impact of learning efficiency on collective outcomes and strategy dominance.
• * To explore the role of network restructuring in promoting cooperation.

Main Methods:

• * Employed a public goods game framework within static and dynamic networks.
• * Modeled individual decision-making based on aspiration levels and satisfaction.
• * Analyzed strategy fractions, learning efficiency, and network topology.

Main Results:

• * Cooperation dominates below a threshold learning efficiency, despite short-term strategy fluctuations.
• * Player categorization by satisfaction reveals periodic cycling between cooperative and defective strategies.
• * Network restructuring, even with a small fraction of nodes, significantly promotes cooperation.

Conclusions:

• * Dynamic aspiration levels and efficient learning are crucial for cooperative strategy dominance.
• * Network restructuring is a powerful mechanism for ensuring long-term altruism in social dilemmas.
• * Internal criteria for strategy updates combined with network changes favor cooperative outcomes.