Memory-based strategy reputation and adaptive learning in spatial evolutionary games: A robust agent-based model for...

Area of Science:

• Evolutionary Game Theory
• Computational Social Science
• Agent-Based Modeling

Background:

• Cooperation is a key aspect of social behavior, yet its emergence and stability in populations remain a challenge.
• Spatial evolutionary games model interactions on lattices, but standard models often overlook nuanced social learning mechanisms.
• Reputation and learning heterogeneity are crucial factors influencing strategic interactions.

Purpose of the Study:

• To investigate how local reputation and heterogeneous learning influence cooperation in spatial evolutionary games.
• To analyze the impact of memory-based reputation on neighbor selection for strategy imitation.
• To examine the combined effect of reputation and varied learning sensitivities on cooperative dynamics.

Main Methods:

• Agents on a 2D lattice play the Prisoner's Dilemma with neighbors.
• A local reputation system is implemented using a finite memory window of past cooperative behavior.
• Strategy adoption follows a Fermi update rule with agent-specific learning sensitivity.

Main Results:

• Reputation-biased neighbor selection enhances cooperation by prioritizing historically cooperative agents.
• The combination of reputation and heterogeneous learning fosters more stable cooperative clusters.
• This mechanism effectively delays the erosion of cooperation even when the temptation to defect increases.

Conclusions:

• Cooperation in structured populations is significantly influenced by how agents form reputations and adapt their learning.
• Memory-based reputation and heterogeneous learning are vital for sustaining cooperation.
• Findings highlight the importance of social learning mechanisms in evolutionary game dynamics.