Evolutionary Game of GTFT on a Scale-Free Network

GTFT (Generous Tit-for-Tat) is an evolutionary game model implemented on a scale-free network architecture. This game features an iterative mechanism where the number of generations can be modified programmatically to facilitate detailed study of its characteristics. In this evolutionary game framework, each agent can choose to either cooperate with or defect against other participants. Through successive iterations, the strategies of these agents continuously evolve to adapt to environmental changes. The implementation typically involves adjacency matrix representations for network connectivity and probabilistic decision functions for strategy updates.

This evolutionary game model finds extensive applications in theoretical computer science and ecology, particularly in studying cooperation emergence and network dynamics. After 20 iterations, the properties of this evolutionary game become more distinct, allowing for better analysis of behavioral patterns and evolutionary trajectories. Key algorithmic components include fitness calculation based on payoff matrices, strategy update rules using reinforcement learning principles, and network topology preservation throughout the simulation cycle.