In real-world scenarios, individuals often cooperate for mutual benefit. However, differences in wealth can lead to varying outcomes for similar actions. In complex social networks, individuals' choices are also influenced by their neighbors. To explore the evolution of strategies in realistic settings, we conducted repeated asymmetric prisoners dilemma experiments on a weighted BA scale-free network. Our analysis highlighted how the four components of memory-one strategies affect win rates, found two special strategies in the evolutionary process, and increased the cooperation levels among individuals. These findings offer practical insights for addressing real-world problems.

翻译：在现实场景中，个体常为互利而合作。然而，财富差异可能导致相似行为产生不同结果。在复杂社交网络中，个体的选择亦受其邻居影响。为探究现实环境中策略的演化规律，我们在加权的BA无标度网络上进行了重复不对称囚徒困境实验。我们的分析揭示了记忆单步策略的四个组成部分如何影响胜率，发现了演化过程中的两种特殊策略，并提升了个体间的合作水平。这些发现为解决现实问题提供了实用见解。