Nothing remains the same in social networks, especially the closeness of relationships between selfish individuals. With the passage of time, this relationship may change dynamically and have an important impact on cooperation. In this paper, we propose a new evolutionary game model to investigate the evolution of cooperation, in which the link weight is adaptively adjusted by comparing the individual payoff with her/his surrounding environment, and the learning ability of individual is affected by his historical strategies at the same time. To be specific, if the focal individual’s payoff is greater than the average one of his nearest neighbors, the link weight between the focal individual and nearest neighbors will be increased by one unit; however, if the focal individual’s payoff is smaller than the average one of his nearest neighbors, the link weight between them will be reduced by one unit; otherwise, the link weight between them will be unchanged. In addition, we use a specific parameter $\epsilon$ to determine the link weight adjustment range. Meanwhile, the focal individual will decide how to learn from her/his neighbor’s strategy according to his strategy of the last $M$ game rounds when she/he updates the current strategy. Through extensive Monte Carlo simulations, we find that dynamic adjustment of link weights can significantly promote the evolution of cooperation. Particularly, the parameter $\delta$ determining the intensity of weight adjustment has an optimal value regarding the level of cooperation, and then the cooperation has significantly been improved with the growth of $\epsilon$. Also, there is an optimal memory length $M$ as far as the emergence and persistence of cooperation is concerned. The current results are extremely conducive to understanding how selfish individuals in social dilemma dynamically adjust their relationships to promote the collective cooperation.

在社交网络中，没有什么是一样的，尤其是自私个体之间的亲密关系。随着时间的流逝，这种关系可能会动态变化，并对合作产生重要影响。在本文中，我们提出了一种新的演化博弈模型来研究合作的演化，其中通过比较个人收益与其周围环境的适应性来调整链接权重，并且个人的学习能力受其历史策略的影响。同时。具体而言，如果焦点人物的收益大于他最近的邻居的平均值，则焦点人物和最近的邻居之间的链接权重将增加一个单位；但是，如果焦点人物的收益小于他最近的邻居的平均收益，它们之间的链接权重将减少一个单位；否则，它们之间的链接权重将保持不变。另外，我们使用特定的参数$\epsilon$确定链接权重调整范围。同时，焦点人物将根据他/她的上一个策略来决定如何从邻居的策略中学习$\mathrm{中号}$当她/他更新当前策略时，游戏回合。通过广泛的蒙特卡洛模拟，我们发现链接权重的动态调整可以显着促进合作的发展。特别是参数$\delta$ 确定权重调整的强度对于合作水平具有最佳值，然后随着 $\epsilon$。此外，还有一个最佳的内存长度$\mathrm{中号}$就合作的出现和持久性而言。当前的结果非常有助于了解社会困境中自私的个体如何动态地调整他们的关系以促进集体合作。