Traditionally, the evolution of cooperation on structured population assumed the uniform interaction partner between gaming and learning. Yet in real-world society, individuals often act different roles in which environments gaming partners differ from learning partners. This investigation studies the evolution of cooperation under the effects of the diverse selection intensity induced by network asymmetry on two-layer networks, where the gaming and learning environments are modeled by different layers, respectively. It is found that heterogeneous selection intensity can alleviate the cooperation dilemma induced by asymmetry between gaming and learning environments. When selection intensity has a correlation with the edge overlap level of two layers, it is found that both positive correlation and negative correlation can optimize the evolution of cooperation for a moderate overlap level. However, positive correlation performs better than negative correlation in promoting the evolution of cooperation. Moreover, the increasing heterogeneity of selection enhances the evolution of cooperation under positive correlation, yet has different effects on cooperation under negative correlation for different temptations. Furthermore, we prove that the results are robust to the deterministic learning process as well as a higher noise.

中文翻译：

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多样化的选择强度解决了交互与学习对称性被打破所带来的合作困境


传统上，结构化人口合作的演变假定游戏和学习之间有统一的互动伙伴。然而，在现实社会中，个人经常扮演不同的角色，其中游戏伙伴与学习伙伴的环境不同。本研究研究了两层网络上网络不对称引起的不同选择强度影响下的合作演化，其中游戏和学习环境分别由不同层建模。研究发现，异质选择强度可以缓解博弈和学习环境不对称引起的合作困境。当选择强度与两层的边缘重叠水平相关时，发现正相关和负相关都可以优化适度重叠水平的合作演化。然而，正相关性在促进合作演化方面表现优于负相关性。此外，选择异质性的增加促进了正相关下合作的演化，但对于不同的诱惑，对负相关下的合作有不同的影响。此外，我们证明结果对于确定性学习过程以及较高的噪声具有鲁棒性。