近几年来的一些真人博弈实验结果表明:以往博弈理论模型中所提出的个体策略更新动力学都不能准确地描述群体层面的策略演化以及系统合作行为的涌现现象。在本项目中，我们将开展大规模真人网络博弈实验，通过详细跟踪个体所采取策略与所获得收益的时间序列，分析其与网络邻居的策略与收益时间序列的关联行为，探寻影响个体决策动力学的关键性因素。同时根据行为心理学的研究成果，研究不同的强化学习规则对群体合作行为演化的影响，通过调节学习速率参数，找到理论预测结果与真人实验结果最符合的规则，为更好地理解实际社会系统中合作演化提供科学指导。最后我们还将借用统计物理学和非线性动力学中的思想和方法来详细研究网络博弈演化过程中呈现出来的相变行为，探究影响其临界指数或普适类的结构因素和动力学机制，为深入理解复杂适应系统局部微观作用导致宏观涌现行为提供深刻的理论解释。

The comprehensive results of recent human game experiments indicate that the strategy-updating rules proposed in previous game theoretical models cannot characterize how our humans change strategies and how the collective cooperation emerge in the real social population. In this project, we plan to implement large-scaled networked game experiments with participators recruited from our universities. We expect to find the crucial factors affecting ones strategy decision-making processes, by trailing the time series of each person's strategy and payoff information and by investigating how these two series correlate with those of the neighbors. Meanwhile, inspired by the results from behavioral psychology field, we want to study how the various reinforcement learning rules influence the evolution of collective cooperation. By tuning the parameters of reinforcement learning rules, we expect to find the optimal ones that can better match those results from real experiments to guide our understanding how cooperation evolves in real social systems. Besides, we will also explore the phase transition phenomena arisen in networked games by using the methods and ideas from statistical physics and nonlinear dynamics. By analyzing the critical exponents, universality classs, and their dependence on the network topology and dynamical rules, our results will hope to help us deep understanding how local interactions lead to global emergent phenomenon in complex adaptive systems.