Replication data for: When Does Learning in Games Generate Convergence to Nash Equilibria? The Role of Supermodularity in an Experimental Setting

This study clarifies the conditions under which learning in games produces convergence to Nash equilibria in practice. We experimentally investigate the role of supermodularity, which is closely related to the more familiar concept of strategic complementarities, in achieving convergence through learning. Using a game from the literature on solutions to externalities, we find that supermodular and "near-supermodular" games converge significantly better than those far below the threshold of supermodularity. From a little below the threshold to the threshold, the improvement is statistically insignificant. Increasing the parameter far beyond the threshold does not significantly improve convergence.本研究厘清了博弈学习在实践场景中收敛至纳什均衡（Nash equilibrium）的适用条件。我们通过实验探究了超模性（supermodularity）——这一概念与更为人熟知的战略互补性（strategic complementarities）紧密相关——在学习驱动收敛过程中所扮演的角色。我们采用外部性解决方案相关文献中的一个博弈开展实验，结果发现，超模博弈与“近超模博弈”的收敛表现显著优于远低于超模性阈值的博弈。从略低于阈值至恰好达到阈值的区间内，收敛效果的提升在统计学上并无显著性。进一步将参数调高至远超阈值的范围，亦无法对收敛效果产生显著改善。