摘要
This paper introduces a learning algorithm that allows for imitation in recursive dynamic games. The Kiyotaki–Wright model of money is a well-known example of such decision environments. In this context, learning by experience has been studied before. Here, we introduce imitation as an additional channel for learning. In numerical simulations, we observe that the presence of imitation either speeds up social convergence to the theoretical Markov–Nash equilibrium or leads every agent of the same type to the same mode of suboptimal behavior. We observe an increase in the probability of convergence to equilibrium, as the incentives for optimal play become more pronounced.
摘要译文
本文介绍了一种学习算法，允许在递归动态游戏中进行模仿。 Kiyotaki-Wright货币模型是这种决策环境的一个众所周知的例子。在这种背景下，以前已经研究过经验学习。在这里，我们引入模仿作为学习的额外渠道。在数值模拟中，我们观察到模仿的存在要么加速社会趋同到理论马尔可夫 - 纳什均衡，要么将相同类型的每个代理引导到相同的次优行为模式。我们观察到收敛到均衡的可能性增加，因为最佳游戏的激励变得更加明显。
ErdemBaşçı;. Learning by imitation[J]. Journal of Economic Dynamics and Control, 1999,23(9-10): 1569-1585