论文标题
耦合信念的融合和稳定性 - 连续游戏中的策略学习动力
Convergence and Stability of Coupled Belief--Strategy Learning Dynamics in Continuous Games
论文作者
论文摘要
我们提出了一种学习动力，以模拟战略代理如何反复玩连续游戏，同时依靠信息平台来学习未知的与收益相关的参数。在每个时间步骤中，平台都会根据玩家的策略来更新参数的信念估计，并使用贝叶斯的规则实现了回报。然后，玩家采用通用学习规则，根据更新的信念来调整其策略。我们介绍了信念和策略的融合以及动力学的收敛固定点的特性。我们为存在全球稳定的固定点获得足够和必要的条件。我们还为固定点的局部稳定性提供了足够的条件。这些结果提供了一种方法，可以分析贝叶斯信念学习和游戏中的策略学习之间的相互作用产生的长期结果，并使我们能够表征学习的条件，在这些条件下，学习会导致完整的信息平衡。
We propose a learning dynamics to model how strategic agents repeatedly play a continuous game while relying on an information platform to learn an unknown payoff-relevant parameter. In each time step, the platform updates a belief estimate of the parameter based on players' strategies and realized payoffs using Bayes's rule. Then, players adopt a generic learning rule to adjust their strategies based on the updated belief. We present results on the convergence of beliefs and strategies and the properties of convergent fixed points of the dynamics. We obtain sufficient and necessary conditions for the existence of globally stable fixed points. We also provide sufficient conditions for the local stability of fixed points. These results provide an approach to analyzing the long-term outcomes that arise from the interplay between Bayesian belief learning and strategy learning in games, and enable us to characterize conditions under which learning leads to a complete information equilibrium.
