论文标题
重新加权采样动态的随机稳定性
Stochastic Stability of a Recency Weighted Sampling Dynamic
论文作者
论文摘要
我们介绍并研究了罕见相互作用的长期惯例形成模型。这种模型中的玩家通过观察过去的互动样本来形成信念，他们对此做出了吵闹的反应。我们提出了一个连续的州马尔可夫模型，非常适合我们的设置，并开发了一种与较大类似的学习模型相关的方法。我们表明，该模型接收了一个独特的渐近分布，该分布将其质量集中在某些最小的路缘块构型上。与现有的长期惯例形成的现有文献相反，我们专注于在最小的路缘块内部的行为，并为（近似）混合平衡惯例提供了收敛条件。
We introduce and study a model of long-run convention formation for rare interactions. Players in this model form beliefs by observing a recency-weighted sample of past interactions, to which they noisily best respond. We propose a continuous state Markov model, well-suited for our setting, and develop a methodology that is relevant for a larger class of similar learning models. We show that the model admits a unique asymptotic distribution which concentrates its mass on some minimal CURB block configuration. In contrast to existing literature of long-run convention formation, we focus on behavior inside minimal CURB blocks and provide conditions for convergence to (approximate) mixed equilibria conventions inside minimal CURB blocks.
