Inference for Games with Many Players
Inference for Games with Many Players
复制标题
多人游戏的推理
DOI：
10.1093/restud/rdv038
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Konrad Menzel
中科院分区：
文献类型：
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作者：
Konrad Menzel
我们发展了具有大量参与者的静态离散动作博弈的渐近理论，并提出了一种新的基于围绕有限参与者博弈极限的随机展开的推理方法。我们的分析侧重于匿名博弈，其中收益是代理自身行为和对手玩法的经验分布的函数。我们建立了一个大数定律和中心极限定理，可以用来建立点或集估计量的相合性和随着参与者数量的增加对结构参数推理的渐近有效性。所提出的方法以及极限理论都以观察到的样本中实现的平衡为条件，因此不需要在多个平衡中进行选择的任何假设。
We develop an asymptotic theory for static discrete-action games with a large number of players, and propose a novel inference approach based on stochastic expansions around the limit of the finite-player game. Our analysis focuses on anonymous games in which payoffs are a function of the agent's own action and the empirical distribution of her opponents' play. We establish a law of large numbers and central limit theorem which can be used to establish consistency of point or set estimators and asymptotic validity for inference on structural parameters as the number of players increases. The proposed methods as well as the limit theory are conditional on the realized equilibrium in the observed sample and therefore do not require any assumptions regarding selection among multiple equilibria.