摘要翻译：
本文介绍了有限状态和行动空间的平均场对策的一个自然学习规则，即所谓的短视调整过程。这些考虑的主要动机是确定动态平均场平衡所必需的复杂计算，这使得代理是否真的能够发挥这些平衡似乎是有疑问的。我们证明了在相当宽泛的条件下，短视调整过程在确定性均衡策略下局部收敛于平稳均衡。此外，对于两策略的情形，我们也得到了一个更强而直观的全局收敛性结果。
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英文标题：
《A Myopic Adjustment Process for Mean Field Games with Finite State and
  Action Space》
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作者：
Berenice Anne Neumann
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最新提交年份：
2020
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分类信息：

一级分类：Mathematics        数学
二级分类：Optimization and Control        优化与控制
分类描述：Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学，线性规划，控制论，系统论，最优控制，博弈论
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一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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英文摘要：
  In this paper, we introduce a natural learning rule for mean field games with finite state and action space, the so-called myopic adjustment process. The main motivation for these considerations are the complex computations necessary to determine dynamic mean-field equilibria, which make it seem questionable whether agents are indeed able to play these equilibria. We prove that the myopic adjustment process converges locally towards stationary equilibria with deterministic equilibrium strategies under rather broad conditions. Moreover, for a two-strategy setting, we also obtain a global convergence result under stronger, yet intuitive conditions.
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PDF链接：
https://arxiv.org/pdf/2008.13420