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摘要翻译：
在经典的羊群文献中，代理人接收到关于二元自然状态的私人信号，并在观察其前人的行动后依次选择一个行动。当私有信号的信息量无界时，已知Agent收敛到正确的行动和正确的信念。我们研究了收敛发生的速度，并表明它发生得比智能体观察信号时慢得多。然而，我们也表明，从动作中学习的速度可以任意接近从信号中学习的速度。特别是，在代理停止采取错误操作之前的预期时间可以是有限的，也可以是无限的，这取决于私有信号的分布。在高斯私有信号的典型情况下，我们精确地计算了收敛速度，并明确地表明，在这种情况下，从动作中学习比从信号中学习要慢得多。
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英文标题：
《The speed of sequential asymptotic learning》
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作者：
Wade Hann-Caruthers, Vadim V. Martynov and Omer Tamuz
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最新提交年份：
2017
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分类信息：
一级分类：Mathematics 数学
二级分类：Probability 概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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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 the classical herding literature, agents receive a private signal regarding a binary state of nature, and sequentially choose an action, after observing the actions of their predecessors. When the informativeness of private signals is unbounded, it is known that agents converge to the correct action and correct belief. We study how quickly convergence occurs, and show that it happens more slowly than it does when agents observe signals. However, we also show that the speed of learning from actions can be arbitrarily close to the speed of learning from signals. In particular, the expected time until the agents stop taking the wrong action can be either finite or infinite, depending on the private signal distribution. In the canonical case of Gaussian private signals we calculate the speed of convergence precisely, and show explicitly that, in this case, learning from actions is significantly slower than learning from signals.
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PDF链接：
https://arxiv.org/pdf/1707.02689
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