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Learning from Neighbors about a Changing State
arXiv - CS - Computer Science and Game Theory Pub Date : 2018-01-06 , DOI: arxiv-1801.02042
Krishna Dasaratha, Benjamin Golub, Nir Hak

Agents learn about a changing state using private signals and past actions of neighbors in a network. We characterize equilibrium learning and social influence in this setting. We then examine when agents can aggregate information well, responding quickly to recent changes. A key sufficient condition for good aggregation is that each individual's neighbors have sufficiently different types of private information. In contrast, when signals are homogeneous, aggregation is suboptimal on any network. We also examine behavioral versions of the model, and show that achieving good aggregation requires a sophisticated understanding of correlations in neighbors' actions. The model provides a Bayesian foundation for a tractable learning dynamic in networks, closely related to the DeGroot model, and offers new tools for counterfactual and welfare analyses.

中文翻译:

向邻居学习有关不断变化的状态

代理使用私有信号和网络中邻居的过去行为来了解不断变化的状态。我们描述了这种情况下的均衡学习和社会影响。然后我们检查代理何时可以很好地聚合信息,对最近的变化做出快速响应。良好聚合的一个关键充分条件是每个人的邻居拥有足够不同类型的私人信息。相比之下,当信号是同质的时,聚合在任何网络上都是次优的。我们还检查了模型的行为版本,并表明要实现良好的聚合需要对邻居行为的相关性有深入的了解。该模型为网络中易处理的学习动态提供了贝叶斯基础,与 DeGroot 模型密切相关,
更新日期:2020-01-22
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