Informational Herding with Model Misspecification
Informational Herding with Model Misspecification
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DOI：
10.2139/ssrn.2402889
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发表时间：
2013-06
期刊：
影响因子：
--
通讯作者：
J. Bohren
中科院分区：
文献类型：
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作者：
J. Bohren
本文证明了一个错误的信息处理模型干扰长期学习，并提供了一个解释，为什么个人可能会继续选择一个低效的行动，尽管有足够的公共信息来学习的真实状态。我认为一个社会学习环境，代理人从私人信号，公共信号和他们的前辈的行动，并有足够的公共信息存在，以实现渐近有效的学习推断。先前的行动聚集了多个信息源;代理面临着区分新信息和冗余信息的推理挑战。我表明，当个人显着高估的新信息量包含在先前的行动，信念的未知状态变得根深蒂固，不正确的学习可能会发生。另一方面，当个体充分高估冗余信息的数量时，信念是脆弱的，学习是不完整的。当智能体有一个近似正确的推理模型时，学习就完成了--没有信息处理偏差的模型对扰动是鲁棒的。
This paper demonstrates that a misspecified model of information processing interferes with long-run learning and offers an explanation for why individuals may continue to choose an inefficient action, despite sufficient public information to learn the true state. I consider a social learning environment where agents draw inference from private signals, public signals and the actions of their predecessors, and sufficient public information exists to achieve asymptotically efficient learning. Prior actions aggregate multiple sources of information; agents face an inferential challenge to distinguish new information from redundant information. I show that when individuals significantly overestimate the amount of new information contained in prior actions, beliefs about the unknown state become entrenched and incorrect learning may occur. On the other hand, when individuals sufficiently overestimate the amount of redundant information, beliefs are fragile and learning is incomplete. When agents have an approximately correct model of inference, learning is complete - the model with no information-processing bias is robust to perturbation.