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Informational herding with model misspecification

机译：模型不合规格的信息羊群

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This paper demonstrates that a misspecified model of information processing interferes with long-run learning and allows inefficient choices to persist, despite sufficient information for asymptotic learning. I consider an observational learning environment in which agents observe a private signal about an unknown state and some agents observe the actions of their predecessors. Individuals face an inferential challenge when extracting information from the actions of others, as prior actions aggregate multiple sources of correlated information. A misspecified model allows for the fact that an agent may not be able to distinguish between new and redundant information, and may have an incorrect model of how others process this information. When individuals significantly overestimate the amount of new information, beliefs about the state become entrenched and incorrect learning occurs with positive probability. When individuals sufficiently overestimate the amount of redundant information, beliefs fail to converge and learning is incomplete. Learning is complete when agents have an approximately correct model of inference, establishing that the correctly specified model is robust to perturbation. (C) 2016 Elsevier Inc. All rights reserved.
机译：本文证明，尽管有足够的信息用于渐进学习，但错误指定的信息处理模型会干扰长期学习，并使低效的选择得以持续。我考虑一个观察性学习环境，在该环境中，特工观察到有关未知状态的私人信号，而某些特工观察其前任的行为。当从其他人的行为中提取信息时，个人会面临推论性的挑战，因为先前的行为会聚合相关信息的多个来源。错误指定的模型会导致这样一个事实，即代理可能无法区分新信息和冗余信息，并且可能对其他人如何处理此信息具有错误的模型。当个人大大高估了新信息的数量时，关于状态的信念就会变得根深蒂固，错误的学习发生的可能性也就很大。当个人充分高估冗余信息的数量时，信念将无法融合，学习将变得不完整。当代理具有近似正确的推理模型时，即表明正确指定的模型对扰动具有鲁棒性，学习即告完成。 （C）2016 Elsevier Inc.保留所有权利。

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