We study a sequential learning model featuring naive agents on a network. Agents wrongly believe their predecessors act solely on private information, so they neglect redundancies among observed actions. We provide a simple linear formula expressing agents' actions in terms of network paths and use this formula to completely characterize the set of networks guaranteeing eventual correct learning. This characterization shows that on almost all networks, disproportionately influential early agents can cause herding on incorrect actions. Going beyond existing social-learning results, we compute the probability of such mislearning exactly. This lets us compare likelihoods of incorrect herding, and hence expected welfare losses, across network structures. The probability of mislearning increases when link densities are higher and when networks are more integrated. In partially segregated networks, divergent early signals can lead to persistent disagreement between groups. We conduct an experiment and find that the accuracy gain from social learning is twice as large on sparser networks, which is consistent with naive inference but inconsistent with the rational-learning model.

中文翻译：

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网络结构和朴素的顺序学习

我们研究了一个以网络上的幼稚代理为特征的顺序学习模型。代理错误地认为他们的前任仅根据私人信息行事，因此他们忽略了观察到的行为之间的冗余。我们提供了一个简单的线性公式，根据网络路径表达代理的行为，并使用该公式来完全表征网络集，以确保最终正确学习。这种特征表明，在几乎所有网络上，影响力过大的早期智能体都会导致对错误行为的追赶。超越现有的社会学习结果，我们准确地计算了这种错误学习的概率。这让我们可以比较不同网络结构中不正确放牧的可能性，从而比较预期的福利损失。当链接密度更高并且网络更加集成时，错误学习的可能性会增加。在部分隔离的网络中，不同的早期信号会导致群体之间持续存在分歧。我们进行了一个实验，发现在稀疏网络上，社交学习的准确度增益是原来的两倍，这与朴素推理一致，但与理性学习模型不一致。