AI聚合速度过快会阻碍集体学习，慢速或局部聚合更有效。
How AI Aggregation Affects Knowledge
- 引入AI聚合器，根据群体信念训练并反馈合成信号
- 聚合更新过快时，无法找到稳定提升学习的权重组合
- 局部聚合器在各类环境中均能改善学习，优于全局聚合
人工智能改变社会学习模式，当聚合输出成为未来预测的训练数据时尤为明显。本文扩展DeGroot模型，引入一个基于群体信念训练并反馈合成信号的AI聚合器，定义学习差距为长期信念与最优基准的偏离，以衡量AI聚合对学习的影响。主要发现：当聚合器更新速度过快时，在广泛环境类中不存在正测度的训练权重能稳健提升学习；而更新足够慢时，此类权重存在。进一步比较全局与局部架构：基于邻近或主题特定数据训练的局部聚合器在所有环境中均能稳健改进学习。因此，用单一全局聚合器替代多个专用局部聚合器，至少在状态的一个维度上会恶化学习效果。
原文摘要 · Abstract (English)
Artificial intelligence (AI) changes social learning when aggregated outputs become training data for future predictions. To study this, we extend the DeGroot model by introducing an AI aggregator that trains on population beliefs and feeds synthesized signals back to agents. We define the learning gap as the deviation of long-run beliefs from the efficient benchmark, allowing us to capture how AI aggregation affects learning. Our main result identifies a threshold in the speed of updating: when the aggregator updates too quickly, there is no positive-measure set of training weights that robustly improves learning across a broad class of environments, whereas such weights exist when updating is sufficiently slow. We then compare global and local architectures. Local aggregators trained on proximate or topic-specific data robustly improve learning in all environments. Consequently, replacing specialized local aggregators with a single global aggregator worsens learning in at least one dimension of the state.
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