信息聚合与社交网络：响应性与颠覆
Information Aggregation and Social Networks: Responsiveness and Overturning
- Hitotsubashi University（一桥大学）
- Nagoya University（名古屋大学）
- Keio University（庆应义塾大学）
- The University of Tokyo（东京大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文探究社交学习环境中网络结构对信息聚合效率的影响，构建理性智能体序列行动模型，发现星型与完全网络分别在特定信息结构下更优，揭示网络响应性与颠覆效应的权衡关系。
AI中文摘要：
本文研究社交学习环境中网络结构如何影响信息聚合效率。我们构建模型，其中理性智能体基于私人信号和网络中邻居的行动序列选择行动。聚焦给定有限期的期望支付比较，我们证明存在一种信息结构，使得星型网络的期望支付严格高于其他任何网络；还存在另一种信息结构，使得完全网络的期望支付严格高于其他任何网络。综上，这些结果表明，没有任何网络在所有信息结构中都统一最优。我们的分析凸显了响应性效应与颠覆效应之间的权衡：不连通的网络保留行动对私人信号的响应性，而高度连通的网络则促进能颠覆公共信念的极端信息的聚合。
英文摘要：
This paper studies how network structure affects information aggregation in social learning. Agents sequentially choose actions based on private signals and observations of neighbors' actions. Comparing a focal agent' s expected payoff across networks at a finite period, we show that a network is uniquely optimal for some informational environment if and only if the focal agent observes every predecessor. This characterization reveals that which network performs best depends critically on the informational environment. We then revisit two important implications of the characterization through transparent constructions: the star network can uniquely outperform every alternative under binary signals, while the complete network can do so with richer signals. These constructions highlight a trade-off between the responsiveness effect and the overturning effect: sparse networks facilitate information aggregation by preserving the responsiveness of actions to private signals, whereas dense networks facilitate information aggregation by revealing extreme information that overturns existing public beliefs.
