Experimentation in Networks
研究前瞻性个体在社交网络中从自身和邻居的成功中学习，发现社会学习会挤占个人实验，总信息随网络密度下降，但福利在中等密度时达到次优水平。
We propose a model of strategic experimentation on social networks in which forward-looking agents learn from their own and neighbors’ successes. In equilibrium, private discovery is followed by social diffusion. Social learning crowds out own experimentation, so total information decreases with network density; we determine density thresholds below which agents’ asymptotic learning is perfect. By contrast, agent welfare is single peaked in network density and achieves a second-best benchmark level at intermediate levels that strike a balance between discovery and diffusion.