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Experimentation in Networks
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DOI：10.1257/aer.20230233.png)
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摘要
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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. ( JEL D82, D83, D86, O31, O33, Z13)
Keyword:
DIFFUSION
ADOPTION
INNOVATION
期刊
IF:
11.6
论文数:
5.0K
被引数:
7.5W
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