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Sequential learning models situations where agents predict a ground truth in sequence, by using their private, noisy measurements, and the predictions of agents who came earlier in the sequence. We study sequential learning in a social network, where agents only see the actions of the previous agents in their own neighborhood. The fraction of agents who predict the ground truth correctly depends heavily on both the network topology and the ordering in which the predictions are made. A natural question is to find an ordering, with a given network, to maximize the (expected) number of agents who predict the ground truth correctly. In this paper, we show that it is in fact NP-hard to answer this question for a general network, with both the Bayesian learning model and a simple majority rule model. Finally, we show that even approximating the answer is hard.
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Experimentation in Networks
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)
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- Award ID(s):
- 2149291
- PAR ID:
- 10624052
- Publisher / Repository:
- American Economic Review
- Date Published:
- Journal Name:
- American Economic Review
- Volume:
- 114
- Issue:
- 9
- ISSN:
- 0002-8282
- Page Range / eLocation ID:
- 2940 to 2980
- Subject(s) / Keyword(s):
- Networks, Experimentation, Diffusion
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1257/aer.20230233
