Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.