Network Seeding

This research studies the optimal seeding of a network of consumers who are potential adopters of a technology. This problem has a wide range of applications which include launching a network good, inducing network-based marketing, igniting collaboration in organizations, and stimulating R&D with local spillovers. I have two main results so far. The first result shows that when a firm is unaware of how connected its consumers’ are, randomly seeding a network is almost always optimal. The second result provides a a simple and plausible set of conditions under which, when a firm that is aware of how connected its consumers are, it is optimal for the firm to "seed the fringes rather than the hubs", that is, focus on seeding the least connected consumers, and not the highly connected consumers. These results hold for any distribution over any arbitrary set of asymmetric networks, and are also independent of the strength of the network effects. My ongoing research focuses primarily on extending these results to include seeding with probabilistic adoption, costly and constrained seeding, myopic consumers, and expanding the model to permit the social network to be a basis for the diffusion of product information.