A dynamic network game for the adoption of new technologies

Abstract

When a product or technology is first introduced, there is uncertainty about its value or quality. This quality can be learned by trying the product, at a risk. It can also be learned by letting others try it and free-riding on the information that they generate. We propose a class of dynamic games to study the adoption of technologies of uncertain value, when agents are connected by a network. This class of games allows for referral incentives, whereby an agent can earn rewards by encouraging his neighbors to adopt. Dynamic network games can pose important tractability issues. To circumvent such problems, we derive a mean-field equilibrium (MFE) and show that a pricing policy that involves referral incentives leads to a double-threshold strategy by which both low and high-degree agents may choose to experiment with the technology of uncertain value whereas the middle-degree agents free-ride on the information revealed by that experimentation. We characterize how different dynamic pricing mechanisms affect the pattern of early/late adoption and information diffusion. Pricing mechanisms that allow a monopolist to guarantee early adoption by agents of high or low degrees are proposed. We show that dynamic pricing policies that do not involve referral incentives (i.e. price discounts for early adopters) always result in lower-degree agents adopting early. Likewise, dynamic pricing policies involving referral incentives that are high enough always result in higher-degree agents adopting early. The only network information needed to implement such pricing mechanisms is the degree distribution. We illustrate how referral incentives can be preferable on certain networks while price discounts may be preferable on others.