The dynamics of innovation

We analyze social learning and innovation in an overlapping generations model in which available technologies have correlated payoffs. Each generation experiments within a set of policies whose payoffs are initially unknown and drawn from the path of a Brownian motion with drift. Marginal innovation consists in choosing a technology within the convex hull of policies already explored and entails no direct cost. Radical innovation consists in experimenting beyond the frontier of that interval, and entails a cost that increases with the distance from the frontier, and may decrease with the best technology currently available. We study how successive generations alternate between radical and marginal innovation, in a pattern reminiscent of Schumpeterian cycles. Even when the underlying Brownian motion has a positive drift, radical innovation stops in finite time. New generations then fine-tune policies in search of a local optimum, converging to a single technology. Our analysis thus suggests that sustaining radical innovation in the long-run requires external intervention.