Learning-by-doing and organizational forgetting are empirically important in a variety of industrial settings. This paper provides a general model of dynamic competition that accounts for these fundamentals and shows how they shape industry structure and dynamics. We show that forgetting does not simply negate learning. Rather, they are distinct economic forces that interact in subtle ways to produce a great variety of pricing behaviors and industry dynamics. In particular, a model with learning and forgetting can give rise to aggressive pricing behavior, varying degrees of long-run industry concentration ranging from moderate leadership to absolute dominance, and multiple equilibria.

We study industries where the price that a firm sets serves as an investment into lower cost or higher demand. We assess the welfare implications of the ensuing competition for the market using analytical and numerical approaches to compare the equilibria of a learning-by-doing model to the first-best planner solution. We show that dynamic competition leads to low deadweight loss. This cannot be attributed to similarity between the equilibria and the planner solution. Instead, we show how learning-by-doing causes the various contributions to deadweight loss to either be small or partly offset each other. (JEL D21, D25, D43, D83, L13)

We formally characterize predatory pricing in a modern industry-dynamics framework that endogenizes competitive advantage and industry structure. As an illustrative example we focus on learning-by-doing. To disentangle predatory pricing from mere competition for efficiency on a learning curve we decompose the equilibrium pricing condition. We show that forcing firms to ignore the predatory incentives in setting their prices can have a large impact and that this impact stems from eliminating equilibria with predation-like behavior. Along with the predation-like behavior, however, a fair amount of competition for the market is eliminated. (JEL D21, D43, D83, K21, L13, L41)