Endogenous Learning from Incremental Actions

In today's seminar Julia Salmi, from UCL, discusses her paper on Endogenous Learning from Incremental Actions

Abstract:

We study an experimentation problem where actions today have a long-lasting impact on information generation in the future. Actions are irreversible and generate information gradually over time. 

We solve for the optimal path of actions when the decision maker does not know the payoff-relevant state of the world. Because current choices have persistent effects, the problem has two state variables: a summary of past actions and the current belief on the state of the world. There is a novel informational trade-off as acting today speeds up information generation but postponing actions results in more informed choices.

Our two leading examples cover the monopoly pricing of durable goods with social learning and capacity expansion in a market with uncertain optimal size. We show that since the monopolist can internalize future benefits from learning, the monopolist's optimal solution may result in a higher social surplus than the competitive market in both examples.