We study investment and consumption decisions in a dynamic game under learning. To that end, we present a model in which agents not only extract a resource for consumption but also invest in technology to improve the future stock. At the same time, the agents learn about the stochastic process...

In models of active learning or experimentation, agents modify their actions to affect the distribution of a signal that provides information about future payoffs. A standard result in the experimentation literature is that agents experiment, if at all, to increase information. This finding is a...

We embed learning (without experimentation) in optimal growth. We extend the Mirman-Zilcha results of stochastic optimal growth to the learning case. We use recursive methods to study the effect of learning on the dynamic program by considering the case of iso-elastic utility and linear...