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 governing the evolution of public capital, including the effect of investment in technology on future stock. Although the characterization of a dynamic game with Bayesian dynamics (and without the assumption of adaptive learning) is generally intractable, we characterize the unique symmetric Bayesian-learning recursive Cournot-Nash equilibrium for any finite horizon and for general distributions of the random variables. We also show that the limits of the equilibrium outcomes for a finite horizon exist. The addition of learning to a stochastic environment is shown to have a profound effect on the equilibrium.
['Leonard J. Mirman', 'Marc Santugini']
Learning and Technological Progress in Dynamic Games