On Learning and Growth
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 production, for general distributions of the random shocks and beliefs (i.e., there is no conjugate priors) and for any horizon. We finally address the issue of experimentation by providing a solution to an infinite-horizon optimal dynamic program.
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95-12, Brown University, Department of Economics.
- Aghion Philippe & Bolton, Patrick & Harris Christopher & Jullien Bruno, 1991.
"Optimal learning by experimentation,"
CEPREMAP Working Papers (Couverture Orange)
- Manjira Datta & Leonard J. Mirman & Edward E. Schlee, 2002. "Optimal Experimentation in Signal Dependent Decision Problems," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(2), pages 577-608, May.
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