On Learning and Growth
We study optimal growth under learning. We extend the Mirman-Zilcha stochastic growth results characterizing optimal programs for general utility and production functions to the case of learning. We then 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., without the use of conjugate priors), for any horizon. Finally, we address the issue of experimentation by providing a solution to an infinite-horizon optimal dynamic program.
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"Growth under uncertainty with experimentation,"
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- Mirman, Leonard J & Samuelson, Larry & Urbano, Amparo, 1993.
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 34(3), pages 549-63, August.
- Mirman, L.J. & Samuelson, L. & Urbano, A., 1989. "Monopoly Experimentation," Papers 8-89-7, Pennsylvania State - Department of Economics.
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- 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.
- Philippe Aghion & Patrick Bolton & Christopher Harris & Bruno Jullien, 1991. "Optimal Learning by Experimentation," Review of Economic Studies, Oxford University Press, vol. 58(4), pages 621-654.
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