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...

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Bibliographic Details
Published in:Economic theory 2016-04, Vol.61 (4), p.641-684
Main Authors: Mirman, Leonard J., Reffett, Kevin, Santugini, Marc
Format: Article
Language:English
Subjects:
Analysis
Consumption
Decision making
Dynamic programming
Economic growth
Economic growth models
Economic theory
Economic Theory/Quantitative Economics/Mathematical Methods
Economic uncertainty
Economics
Economics and Finance
Educational environment
Educational outcomes
Euler equations
Eulers equations
Experimentation
Forecasts and trends
Game Theory
Growth models
Learning
Learning models (Stochastic processes)
Microeconomics
Optimal consumption
Optimal policy
Production functions
Public Finance
Research Article
Social and Behav. Sciences
Studies
Utility models
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Summary: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.
ISSN:0938-2259
1432-0479
DOI:10.1007/s00199-015-0948-x