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Finance and Economics Discussion Series
The Finance and Economics Discussion Series logo links to FEDS home page Learning by Doing and the Value of Experimentation
Volker Wieland

Abstract: Research on learning-by-doing has typically been restricted to cases where estimation and control can be treated separately. Recent work has provided convergence results for more general learning problems where experimentation is an important aspect of optimal control. However the associated optimal policy cannot be derived analytically because Bayesian learning introduces a nonlinearity in the dynamic programming problem. This paper characterizes the optimal policy numerically and shows that it incorporates a substantial degree of experimentation. Dynamic simulations indicate that optimal experimentation dramatically improves the speed of learning, while separating control and estimation frequently induces a long-lasting bias in the control and target variables.

Keywords: Bayesian optimal control, learning by doing, experimentation, dynamic programming

Full paper (692 KB PDF) | Full paper (1582 KB Postscript)

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Last update: July 16, 1997