A Numerical Dynamic Programming Algorithm for Optimal Learning Problems

Volker Wieland

No 193, Computing in Economics and Finance 2005 from Society for Computational Economics

Abstract: This paper presents a numerical nonlinear dynamic programming algorithm for solving so-called optimal learning or adaptive control problems. These are decision problems with unknown parameters where the decisionmaker updates beliefs by Bayes rule. The updating equations are nonlinear. As a result the dynamic decision problem exhibits mulitiple optima, nondifferentiability of the value function and discontinuity of the policy function. Computational complexity rises quickly as multiple state variables are need to describe the evolution of the decisionmaker's beliefs. The algorithm presented delivers approximations to optimal policies for a class of optimal learning problems.

Keywords: numerical methods; optimal learning; nonlinear dynamic programming (search for similar items in EconPapers)
JEL-codes: C61 C63 (search for similar items in EconPapers)
Date: 2005-11-11
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf5:193

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