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A Numerical Dynamic Programming Algorithm for Optimal Learning Problems

Author

Listed:
  • Volker Wieland

    (School of Business and Economics Goethe University of Frankfurt)

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.

Suggested Citation

  • Volker Wieland, 2005. "A Numerical Dynamic Programming Algorithm for Optimal Learning Problems," Computing in Economics and Finance 2005 193, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:193
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    Cited by:

    1. Wieland, Volker, 2000. "Learning by doing and the value of optimal experimentation," Journal of Economic Dynamics and Control, Elsevier, vol. 24(4), pages 501-534, April.

    More about this item

    Keywords

    numerical methods; optimal learning; nonlinear dynamic programming;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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