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How active is active learning: value function method vs an approximation method

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  • Hans M. Amman

    ()

  • Marco Paolo Tucci

    ()

Abstract

In a previous paper Amman and Tucci (2018) compare the two dominant approaches for solving models with optimal experimentation (also called active learning), i.e. the value function and the approximation method. By using the same model and dataset as in Beck and Wieland (2002), theyfind that the approximation method produces solutions close to those generated by the value function approach and identify some elements of the model specifications which affect the difference between the two solutions. They conclude that differences are small when the effects of learning are limited. However the dataset used in the experiment describes a situation where the controller is dealing with a nonstationary process and there is no penalty on the control. The goal of this paper is to see if their conclusions hold in the more commonly studied case of a controller facing a stationary process and a positive penalty on the control.

Suggested Citation

  • Hans M. Amman & Marco Paolo Tucci, 2018. "How active is active learning: value function method vs an approximation method," Department of Economics University of Siena 788, Department of Economics, University of Siena.
  • Handle: RePEc:usi:wpaper:788
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    File URL: http://repec.deps.unisi.it/quaderni/788.pdf
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    References listed on IDEAS

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    1. Wieland, Volker, 2000. "Monetary policy, parameter uncertainty and optimal learning," Journal of Monetary Economics, Elsevier, vol. 46(1), pages 199-228, August.
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    4. Tucci, Marco P. & Kendrick, David A. & Amman, Hans M., 2010. "The parameter set in an adaptive control Monte Carlo experiment: Some considerations," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1531-1549, September.
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    8. Beck, Gunter W. & Wieland, Volker, 2002. "Learning and control in a changing economic environment," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1359-1377, August.
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    Cited by:

    1. D. Blueschke & I. Savin & V. Blueschke-Nikolaeva, 2020. "An Evolutionary Approach to Passive Learning in Optimal Control Problems," Computational Economics, Springer;Society for Computational Economics, vol. 56(3), pages 659-673, October.

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    More about this item

    Keywords

    Optimal experimentation; value function; approximation method; adaptive control; active learning; time-varying parameters; numerical experiments.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy

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