How Active is Active Learning: Value Function Method Versus an Approximation Method

Hans Amman and Marco P. Tucci ()
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Marco P. Tucci: University of Siena

Computational Economics, 2020, vol. 56, issue 3, No 5, 675-693

Abstract: Abstract In a previous paper Amman et al. (Macroecon Dyn, 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 (J Econ Dyn Control 26:1359–1377, 2002), they find 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.

Keywords: Optimal experimentation; Value function; Approximation method; Adaptive control; Active learning; Time-varying parameters; Numerical experiments (search for similar items in EconPapers)
JEL-codes: C63 E61 E62 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10614-020-09968-2

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