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

Hans Amman and Marco Paolo Tucci ()

Department of Economics University of Siena from Department of Economics, University of Siena

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.

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: 2018-10
New Economics Papers: this item is included in nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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