How Active is Active Learning: Value Function Method Versus an Approximation Method
| Autor: | Marco P. Tucci, Hans M. Amman |
|---|---|
| Přispěvatelé: | Equilibrium, Expectations & Dynamics / CeNDEF (ASE, FEB), Faculteit Economie en Bedrijfskunde |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adaptive control
Stationary process Optimal experimentation Value function Approximation method Adaptive control Active learning Time-varying parameters Numerical experiments Active learning Active learning (machine learning) 05 social sciences Economics Econometrics and Finance (miscellaneous) Control (management) Process (computing) Approximation method Computer Science Applications Time-varying parameters Control theory Bellman equation 0502 economics and business Value function Applied mathematics Optimal experimentation 050207 economics 050205 econometrics Mathematics Numerical experiments |
| Zdroj: | Computational Economics, 56(3), 675-693. Springer Netherlands |
| ISSN: | 0927-7099 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink