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Optimal experimentation and the perturbation method in the neighborhood of the augmented linear regulator problem

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Listed author(s):
  • Cosimano, Thomas F.

Abstract

The perturbation method is used to approximate optimal experimentation problems. The approximation is in the neighborhood of the linear regulator (LR) problem. The first order perturbation of the optimal decision under experimentation is a combination of the LR solution and a term that captures the impact of the uncertainty on the agent's value function. An algorithm is developed in a companion paper to quickly implement this procedure on the computer. As a result, the impact of optimal experimentation on an agent's decisions can be quantified and estimated for a large class of problems encountered in economics.

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File URL: http://www.sciencedirect.com/science/article/pii/S0165-1889(07)00174-1
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Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 32 (2008)
Issue (Month): 6 (June)
Pages: 1857-1894

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Handle: RePEc:eee:dyncon:v:32:y:2008:i:6:p:1857-1894
Contact details of provider: Web page: http://www.elsevier.com/locate/jedc

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Cited by:

  1. 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.
  2. Dia, Enzo, 2013. "How do banks respond to shocks? A dynamic model of deposit-taking institutions," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3623-3638.
  3. Thierry Bréchet & Natali Hritonenko & Yuri Yatsenko, 2013. "Adaptation and Mitigation in Long-term Climate Policy," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 55(2), pages 217-243, June.
  4. Ivan Savin & Dmitri Blueschke, 2016. "Lost in Translation: Explicitly Solving Nonlinear Stochastic Optimal Control Problems Using the Median Objective Value," Computational Economics, Springer;Society for Computational Economics, vol. 48(2), pages 317-338, August.
  5. Hans Amman & David Kendrick, 2014. "Comparison of policy functions from the optimal learning and adaptive control frameworks," Computational Management Science, Springer, vol. 11(3), pages 221-235, July.
  6. Tim Willems, 2013. "Actively Learning by Pricing: A Model of an Experimenting Seller," Economics Series Working Papers 687, University of Oxford, Department of Economics.
  7. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.
  8. H.M. Amman & D.A. Kendrick, 2012. "Conjectures on the policy function in the presence of optimal experimentation," Working Papers 12-09, Utrecht School of Economics.

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