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An Approximated Solution to Continuous-Time Stochastic Optimal Control Problems Through Markov Decision Chains

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Author Info

  • Jacek B. Krawczyk

    (Victoria University of Wellington)

  • Alistair Windsor

    (Victoria University of Wellington)

Abstract

Strategies for constructing a Markov decision chain approximating a continuous-time finite-horizon optimal control problem are investigated. Some simple, analytically soluble, examples are treated and low computational complexity is reported. Extensions to the method and implementation are discussed. In particular, relevance of the approximated solution to a stochastic renewable resource valuation problem is examined.

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Bibliographic Info

Paper provided by EconWPA in its series Computational Economics with number 9710001.

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Length: 38 pages
Date of creation: 01 Oct 1997
Date of revision:
Handle: RePEc:wpa:wuwpco:9710001

Note: Type of Document - LaTeX; prepared on UNIX; to print on PostScript; pages: 38 ; figures: included
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Web page: http://128.118.178.162

Related research

Keywords: Approximating Markov decision chains; simple noise discretisation. Natural resource valuation.;

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Cited by:
  1. Azzato, Jeffrey & Krawczyk, Jacek, 2006. "SOCSol4L An improved MATLAB package for approximating the solution to a continuous-time stochastic optimal control problem," MPRA Paper 1179, University Library of Munich, Germany.
  2. Jacek B. Krawczyk, 2000. "A Markovian Approximated Solution To A Portfolio Management Problem," Computing in Economics and Finance 2000 233, Society for Computational Economics.
  3. Azzato, Jeffrey & Krawczyk, Jacek B & Sissons, Christopher, 2011. "On loss-avoiding lump-sum pension optimization with contingent targets," Working Paper Series 1532, Victoria University of Wellington, School of Economics and Finance.

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