An Approximated Solution to Continuous-Time Stochastic Optimal Control Problems Through Markov Decision Chains
AbstractStrategies 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 InfoPaper provided by EconWPA in its series Computational Economics with number 9710001.
Length: 38 pages
Date of creation: 01 Oct 1997
Date of revision:
Note: Type of Document - LaTeX; prepared on UNIX; to print on PostScript; pages: 38 ; figures: included
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Approximating Markov decision chains; simple noise discretisation. Natural resource valuation.;
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- 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.
- Jacek B. Krawczyk, 2000. "A Markovian Approximated Solution To A Portfolio Management Problem," Computing in Economics and Finance 2000 233, Society for Computational Economics.
- 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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