A Comparison of Parametric Approximation Techniques to Continuous-Time Stochastic Dynamic Programming Problems

Tom Kompas and Long Chu

Environmental Economics Research Hub Research Reports from Environmental Economics Research Hub, Crawford School of Public Policy, The Australian National University

Abstract: We compare three parametric techniques to approximate Hamilton-Jacobi-Bellman equations via unidimensional and multidimensional problems. The linear programming technique is very efficient for unidimensional problems and offers a balance of speed and accuracy for multidimensional problems. A comparable projection technique is shown to be slow, but has stable accuracy, whereas a perturbation technique has the least accuracy although its speed suffers least from the curse of dimensionality. The linear programming technique is also shown to be suitable for problems in resource management, including applications to biosecurity and marine reserve design.

Date: 2010-09
New Economics Papers: this item is included in nep-dge
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Working Paper: A Comparison of Parametric Approximation Techniques to Continuous-Time Stochastic Dynamic Programming Problems (2010)
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