Abstract
For families of dynamic programming problems, the concept of ε-horizon (approximative horizon) is introduced. Intuitively h is an ε-horizon, ε >0, iff the knowledge of conditions for h steps ahead, allow us to make decisions with the final efect differing from the optimal by not more than ε.
The notion of ε-horizon is applied to families of linear growth programming problems, where in every step a strongly indecomposable von Neumann Leontieff type model serves as technology and we maximize the rate of expansion of the final production over a final composition boundle.
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References
S. Karlin: Mathematical Methods and Theory in Games, Programming and Economics 1 (1959), Pergamon Press.
J. Loé: Horizon in Dynamic Programs. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. California University Press (1967), p. 479–490.
M. Morishima: Equilibrium Stability and Growth. Oxford University Press (1964).
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© 1971 Springer-Verlag Wien
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Łoś, J. (1971). The Approximative Horizon in Von Neumann Models of Optimal Growth. In: Bruckmann, G., Weber, W. (eds) Contributions to the Von Neumann Growth Model. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-24667-2_10
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DOI: https://doi.org/10.1007/978-3-662-24667-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-22738-1
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