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Persistently optimal policies in stochastic dynamic programming with generalized discounting

Author

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  • Jaśkiewicz, Anna
  • Matkowski, Janusz
  • Nowak, Andrzej S.

Abstract

In this paper we study a Markov decision process with a non-linear discount function. Our approach is in spirit of the von Neumann-Morgenstern concept and is based on the notion of expectation. First, we define a utility on the space of trajectories of the process in the finite and infinite time horizon and then take their expected values. It turns out that the associated optimization problem leads to a non-stationary dynamic programming and an infinite system of Bellman equations, which result in obtaining persistently optimal policies. Our theory is enriched by examples.

Suggested Citation

  • Jaśkiewicz, Anna & Matkowski, Janusz & Nowak, Andrzej S., 2011. "Persistently optimal policies in stochastic dynamic programming with generalized discounting," MPRA Paper 31755, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:31755
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    References listed on IDEAS

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    Cited by:

    1. Anna Jaśkiewicz & Janusz Matkowski & Andrzej Nowak, 2014. "On variable discounting in dynamic programming: applications to resource extraction and other economic models," Annals of Operations Research, Springer, vol. 220(1), pages 263-278, September.

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    More about this item

    Keywords

    Stochastic dynamic programming; Persistently optimal policies; Variable discounting; Bellman equation; Resource extraction; Growth theory;
    All these keywords.

    JEL classification:

    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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