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Dynamic optimization of some forward-looking stochastic models

  • Models And Control Policies In Economics
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Modeling and Control of Systems

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 121))

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Abstract

A dynamic decision model is said to be forward-looking if the evolution of the underlying system depends explicitly on the expectations the agents form on the future evolution itself. Such models lead to nonstandard stochastic dynamic optimization problems where one has to take into account the fact that there is a circular (closed) relationship between future forecasts and future system behavior. In this paper we study a class of such problems where there is an additional control input designed to make the system track a given trajectory. This leads to a game-theoretic formulation in which context we consider both finite and infinite horizon formulations. It is shown that for the finite horizon problem the unique Nash equilibrium solution requires (fixed size) memory for both agents because of spillover across stages, whereas for the infinite horizon version no memory is needed.

This work was performed while the author was spending a sabbatical year at INRIA, Sophia Antipolis, France, and it was also partially supported by the Air Force Office of Scientific Research under Grant No. AFOSR 084-0056, through the University of Illinois.

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Authors and Affiliations

  1. Decision and Control Laboratory Coordinated Science Laboratory and the Department of Electrical and Computer Engineering, University of Illinois, 1101 W. Springfield Avenue, 61801, Urbana, Illinois, USA

    Tamer Başar

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  1. Tamer Başar
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Austin Blaquiére

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© 1989 Springer-Verlag

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Başar, T. (1989). Dynamic optimization of some forward-looking stochastic models. In: Blaquiére, A. (eds) Modeling and Control of Systems. Lecture Notes in Control and Information Sciences, vol 121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0041201

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  • DOI: https://doi.org/10.1007/BFb0041201

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-50790-1

  • Online ISBN: 978-3-540-46087-9

  • eBook Packages: Springer Book Archive

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