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Myopic Solutions of Homogeneous Sequential Decision Processes

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  • Matthew J. Sobel

    (Department of Operations, Weatherhead School of Management, Case Western Reserve University, Cleveland, Ohio 44106)

  • Wei Wei

    (Department of Operations, Weatherhead School of Management, Case Western Reserve University, Cleveland, Ohio 44106)

Abstract

An optimum of a Markov decision process (MDP) is myopic if it can be obtained by solving a series of static problems. Myopic optima are desirable because they can be computed relatively easily. We identify new classes of MDPs with myopic optima and sequential games with myopic equilibrium points. In one of the classes, the single-period reward is homogeneous with respect to the state variable. We illustrate the results with models of revenue management and investment.

Suggested Citation

  • Matthew J. Sobel & Wei Wei, 2010. "Myopic Solutions of Homogeneous Sequential Decision Processes," Operations Research, INFORMS, vol. 58(4-part-2), pages 1235-1246, August.
    • Handle: RePEc:inm:oropre:v:58:y:2010:i:4-part-2:p:1235-1246
    DOI: 10.1287/opre.1090.0767
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    References listed on IDEAS

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    Cited by:

    1. Kenneth Judd & Garrett van Ryzin, 2010. "Preface to the Special Issue on Computational Economics," Operations Research, INFORMS, vol. 58(4-part-2), pages 1035-1036, August.
    2. Stoll, Sebastian & Zöttl, Gregor, 2014. "Transparency in Buyer-Determined Auctions: Should Quality be Private or Public?," Discussion Paper Series of SFB/TR 15 Governance and the Efficiency of Economic Systems 459, Free University of Berlin, Humboldt University of Berlin, University of Bonn, University of Mannheim, University of Munich.
    3. Matthew J. Sobel & Volodymyr Babich, 2012. "Optimality of Myopic Policies for Dynamic Lot-Sizing Problems in Serial Production Lines with Random Yields and Autoregressive Demand," Operations Research, INFORMS, vol. 60(6), pages 1520-1536, December.

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