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Stochastic Programming: Non-Anticipativity and Lagrange Multipliers

Author

Listed:
  • Evstigneev, I.V.
  • Flam, S.D.

Abstract

Introduction: Decision making under uncertainty can often be formalized as a stochastic program, constrained not merely in material terms, but also by limited information. The former type of constraints, accounting for material bound, is usually described by inequalities required to hold almost surely. The latter type, reflecting informational restrictions, often assumes the form of linear equations involving conditional expectation operators...

Suggested Citation

  • Evstigneev, I.V. & Flam, S.D., 2000. "Stochastic Programming: Non-Anticipativity and Lagrange Multipliers," Norway; Department of Economics, University of Bergen 1100, Department of Economics, University of Bergen.
  • Handle: RePEc:fth:bereco:1100
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    Citations

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    Cited by:

    1. S. Flåm & L. Koutsougeras, 2010. "Private information, transferable utility, and the core," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 42(3), pages 591-609, March.
    2. Borglin, Anders & Flåm, Sjur, 2007. "Risk Exchange as a Market or Production Game," Working Papers 2007:16, Lund University, Department of Economics.
    3. Flåm, Sjur, 2007. "Option Pricing by Mathematical Programming," Working Papers 2007:10, Lund University, Department of Economics.
    4. Igor Evstigneev & Klaus Reiner Schenk-Hoppé, 2006. "Stochastic equilibria in von Neumann–Gale dynamical systems," The School of Economics Discussion Paper Series 0620, Economics, The University of Manchester.
    5. Flåm, S. D. & Ermoliev, Y. M., 2009. "Investment, uncertainty, and production games," Environment and Development Economics, Cambridge University Press, vol. 14(01), pages 51-66, February.

    More about this item

    Keywords

    LAGRANGE MULTIPLIERS ; STOCHATIQUE PROGRAMS;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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