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Stochastic programming: Potential hazards when random variables reflect market interaction

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  • Kjetil Haugen
  • Stein Wallace

Abstract

There are two types of random phenomena modeled in stochastic programs. One type is what we may term “external” or “natural” random variables, such as temperature or the roll of a dice. But in many other cases, random variables are used to reflect the behavior of other market participants. This is the case for such as price and demand of a product. Using simple game theoretic models, we demonstrate that stochastic programming may not be appropriate in these cases, as there may be no feasible way to replace the decisions of others by a random variable, and arrive at the correct decision. Hence, this simple note is a warning against certain types of stochastic programming models. Stochastic programming is unproblematic in pure forms of monopoly and perfect competition, and also with respect to external random phenomena. But if market power is involved, such as in oligopolies, the modeling may not be appropriate. Copyright Springer Science + Business Media, Inc. 2006

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  • Kjetil Haugen & Stein Wallace, 2006. "Stochastic programming: Potential hazards when random variables reflect market interaction," Annals of Operations Research, Springer, vol. 142(1), pages 119-127, February.
    • Handle: RePEc:spr:annopr:v:142:y:2006:i:1:p:119-127:10.1007/s10479-006-6164-0
    DOI: 10.1007/s10479-006-6164-0
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    References listed on IDEAS

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    1. Kanudia, Amit & Shukla, PR, 1998. "Modelling of Uncertainties and Price Elastic Demands in Energy-environment Planning for India," Omega, Elsevier, vol. 26(3), pages 409-423, June.
    2. Eduardo Marco Modiano, 1987. "Derived Demand and Capacity Planning Under Uncertainty," Operations Research, INFORMS, vol. 35(2), pages 185-197, April.
    3. Stein W. Wallace, 2000. "Decision Making Under Uncertainty: Is Sensitivity Analysis of Any Use?," Operations Research, INFORMS, vol. 48(1), pages 20-25, February.
    4. Samer Takriti & Benedikt Krasenbrink & Lilian S.-Y. Wu, 2000. "Incorporating Fuel Constraints and Electricity Spot Prices into the Stochastic Unit Commitment Problem," Operations Research, INFORMS, vol. 48(2), pages 268-280, April.
    5. Hobbs, Benjamin F., 1995. "Optimization methods for electric utility resource planning," European Journal of Operational Research, Elsevier, vol. 83(1), pages 1-20, May.
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    Cited by:

    1. Wallace, Stein W. & Choi, Tsan-Ming, 2011. "Flexibility, information structure, options, and market power in robust supply chains," International Journal of Production Economics, Elsevier, vol. 134(2), pages 284-288, December.
    2. Colvin, Matthew & Maravelias, Christos T., 2010. "Modeling methods and a branch and cut algorithm for pharmaceutical clinical trial planning using stochastic programming," European Journal of Operational Research, Elsevier, vol. 203(1), pages 205-215, May.

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