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Stochastic Decision Trees for the Analysis of Investment Decisions


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  • Richard F. Hespos

    (McKinsey and Company, Inc., New York)

  • Paul A. Strassmann

    (National Dairy Products Corporation, New York)

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    This paper describes an improved method for investment decision making. The method, which is called the stochastic decision tree method, is particularly applicable to investments characterized by high uncertainty and requiring a sequence of related decisions to be made over a period of time. The stochastic decision tree method builds on concepts used in the risk analysis method and the decision tree method of analyzing investments. It permits the use of subjective probability estimates or empirical frequency distributions for some or all factors affecting the decision. This application makes it practicable to evaluate all or nearly all feasible combinations of decisions in the decision tree, taking account of both expected value of return and aversion to risk, thus arriving at an optimal or near optimal set of decisions. Sensitivity analysis of the model can highlight factors that are critical because of high leverage on the measure of performance, or high uncertainty, or both. The method can be applied relatively easily to a wide variety of investment situations, and is ideally suited for computer simulation.

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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 11 (1965)
    Issue (Month): 10 (August)
    Pages: B244-B259

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    Handle: RePEc:inm:ormnsc:v:11:y:1965:i:10:p:b244-b259

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    Cited by:
    1. Heidenberger, Kurt, 1996. "Dynamic project selection and funding under risk: A decision tree based MILP approach," European Journal of Operational Research, Elsevier, vol. 95(2), pages 284-298, December.
    2. Collan, Mikael, 2004. "Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments," MPRA Paper 4328, University Library of Munich, Germany.
    3. Anderson, Jock R., 1972. "An Overview of Modelling in Agricultural Management," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 40(03), September.
    4. Boguslaw Nowak & Maciej Nowak & Tadeusz Trzaskalik, 2011. "Multicriteria decision aiding in project planning using dynamic programming and simulation," RePAd Working Paper Series UQO-DSA-wp2202011, D├ępartement des sciences administratives, UQO.
    5. Anderson, Kim B. & Holt, John, 1977. "A User-Oriented Model For Incorporating Risk Into Short-Run Decisions," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 9(02), December.


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