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Preference Factoring for Stochastic Trees


  • Gordon Hazen

    () (Department of Industrial Engineering and Management Sciences, Northwestern University, Technological Institute, Evanston, Illinois 60208-3119)


Stochastic trees are extensions of decision trees that facilitate the modeling of temporal uncertainties. Their primary application has been to medical treatment decisions. It is often convenient to present stochastic trees in factored form, allowing loosely coupled pieces of the model to be formulated and presented separately. In this paper, we show how the notion of factoring can be extended as well to preference components of the stochastic model. We examine updateable-state utility, a flexible class of expected utility models that permit stochastic trees to be rolled back much in the manner of decision trees. We show that preference summaries for updateable-state utility can be factored out of the stochastic tree. In addition, we examine utility decompositions which can arise when factors in a stochastic tree are treated as attributes in a multiattribute utility function.

Suggested Citation

  • Gordon Hazen, 2000. "Preference Factoring for Stochastic Trees," Management Science, INFORMS, vol. 46(3), pages 389-403, March.
  • Handle: RePEc:inm:ormnsc:v:46:y:2000:i:3:p:389-403

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    References listed on IDEAS

    1. Pellissier, James M. & Hazen, Gordon B., 1994. "Implementation of continuous risk utility assessment: The total hip replacement decision," Socio-Economic Planning Sciences, Elsevier, vol. 28(4), pages 251-276, December.
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    Cited by:

    1. C. Armero & G. García-Donato & A. López-Quílez, 2010. "Bayesian methods in cost-effectiveness studies: objectivity, computation and other relevant aspects," Health Economics, John Wiley & Sons, Ltd., vol. 19(6), pages 629-643.


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