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International Conference on Algorithmic Decision Theory

ADT 2009: Algorithmic Decision Theory pp 365–376Cite as

Algorithmic Aspects of Scenario-Based Multi-stage Decision Process Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5783))

Abstract

Multi-stage decision optimization under uncertainty depends on a careful numerical approximation of the underlying stochastic process, which describes the future uncertain values on which the decision will depend on. The quality of the scenario model severely affects the quality of the solution of the optimization model. Various approaches towards an optimal generation of discrete-state approximations (represented as scenario trees) have been suggested in the literature. Direct scenario tree sampling based on historical data or econometric models, as well as scenario path simulation and optimal tree approximation methods are discussed from an algorithmic perspective. A multi-stage financial asset management decision optimization model is presented to outline strategies to analyze the impact of various algorithmic scenario generation methodologies.

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Author information

Authors and Affiliations

  1. Department of Statistics and Decision Support Systems, University of Vienna, Vienna, Austria

    Ronald Hochreiter

Authors
  1. Ronald Hochreiter
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Editor information

Editors and Affiliations

  1. Department of Pure and Applied Mathematics, University of Padova, Via Trieste 63, 35121, Padova, Italy

    Francesca Rossi

  2. LAMSADE-CNRS, University of Paris Dauphine, Place du Maréchal De Lattre de Tassigny, 75775, Paris Cedex 16, France

    Alexis Tsoukias

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Hochreiter, R. (2009). Algorithmic Aspects of Scenario-Based Multi-stage Decision Process Optimization. In: Rossi, F., Tsoukias, A. (eds) Algorithmic Decision Theory. ADT 2009. Lecture Notes in Computer Science(), vol 5783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04428-1_32

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  • DOI: https://doi.org/10.1007/978-3-642-04428-1_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04427-4

  • Online ISBN: 978-3-642-04428-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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