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Multivariate Almost Stochastic Dominance: Transfer Characterizations and Sufficient Conditions Under Dependence Uncertainty

Author

Listed:
  • Alfred Müller

    (Department Mathematik, Universität Siegen, 57072 Siegen, Germany)

  • Marco Scarsini

    (Dipartimento di Economia e Finanza, Luiss University, 00197 Roma, Italy)

  • Ilia Tsetlin

    (INSEAD, Singapore 138676)

  • Robert L. Winkler

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

Abstract

Most often, important decisions involve several unknown attributes. This produces a double challenge in the sense that both assessing the individual multiattribute preferences and assessing the joint distribution of the attributes can be extremely hard. To handle the first challenge, we suggest multivariate almost stochastic dominance, a relation based on bounding marginal utilities. We provide necessary and sufficient characterizations in terms of simple transfers, which are easily communicated to decision makers and, thus, can be used for preference elicitation. To handle the second challenge, we develop sufficient conditions that do not consider the dependence structure and are based on either marginal distributions of the attributes or just their means and variances. We apply the theoretical results to a case study of comparing the efficiency of photovoltaic plants.

Suggested Citation

  • Alfred Müller & Marco Scarsini & Ilia Tsetlin & Robert L. Winkler, 2025. "Multivariate Almost Stochastic Dominance: Transfer Characterizations and Sufficient Conditions Under Dependence Uncertainty," Operations Research, INFORMS, vol. 73(2), pages 879-893, March.
    • Handle: RePEc:inm:oropre:v:73:y:2025:i:2:p:879-893
    DOI: 10.1287/opre.2022.0596
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    Keywords

    Decision Analysis;

    Statistics

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