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

• 成果类型: Article
• 署名作者: Mueller, Alfred; Scarsini, Marco; Tsetlin, Ilia; Winkler, Robert L.
• 署名单位: Universitat Siegen; Luiss Guido Carli University; INSEAD Business School; Duke University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.0596
• 发表日期: 2025
• 关键词: risk aggregation; optimization; bounds; MODEL; Robustness

摘要

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.