Multidimensional Risk Made Easy
Abstract
Suppose we want to assign a certainty equivalent--one number--to a multivariate risk. Which such assignments are law-invariant, monotone with respect to vector stochastic dominance, and invariant to independent background risk? I show that every such certainty equivalent is a positive mixture of scalar entropic certainty equivalents applied to positive projections of the vector risk. The same representation yields a robust-order characterization: unanimity across such certainty equivalents is equivalent, up to closure, to dominance after adding independent multidimensional background risk. In a social-welfare specialization, the corresponding shadow valuations are welfare weights.引用本文复制引用
Mark Whitmeyer.Multidimensional Risk Made Easy[EB/OL].(2026-07-01)[2026-07-04].https://arxiv.org/abs/2607.01229.学科分类
数学
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