Inverse stochastic dominance constraints and rank dependent expected utility theory

• 成果类型: Article
• 署名作者: Dentcheva, Darinka; Ruszczynski, Andrzej
• 署名单位: Stevens Institute of Technology; Rutgers University System; Rutgers University New Brunswick
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-006-0712-x
• 发表日期: 2006
• 页码: 297-311
• 关键词: dual theory; optimization; RISK

摘要

We consider optimization problems with second order stochastic dominance constraints formulated as a relation of Lorenz curves. We characterize the relation in terms of rank dependent utility functions, which generalize Yaari's utility functions. We develop optimality conditions and duality theory for problems with Lorenz dominance constraints. We prove that Lagrange multipliers associated with these constraints can be identified with rank dependent utility functions. The problem is numerically tractable in the case of discrete distributions with equally probable realizations.