Statistical robustness in utility preference robust optimization models

• 成果类型: Article
• 署名作者: Guo, Shaoyan; Xu, Huifu
• 署名单位: Dalian University of Technology; Chinese University of Hong Kong
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-020-01555-5
• 发表日期: 2021
• 页码: 679-720
• 关键词: DECISION-MAKING; qualitative robustness; expected utility; weak continuity; functionals; uncertainty; definition; ambiguity; LAW; set

摘要

Utility preference robust optimization (PRO) concerns decision making problems where information on decision maker's utility preference is incomplete and has to be elicited through partial information and the optimal decision is based on the worst case utility function elicited. A key assumption in the PRO models is that the true probability distribution is either known or can be recovered by real data generated by the true distribution. In data-driven optimization, this assumption may not be satisfied when perceived data differ from real data and consequently it raises a question as to whether statistical estimators of the PRO models based on perceived data are reliable. In this paper, we investigate the issue which is also known as qualitative robustness in the literature of statistics (Huber in Robust statistics, 3rd edn, Wiley, New York, 1981) and risk management (Kratschmer et al. in Finance Stoch 18:271-295, 2014). By utilizing the framework proposed by Kratschmer et al. (2014), we derive moderate sufficient conditions under which the optimal value and optimal solution of the PRO models are robust against perturbation of the exogenous uncertainty data, and examine how the tail behaviour of utility functions affects the robustness. Moreover, under some additional conditions on the Lipschitz continuity of the underlying functions with respect to random data, we establish quantitative robustness of the statistical estimators under the Kantorovich metric. Finally, we investigate uniform consistency of the optimal value and optimal solution of the PRO models. The results cover utility selection problems and stochastic optimization problems as special cases.