EconPapers    
Economics at your fingertips  
 

Near-Optimal Bayesian Ambiguity Sets for Distributionally Robust Optimization

Vishal Gupta ()
Additional contact information
Vishal Gupta: Data Science and Operations, University of Southern California Marshall School of Business, Los Angeles, California 90089

Management Science, 2019, vol. 65, issue 9, 4242-4260

Abstract: We propose a Bayesian framework for assessing the relative strengths of data-driven ambiguity sets in distributionally robust optimization (DRO) when the underlying distribution is defined by a finite-dimensional parameter. The key idea is to measure the relative size between a candidate ambiguity set and a specific asymptotically optimal set. As the amount of data grows large, this asymptotically optimal set is the smallest convex ambiguity set that satisfies a novel Bayesian robustness guarantee that we introduce. This guarantee is defined with respect to a given class of constraints and is a Bayesian analog of more common frequentist feasibility guarantees from the DRO literature. Using this framework, we prove that many popular existing ambiguity sets are significantly larger than the asymptotically optimal set for constraints that are concave in the ambiguity. By contrast, we construct new ambiguity sets that are tractable, satisfy our Bayesian robustness guarantee, and are at most a small, constant factor larger than the asymptotically optimal set; we call these sets Bayesian near-optimal. We further prove that asymptotically, solutions to DRO models with our Bayesian near-optimal sets enjoy strong frequentist robustness properties, despite their smaller size. Finally, our framework yields guidelines for practitioners selecting between competing ambiguity set proposals in DRO. Computational evidence in portfolio allocation using real and simulated data confirms that our framework, although motivated by asymptotic analysis in a Bayesian setting, provides practical insight into the performance of various DRO models with finite data under frequentist assumptions.

Keywords: robust optimization; data-driven optimization; Bayesian statistics (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.1287/mnsc.2018.3140 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:65:y:2019:i:9:p:4242-4260

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Matthew Walls ().

 
Page updated 2020-09-26
Handle: RePEc:inm:ormnsc:v:65:y:2019:i:9:p:4242-4260