2025/09/01 by Chung-Han Hsieh, Hsieh, Chung-Han, Gan, Rong
Business, Management and Accounting · Computer Science · Decision Sciences · #90B50 #90C17 #90C25 #90C34 #90C47 #FOS: Economics and business #FOS: Electrical engineering #FOS: Mathematics #Facility Location and Emergency Management #Optimization and Control (math.OC) #Risk Management (q-fin.RM) #Risk and Portfolio Optimization #Stochastic Gradient Optimization Techniques #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2509.01076
openalex publication_date 2025/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Noisy data are often viewed as a challenge for decision-making. This paper studies a distributionally robust optimization (DRO) that shows how such noise can be systematically incorporated. Rather than applying DRO to the noisy empirical distribution, we construct ambiguity sets over the latent distribution by centering a Wasserstein ball at the noisy empirical distribution in the observation space and taking its inverse image through a known noise kernel. We validate this inverse-image construction by deriving a tractable convex reformulation and establishing rigorous statistical guarantees, including finite-sample performance and asymptotic consistency. Crucially, we demonstrate that, under mild conditions, noisy data may be a ``blessing in disguise." Our noisy-data DRO model is less conservative than its direct counterpart, leading to provably higher optimal values and a lower price of ambiguity. In the context of fair resource allocation problems, we demonstrate that this robust approach can induce solutions that are structurally more equitable. Our findings suggest that managers can leverage uncertainty by harnessing noise as a source of robustness rather than treating it as an obstacle, producing more robust and strategically balanced decisions.