Paper: arXiv 2509.01076

Authors: Chung-Han Hsieh, Rong Gan

Abstract

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 \emph{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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper provides a highly novel and mathematically rigorous theoretical framework for Distributionally Robust Optimization (DRO) with noisy data via inverse-image Wasserstein ambiguity sets. However, it is primarily a theoretical optimization paper focused on convex reformulations and statistical consistency guarantees, lacking extensive empirical or market backtesting.

Research Flowchart

  flowchart TD
    A["Research Question: Is Noisy Data a Blessing in Disguise for DRO?"] --> B["Inputs: Noisy Empirical Data & Known Noise Kernel"]
    B --> C["Methodology: Inverse-Image Ambiguity Set Construction"]
    C --> D["Process: Tractable Convex Reformulation"]
    D --> E["Theoretical Validation: Finite-Sample Guarantees & Consistency"]
    E --> F["Key Findings: Reduced Conservatism, Lower Price of Ambiguity & Fairer Resource Allocation"]