Dynamic Distributionally Robust Optimization With Adaptive Ambiguity Sets: A Machine Learning-Enhanced Framework for Market Decision-Making

We propose a dynamic distributionally robust optimization (DRO) framework to address strategic decision-making in environments where uncertainty patterns change over time. Traditional optimization methods assume fixed uncertainty distributions, making them unsuitable for volatile business environments with evolving market conditions. Our framework introduces three key innovations: First, we develop adaptive ambiguity sets that automatically adjust their size and shape based on observed data patterns, using Wasserstein distance metrics to maintain mathematical rigor while responding to distributional changes. Second, we employ deep neural networks to process diverse data streams and detect when the underlying uncertainty structure has fundamentally shifted, enabling rapid adaptation to new market regimes. Third, we establish theoretical performance guarantees that bound the system’s regret even when facing non-stationary conditions.The framework combines short-term operational decisions with long-term strategic planning through a dual-layer architecture. Extensive experiments across real-world business datasets demonstrate 7.3% improvement in strategic alignment, and achieving 50.2% faster adaptation to regime changes compared to reinforcement learning-based methods. The approach provides organizations with a mathematically principled yet practical tool for maintaining competitive advantage under uncertainty.