Survey of Data-driven Newsvendor: Unified Analysis and Spectrum of Achievable Regrets

Unifying the Regret Spectrum in Data-Driven Newsvendor The data-driven newsvendor problem seeks to optimize inventory decisions using samples from an unknown demand distribution. Although this problem has attracted significant attention, previous studies have typically analyzed specific distribution classes or regret definitions in isolation. In “Survey of the Data-Driven Newsvendor Problem: Unified Analysis and Spectrum of Achievable Regrets,” Chen and Ma present a unified analysis that synthesizes these settings and simplifies existing proofs. The study utilizes a notion of clustered distributions defined via the cumulative distribution function (CDF). This approach demonstrates that the achievable regret covers the entire spectrum of convergence rates between $1/\sqrt{n}$ and $1/n$. Beyond the theoretical unification, the authors show through simulations that this CDF-based notion accurately predicts the empirical regret and captures how the difficulty of the problem evolves with sample size. This work provides insights into understanding the value of data in the newsvendor problem and, more broadly, decision making under uncertainty.