# Decision-Dependent Distributionally Robust Optimization with Application to Dynamic Pricing

arXiv: https://arxiv.org/abs/2508.06965 · HTML: https://mlsanity.com/paper/2508.06965/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2025.

## Built on by (most cited first, 2 of 2)

- [Decentralized Contextual Bandits with Network Adaptivity](https://mlsanity.com/paper/2508.13411/) (2025)
- [Distributionally Robust Cooperative Multi-Agent Reinforcement Learning via Robust Value Factorization](https://mlsanity.com/paper/2602.11437/) (2026)

## Abstract (arXiv)

We consider decision-making problems under decision-dependent uncertainty (DDU), where the distribution of uncertain parameters depends on the decision variables and is only observable through a finite offline dataset. To address this challenge, we formulate a decision-dependent distributionally robust optimization (DD-DRO) problem, and leverage multivariate interpolation techniques along with the Wasserstein metric to construct decision-dependent nominal distributions (thereby decision-dependent ambiguity sets) based on the offline data. We show that the resulting ambiguity sets provide a finite-sample, high-probability guarantee that the true decision-dependent distribution is contained within them. Furthermore, we establish key properties of the DD-DRO framework, including a non-asymptotic out-of-sample performance guarantee, an optimality gap bound, and a tractable reformulation. The practical effectiveness of our approach is demonstrated through numerical experiments on a dynamic pricing problem with nonstationary demand, where the DD-DRO solution produces pricing strategies with guaranteed expected revenue.