# Decision-dependent Distributionally Robust Optimization

arXiv: https://arxiv.org/abs/2303.03971 · HTML: https://mlsanity.com/paper/2303.03971/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2023.

## Built on by (most cited first, 1 of 1)

- [Distributionally Robust Cooperative Multi-Agent Reinforcement Learning via Robust Value Factorization](https://mlsanity.com/paper/2602.11437/) (2026)

## Abstract (arXiv)

This work presents a new Distributionally Robust Optimization approach, using p-Wasserstein metrics, to analyze a stochastic program in a general context. The ambiguity set in this approach depends on the decision variable and is represented as a ball where both the center and the radius depend on the decision variable. We show that, under Lipschitz's assumptions for the objective function, our approach can be reformulated as a finite-dimensional optimization problem, which is sometimes convex. In addition, we numerically compare our proposed approach with the standard formulation of distributionally robust optimization, which typically does not use ambiguity sets dependent on the decision variable, in the context of portfolio optimization.