# Learning to Price with Persuasion

arXiv: https://arxiv.org/abs/2608.16699 · HTML: https://mlsanity.com/paper/2608.16699/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 51 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [The Sample Complexity of Revenue Maximization](https://mlsanity.com/paper/1502.00963/) (2015)
- [Sample Complexity of Automated Mechanism Design](https://mlsanity.com/paper/1606.04145/) (2016)
- [Settling the Sample Complexity of Single-parameter Revenue Maximization](https://mlsanity.com/paper/1904.04962/) (2019)
- [Learning Simple Auctions](https://mlsanity.com/paper/1604.03171/) (2016)
- [Ironing in the Dark](https://mlsanity.com/paper/1511.06918/) (2015)
- [Learning to Persuade on the Fly: Robustness Against Ignorance](https://mlsanity.com/paper/2102.10156/) (2021)
- [Multi-Receiver Online Bayesian Persuasion](https://mlsanity.com/paper/2106.06480/) (2021)
- [Generalization Guarantees for Multi-item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms](https://mlsanity.com/paper/1705.00243/) (2017)
- [Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information](https://mlsanity.com/paper/2502.00204/) (2025)
- [How to Sell Information Optimally: an Algorithmic Study](https://mlsanity.com/paper/2011.14570/) (2020)
- [Regret-Minimizing Bayesian Persuasion](https://mlsanity.com/paper/2105.13870/) (2021)
- [Online Bayesian Persuasion Without a Clue](https://mlsanity.com/paper/2411.06141/) (2024)
- [New Guarantees for Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs](https://mlsanity.com/paper/2302.11700/) (2023)
- [Learning in Structured Stackelberg Games](https://mlsanity.com/paper/2504.09006/) (2025)
- [How to Sell High-Dimensional Data Optimally](https://mlsanity.com/paper/2510.15214/) (2025)

## Abstract (arXiv)

Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design. Specifically, we consider the economic setting recently introduced by Bergemann et al. (2022), where in addition to the menu of quality-price pairs, the seller offers information on the value of the match between product quality and buyer's taste via a signaling scheme. We relax the assumption that the seller knows the buyers' belief about the distribution of tastes and study the sample requirements of designing a revenue maximizing scheme. We consider both the batch setting where we have access to data from a set of i.i.d. buyers and an online demand query model where we observe the buyers' behaviors to seller's schemes. Despite the apparent non-convexity of the problem, we also give the first FPTAS to compute a scheme that maximizes the revenue within an arbitrarily small additive loss, which was left open by Bergemann et al. (2022). Overall, this brings a new learning perspective in asymmetric economic settings where buyers and sellers know different types of information.