# Learn then Decide: A Learning Approach for Designing Data Marketplaces

arXiv: https://arxiv.org/abs/2503.10773 · HTML: https://mlsanity.com/paper/2503.10773/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [The Sample Complexity of Revenue Maximization](https://mlsanity.com/paper/1502.00963/) (2015)
- [A Survey on Data Pricing: from Economics to Data Science](https://mlsanity.com/paper/2009.04462/) (2020)
- [Learning Multi-item Auctions with (or without) Samples](https://mlsanity.com/paper/1709.00228/) (2017)
- [Making the Most of Your Samples](https://mlsanity.com/paper/1407.2479/) (2014)
- [Learning Strategies in Decentralized Matching Markets under Uncertain Preferences](https://mlsanity.com/paper/2011.00159/) (2020)
- [Learning in Multi-Stage Decentralized Matching Markets](https://mlsanity.com/paper/2102.06988/) (2021)
- [No-Regret Learning in Partially-Informed Auctions](https://mlsanity.com/paper/2202.10606/) (2022)
- [Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model](https://mlsanity.com/paper/2303.08613/) (2023)
- [Mechanism Design for Data Science](https://mlsanity.com/paper/1404.5971/) (2014)
- [Nonparametric Estimation of Repeated Densities with Heterogeneous Sample Sizes](https://mlsanity.com/paper/2012.10009/) (2020)

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

- [A Sustainable AI Economy Needs Data Deals That Work for Generators](https://mlsanity.com/paper/2601.09966/) (2026)

## Abstract (arXiv)

As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism, a novel two-stage approach that first estimates the bidders' value distribution through auctions and then determines the optimal posted price based on the learned distribution. We establish that MAPP is individually rational and incentive-compatible, ensuring truthful bidding while balancing revenue maximization with minimal price discrimination. On the theoretical side, we establish a statistical viewpoint that recasts revenue optimization as a valuation density estimation problem: we show that revenue regret can be controlled by uniform error in estimating the valuation density. MAPP achieves a regret of Op(n-1(log n)2) when incorporating historical bid data, where n is the number of bids in the current round. For sequential dataset sales over T rounds, we propose an online MAPP mechanism that dynamically adjusts pricing across datasets with varying value distributions. Our approach achieves no-regret learning, with the average cumulative regret converging at a rate of Op(T-1/2(log T)2). We validate the effectiveness of MAPP through simulations and real-world data from the FCC AWS-3 spectrum auction.