# Data Market Design through Deep Learning

arXiv: https://arxiv.org/abs/2310.20096 · HTML: https://mlsanity.com/paper/2310.20096/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 26 of 26)

- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [Optimal Auctions through Deep Learning: Advances in Differentiable Economics](https://mlsanity.com/paper/1706.03459/) (2017)
- [Complexity of Mechanism Design](https://mlsanity.com/paper/1408.1486/) (2014)
- [A Permutation-Equivariant Neural Network Architecture For Auction Design](https://mlsanity.com/paper/2003.01497/) (2020)
- [Optimal-er Auctions through Attention](https://mlsanity.com/paper/2202.13110/) (2022)
- [A Scalable Neural Network for DSIC Affine Maximizer Auction Design](https://mlsanity.com/paper/2305.12162/) (2023)
- [PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning](https://mlsanity.com/paper/2106.03215/) (2021)
- [Differentiable Economics for Randomized Affine Maximizer Auctions](https://mlsanity.com/paper/2202.02872/) (2022)
- [Algorithmic Bayesian Persuasion](https://mlsanity.com/paper/1503.05988/) (2015)
- [Certifying Strategyproof Auction Networks](https://mlsanity.com/paper/2006.08742/) (2020)
- [ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning](https://mlsanity.com/paper/2010.06398/) (2020)
- [A Context-Integrated Transformer-Based Neural Network for Auction Design](https://mlsanity.com/paper/2201.12489/) (2022)
- [Deep Learning for Two-Sided Matching](https://mlsanity.com/paper/2107.03427/) (2021)
- [Learning Revenue-Maximizing Auctions With Differentiable Matching](https://mlsanity.com/paper/2106.07877/) (2021)
- [Towards Data Auctions with Externalities](https://mlsanity.com/paper/2003.08345/) (2020)
- [The Economics of Social Data](https://mlsanity.com/paper/2004.03107/) (2020)
- [How to Sell Information Optimally: an Algorithmic Study](https://mlsanity.com/paper/2011.14570/) (2020)
- [Bayesian Persuasion Meets Mechanism Design: Going Beyond Intractability with Type Reporting](https://mlsanity.com/paper/2202.00605/) (2022)
- [Optimal Pricing of Information](https://mlsanity.com/paper/2102.13289/) (2021)
- [On the Hardness of Signaling](https://mlsanity.com/paper/1402.4194/) (2014)
- [Selling Information in Competitive Environments](https://mlsanity.com/paper/2202.08780/) (2022)
- [Selling Information Through Consulting](https://mlsanity.com/paper/1907.04397/) (2019)
- [Welfare-Preserving varepsilon-BIC to BIC Transformation with Negligible Revenue Loss](https://mlsanity.com/paper/2007.09579/) (2020)
- [Third-Party Data Providers Ruin Simple Mechanisms](https://mlsanity.com/paper/1802.07407/) (2018)
- [Selling Data to an Agent with Endogenous Information](https://mlsanity.com/paper/2103.05788/) (2021)
- [Is Selling Complete Information (Approximately) Optimal?](https://mlsanity.com/paper/2202.09013/) (2022)

## Built on by (most cited first, 8 of 8)

- [GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning](https://mlsanity.com/paper/2406.07428/) (2024)
- [LLM Bidders Preserve the Mechanism-Level Orderings of Human Bidders](https://mlsanity.com/paper/2507.09083/) (2025)
- [Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts](https://mlsanity.com/paper/2407.18074/) (2024)
- [LLM-Powered Preference Elicitation in Combinatorial Assignment](https://mlsanity.com/paper/2502.10308/) (2025)
- [Automated Deterministic Auction Design with Objective Decomposition](https://mlsanity.com/paper/2402.11904/) (2024)
- [Incentive-Aligned Multi-Source LLM Summaries](https://mlsanity.com/paper/2509.25184/) (2025)
- [One Model, Two Markets: Bid-Aware Generative Recommendation](https://mlsanity.com/paper/2603.22231/) (2026)
- [Pick the Mixture, Not the Samples: Combinatorial Data Selection for LLMs](https://mlsanity.com/paper/2610.14028/) (2026)

## Abstract (arXiv)

The data market design problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller, where each experiment reveals some of the information known to a seller and has a corresponding price [Bergemann et al., 2018]. Each buyer has their own decision to make in a world environment, and their subjective expected value for the information associated with a particular experiment comes from the improvement in this decision and depends on their prior and value for different outcomes. In a setting with multiple buyers, a buyer's expected value for an experiment may also depend on the information sold to others [Bonatti et al., 2022]. We introduce the application of deep learning for the design of revenue-optimal data markets, looking to expand the frontiers of what can be understood and achieved. Relative to earlier work on deep learning for auction design [D"utting et al., 2023], we must learn signaling schemes rather than allocation rules and handle obedience constraints - these arising from modeling the downstream actions of buyers - in addition to incentive constraints on bids. Our experiments demonstrate that this new deep learning framework can almost precisely replicate all known solutions from theory, expand to more complex settings, and be used to establish the optimality of new designs for data markets and make conjectures in regard to the structure of optimal designs.