# Equilibrium of Data Markets with Externality

arXiv: https://arxiv.org/abs/2302.08012 · HTML: https://mlsanity.com/paper/2302.08012/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [Bandits and Experts in Metric Spaces](https://mlsanity.com/paper/1312.1277/) (2013)
- [Selling to a No-Regret Buyer](https://mlsanity.com/paper/1711.09176/) (2017)
- [Online learning in repeated auctions](https://mlsanity.com/paper/1511.05720/) (2015)
- [A Survey on Data Pricing: from Economics to Data Science](https://mlsanity.com/paper/2009.04462/) (2020)
- [Truthful Data Acquisition via Peer Prediction](https://mlsanity.com/paper/2006.03992/) (2020)
- [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)
- [Selling Information in Competitive Environments](https://mlsanity.com/paper/2202.08780/) (2022)
- [Peer Prediction for Learning Agents](https://mlsanity.com/paper/2208.04433/) (2022)
- [Selling Information Through Consulting](https://mlsanity.com/paper/1907.04397/) (2019)

## Built on by (most cited first, 3 of 3)

- [Strategic Classification With Externalities](https://mlsanity.com/paper/2410.08032/) (2024)
- [Data Sharing with a Generative AI Competitor](https://mlsanity.com/paper/2505.12386/) (2025)
- [Entry Barriers in Content Markets](https://mlsanity.com/paper/2509.01953/) (2025)

## Abstract (arXiv)

We model real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers, as a simultaneous game. A key component here is the negative externality buyers induce on one another due to data purchases. Starting with a simple setting where buyers know their valuations a priori, we characterize both the existence and welfare properties of the pure Nash equilibrium in the presence of such externality. While the outcomes are bleak without any intervention, mirroring the limitations of current data markets, we prove that for a standard class of externality functions, platforms intervening through a transaction cost can lead to a pure equilibrium with strong welfare guarantees. We next consider a more realistic setting where buyers learn their valuations over time through market interactions. Our intervention is feasible here as well, and we consider learning algorithms to achieve low regret concerning both individual and cumulative utility metrics. Lastly, we analyze the promises of this intervention under a much richer externality model.