# Optimal Pricing of Information

arXiv: https://arxiv.org/abs/2102.13289 · HTML: https://mlsanity.com/paper/2102.13289/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2021.

## Built on by (most cited first, 4 of 4)

- [Data Market Design through Deep Learning](https://mlsanity.com/paper/2310.20096/) (2023)
- [How to Sell High-Dimensional Data Optimally](https://mlsanity.com/paper/2510.15214/) (2025)
- [OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Items](https://mlsanity.com/paper/2609.01933/) (2026)
- [Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets](https://mlsanity.com/paper/2609.33289/) (2026)

## Abstract (arXiv)

A decision maker is choosing between an active action (e.g., purchase a house, invest certain stock) and a passive action. The payoff of the active action depends on the buyer's private type and also an unknown state of nature. An information seller can design experiments to reveal information about the realized state to the decision maker, and would like to maximize profit from selling such information. We characterize, in closed-form, the revenue-optimal information selling mechanism for the seller. After eliciting the buyer's type, the optimal mechanism charges the buyer an upfront payment and then simply reveals whether the realized state exceeds a certain threshold or not. The optimal mechanism features both price discrimination and information discrimination. The special buyer type who is a-priori indifferent between the active and passive action benefits the most from participating in the mechanism.