# Selling Information in Competitive Environments

arXiv: https://arxiv.org/abs/2202.08780 · HTML: https://mlsanity.com/paper/2202.08780/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2022.

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

- [Data Market Design through Deep Learning](https://mlsanity.com/paper/2310.20096/) (2023)
- [Equilibrium of Data Markets with Externality](https://mlsanity.com/paper/2302.08012/) (2023)
- [Bayesian Regression Markets](https://mlsanity.com/paper/2310.14992/) (2023)

## Abstract (arXiv)

Data buyers compete in a game of incomplete information about which a single data seller owns some payoff-relevant information. The seller faces a joint information- and mechanism-design problem: deciding which information to sell, while eliciting the buyers' types and imposing payments. We derive the welfare- and revenue-optimal mechanisms for a class of games with binary actions and states. Our results highlight the critical properties of selling information in competitive environments: (i) the negative externalities arising from buyer competition increase the profitability of recommending the correct action to one buyer exclusively; (ii) for the buyers to follow the seller's recommendations, the degree of exclusivity must be limited; (iii) the buyers' obedience constraints also limit the distortions in the allocation of information introduced by a monopolist seller; (iv) as competition becomes fiercer, these limitations become more severe, weakening the impact of market power on the allocation of information.