# Coopetition Against an Amazon

arXiv: https://arxiv.org/abs/2005.10038 · HTML: https://mlsanity.com/paper/2005.10038/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

## Built on by (most cited first, 6 of 6)

- [Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning](https://mlsanity.com/paper/2410.19321/) (2024)
- [Incentivizing Honesty among Competitors in Collaborative Learning and Optimization](https://mlsanity.com/paper/2305.16272/) (2023)
- [Strategic Usage in a Multi-Learner Setting](https://mlsanity.com/paper/2401.16422/) (2024)
- [Prediction-sharing During Training and Inference](https://mlsanity.com/paper/2403.17515/) (2024)
- [Pricing AI Model Accuracy](https://mlsanity.com/paper/2504.13375/) (2025)
- [Data Sharing with a Generative AI Competitor](https://mlsanity.com/paper/2505.12386/) (2025)

## Abstract (arXiv)

This paper studies cooperative data-sharing between competitors vying to predict a consumer's tastes. We design optimal data-sharing schemes both for when they compete only with each other, and for when they additionally compete with an Amazon -- a company with more, better data. We show that simple schemes -- threshold rules that probabilistically induce either full data-sharing between competitors, or the full transfer of data from one competitor to another -- are either optimal or approximately optimal, depending on properties of the information structure. We also provide conditions under which firms share more data when they face stronger outside competition, and describe situations in which this conclusion is reversed.