# Homogeneous Algorithms Can Reduce Competition in Personalized Pricing

arXiv: https://arxiv.org/abs/2503.15634 · HTML: https://mlsanity.com/paper/2503.15634/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2025.

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

- [The Subjectivity of Monoculture](https://mlsanity.com/paper/2602.24086/) (2026)
- [Algorithmic Monocultures in Hiring](https://mlsanity.com/paper/2605.27371/) (2026)
- [Breaking Algorithmic Collusion in Human-AI Ecosystems](https://mlsanity.com/paper/2511.21935/) (2025)

## Abstract (arXiv)

Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data, aim at similar benchmarks, or rely on similar pre-trained models, the result is correlated predictions. We model the impact of correlated algorithms on competition in the context of personalized pricing. Our analysis reveals that (1) higher correlation diminishes consumer welfare and (2) as consumers become more price sensitive, firms are increasingly incentivized to compromise on the accuracy of their predictions in exchange for coordination. We demonstrate our theoretical results in a stylized empirical study where two firms compete using personalized pricing algorithms. Our results underscore the ease with which algorithms facilitate price correlation without overt communication, which raises concerns about a new frontier of anti-competitive behavior. We analyze the implications of our results on the application and interpretation of US antitrust law.