# Too Noisy to Collude? Algorithmic Collusion Under Laplacian Noise

arXiv: https://arxiv.org/abs/2509.02800 · HTML: https://mlsanity.com/paper/2509.02800/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [Mapping Human Anti-collusion Mechanisms to Multi-agent AI Systems](https://mlsanity.com/paper/2601.00360/) (2026)

## Abstract (arXiv)

The rise of autonomous pricing systems has sparked growing concern over algorithmic collusion in markets from retail to housing. This paper examines controlled information quality as an ex ante policy lever: by reducing the fidelity of data that pricing algorithms draw on, regulators can frustrate collusion before supracompetitive prices emerge. We show, first, that information quality is the central driver of competitive outcomes, shaping prices, profits, and consumer welfare. Second, we demonstrate that collusion can be slowed or destabilized by injecting carefully calibrated noise into pooled market data, yielding a feasibility region where intervention disrupts cartels without undermining legitimate pricing. Together, these results highlight information control as a lightweight yet practical lever to blunt digital collusion at its source.