# A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels

arXiv: https://arxiv.org/abs/2004.05629 · HTML: https://mlsanity.com/paper/2004.05629/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2020.

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

- [Mapping Human Anti-collusion Mechanisms to Multi-agent AI Systems](https://mlsanity.com/paper/2601.00360/) (2026)
- [Deep learning for detecting bid rigging: Flagging cartel participants based on convolutional neural networks](https://mlsanity.com/paper/2104.11142/) (2021)
- [Collusion Detection with Graph Neural Networks](https://mlsanity.com/paper/2410.07091/) (2024)

## Abstract (arXiv)

We propose a new method for flagging bid rigging, which is particularly useful for detecting incomplete bid-rigging cartels. Our approach combines screens, i.e. statistics derived from the distribution of bids in a tender, with machine learning to predict the probability of collusion. As a methodological innovation, we calculate such screens for all possible subgroups of three or four bids within a tender and use summary statistics like the mean, median, maximum, and minimum of each screen as predictors in the machine learning algorithm. This approach tackles the issue that competitive bids in incomplete cartels distort the statistical signals produced by bid rigging. We demonstrate that our algorithm outperforms previously suggested methods in applications to incomplete cartels based on empirical data from Switzerland.