Catching Bid-rigging Cartels with Graph Attention Neural Networks

TL;DR

This paper introduces a graph attention neural network approach for detecting bid-rigging cartels, demonstrating high accuracy and transferability across multiple markets, which can aid authorities in identifying collusive behavior.

Contribution

The paper presents a novel application of graph attention networks for cartel detection, showing improved accuracy and transferability over traditional methods across diverse markets.

Findings

Achieves 80-90% accuracy in cross-market predictions.

Outperforms traditional ensemble machine learning approaches.

Maintains strong performance with 84% accuracy on 12 markets.

Abstract

We propose a novel application of graph attention networks (GATs), a type of graph neural network enhanced with attention mechanisms, to develop a deep learning algorithm for detecting collusive behavior, leveraging predictive features suggested in prior research. We test our approach on a large dataset covering 13 markets across seven countries. Our results show that predictive models based on GATs, trained on a subset of the markets, can be effectively transferred to other markets, achieving accuracy rates between 80% and 90%, depending on the hyperparameter settings. The best-performing configuration, applied to eight markets from Switzerland and the Japanese region of Okinawa, yields an average accuracy of 91% for cross-market prediction. When extended to 12 markets, the method maintains a strong performance with an average accuracy of 84%, surpassing traditional ensemble approaches in machine learning. These results suggest that GAT-based detection methods offer a promising tool for competition authorities to screen markets for potential cartel activity.