Lucas Gomes, Jannis Kueck, Mara Mattes, Martin Spindler, Alexey Zaytsev
arXiv 9 Oct 2024 · Econometrics
arXiv:2410.07091 · PDF · DOI · OpenAlex · Extracted main text
Collusion is a complex phenomenon in which companies secretly collaborate to engage in fraudulent practices. This paper presents an innovative methodology for detecting and predicting collusion patterns in different national markets using neural networks (NNs) and graph neural networks (GNNs). GNNs are particularly well suited to this task because they can exploit the inherent network structures present in collusion and many other economic problems. Our approach consists of two phases: In Phase I, we develop and train models on individual market datasets from Japan, the United States, two regions in Switzerland, Italy, and Brazil, focusing on predicting collusion in single markets. In Phase II, we extend the models' applicability through zero-shot learning, employing a transfer learning approach that can detect collusion in markets in which training data is unavailable. This phase also incorporates out-of-distribution (OOD) generalization to evaluate the models' performance on unseen datasets from other countries and regions. In our empirical study, we show that GNNs outperform NNs in detecting complex collusive patterns. This research contributes to the ongoing discourse on preventing collusion and optimizing detection methodologies, providing valuable guidance on the use of NNs and GNNs in economic applications to enhance market fairness and economic welfare.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rodrǵuez, M.J.G., Rodrǵuez-Montequń, V., Ballesteros-Pérez, P., Love… (2022) Collusion detection in public procurement auctions with machine learning algorithms | 1.000 | 12 | 4 | 100% |
| 2 | Conley, T.G., Decarolis, F (2016) Detecting bidders groups in collusive auctions | 0.928 | 4 | 3 | 100% |
| 3 | Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y (2020) A comprehensive survey on graph neural networks | 0.874 | 8 | 2 | 100% |
| 4 | Wallimann, H., Imhof, D., Huber, M (2022) A machine learning approach for flagging incomplete bid-rigging cartels | 0.811 | 4 | 2 | 100% |
| 5 | Huber, M., Imhof, D (2023) Flagging cartel participants with deep learning based on convolutional neural networks | 0.737 | 3 | 2 | 100% |
| 6 | Imhof, D., Wallimann, H (2021) Detecting bid-rigging coalitions in different countries and auction formats | 0.644 | 2 | 2 | 100% |
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| 8 | Kipf, T.N., Welling, M (2016) Semi-supervised classification with graph convolutional networks | 0.585 | 3 | 1 | 100% |
| 9 | Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K (2019) Simplifying graph convolutional networks, in: Chaudhuri, K., Salakhutdinov, R. (Eds.), Proceedings of the 36th International Con… | 0.511 | 2 | 1 | 100% |
| 10 | Ibáñez Colomo, P (2020) Anticompetitive Effects in EU Competition Law | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 27 scored citations.