My bibliography
Save this item
Machine Learning with Screens for Detecting Bid-Rigging Cartels
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Clara Calini & Alessandra Catenazzo & Elisabetta Iossa, 2025. "Using Multiple Tools to Enhance Competition in Public Procurement," CEIS Research Paper 594, Tor Vergata University, CEIS, revised 25 Feb 2025.
- Bedri Kamil Onur Tas, 2024. "A machine learning approach to detect collusion in public procurement with limited information," Journal of Computational Social Science, Springer, vol. 7(2), pages 1913-1935, October.
- Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022.
"Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels,"
Energy Economics, Elsevier, vol. 105(C).
- Douglas Silveira & Silvinha Vasconcelos & Marcelo Resende & Daniel O. Cajueiro, 2021. "Won't Get Fooled Again: A Supervised Machine Learning Approach for Screening Gasoline Cartels," CESifo Working Paper Series 8835, CESifo.
- Max Berre, 2022. "Which Factors Matter Most? Can Startup Valuation be Micro-Targeted?," Post-Print hal-03829877, HAL.
- Martinez-Carrasco, José & ConceiçaÞo, Otavio & Dezolt, Ana Lúcia, 2023. "More Information, Lower Price? Access Market-based Reference Prices and Gains in Public Procurement Efficiency," IDB Publications (Working Papers) 12754, Inter-American Development Bank.
- Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
- Hannes Wallimann & David Imhof & Martin Huber, 2023.
"A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels,"
Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
- Wallimann, Hannes & Imhof, David & Huber, Martin, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," FSES Working Papers 513, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Hannes Wallimann & David Imhof & Martin Huber, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers 2004.05629, arXiv.org.
- Max Berre, 2022. "Which Factors Matter Most? Can Startup Valuation be Micro-Targeted?," Papers 2210.14518, arXiv.org.
- David Imhof & Emanuel W Viklund & Martin Huber, 2025. "Catching Bid-rigging Cartels with Graph Attention Neural Networks," Papers 2507.12369, arXiv.org, revised Jul 2025.
- Jeanne Mouton & Benoit Rottembourg, 2024. "Auditing the Ranking Strategy of a Marketplace 's Algorithm in the Frame of Competition Law Commitments with Surrogate Models: The Amazon 's Buy Box Case," GREDEG Working Papers 2024-27, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
- World Bank, 2022. "Using Data Analytics in Public Procurement Operational Options and a Guiding Framework," World Bank Publications - Reports 37467, The World Bank Group.
- Hannes Wallimann & Silvio Sticher, 2024. "How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection," Papers 2401.14757, arXiv.org.
- Frédéric Marty, 2022. "From Economic Evidence to Algorithmic Evidence: Artificial Intelligence and Blockchain: An Application to Anti-competitive Agreements," GREDEG Working Papers 2022-32, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
- Chengyan Gu, 2023. "Market segmentation and dynamic price discrimination in the U.S. airline industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(5), pages 338-361, October.
- Severin Lenhard, 2025. "Random Pricing: Bertrand Competition with Uncontested Consumers," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 67(2), pages 191-208, August.
- Bovin, Andreas & Bos, Iwan, 2023. "Market Shares as Collusive Marker: Evidence from the European Truck Industry," Research Memorandum 011, Maastricht University, Graduate School of Business and Economics (GSBE).
- Martin Huber & Yu‐Chin Hsu & Ying‐Ying Lee & Layal Lettry, 2020.
"Direct and indirect effects of continuous treatments based on generalized propensity score weighting,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 814-840, November.
- Hsu, Yu-Chin & Huber, Martin & Lee, Ying-Ying & Pipoz, Layal, 2018. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," FSES Working Papers 495, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
- Tomaso Duso & Joseph E. Harrington Jr. & Carl Kreuzberg & Geza Sapi, 2025. "Public Communication and Collusion: New Screening Tools for Competition Authorities," Discussion Papers of DIW Berlin 2131, DIW Berlin, German Institute for Economic Research.
- Aaltio, Aapo & Buri, Riku & Jokelainen, Antto & Lundberg, Johan, 2025. "Complementary bidding and cartel detection: Evidence from Nordic asphalt markets," International Journal of Industrial Organization, Elsevier, vol. 98(C).
- Wallimann, Hannes & Sticher, Silvio, 2023. "On suspicious tracks: Machine-learning based approaches to detect cartels in railway-infrastructure procurement," Transport Policy, Elsevier, vol. 143(C), pages 121-131.
- Dmitry I. Ivanov & Alexander S. Nesterov, 2019. "Stealed-bid Auctions: Detecting Bid Leakage via Semi-Supervised Learning," Papers 1903.00261, arXiv.org, revised Nov 2020.
- David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
- Martin Huber & David Imhof & Rieko Ishii, 2022.
"Transnational machine learning with screens for flagging bid‐rigging cartels,"
Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1074-1114, July.
- Huber, Martin & Imhof, David, 2020. "Transnational machine learning with screens for flagging bid-rigging cartels," FSES Working Papers 519, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Tomaso Duso & Joseph E., Jr. Harrington & Carl Kreuzberg & Geza Sapi, 2025. "Public Communication and Collusion: New Screening Tools for Competition Authorities," CESifo Working Paper Series 12029, CESifo.
- Silveira, Douglas & de Moraes, Lucas B. & Fiuza, Eduardo P.S. & Cajueiro, Daniel O., 2023. "Who are you? Cartel detection using unlabeled data," International Journal of Industrial Organization, Elsevier, vol. 88(C).
- David P. Brown & Andrew Eckert & Douglas Silveira, 2023. "Screening for Collusion in Wholesale Electricity Markets: A Review of the Literature," Working Papers 2023-07, University of Alberta, Department of Economics.
- Garcia Pires, Armando J. & Skjeret, Frode, 2023. "Screening for partial collusion in retail electricity markets," Energy Economics, Elsevier, vol. 117(C).
- Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).
Printed from https://ideas.repec.org/r/fri/fribow/fribow00494.html