Methods of Detecting Cartels Based on Bids in Public Procurement

Student: Efimov Konstantin

Faculty: St.Petersburg School of Economics and Management

Educational Programme: Applied Economics and Mathematical Methods (Master)

Final Grade: 9

Year of Graduation: 2024

The study aimed at identifying cartel collusion included an analysis of more than 1,000 decisions of the Federal Antimonopoly Service of Russia and data on 89,000 auctions. As a result, a machine learning model was developed that predicts signs of collusion between bidders with 91% accuracy based on their history after dividing 40 auctions into test and training samples in a ratio of 30/70. Decomposition of the model using the Shapley vector made it possible to interpret the decision-making process. The behavior of "honest" companies at auctions was also studied, confirmed by an independent simulation test.

Full text (added May 16, 2024)

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