Detecting collusion in procurement auctions

2024/11/16 by Efimov, Konstantin D.
#Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2411.10811

Abstract

The study aimed at detecting cartel collusion involved analyzing decisions of the Russian Federal Antimonopoly Service and data on auctions. As a result, a machine learning model was developed that predicts with 91% accuracy the signs of collusion between bidders based on their history after dividing 40 auctions into test and training samples in a 30/70 ratio. Decomposition of the model using the Shepley vector allowed the interpretation of the decision-making process. The behavior of honest companies in auctions was also studied, confirmed by independent simulation validation.

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