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Detecting collusion in procurement auctions
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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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Cited by 1 Pith paper
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Evaluating LLM Agent Collusion in Double Auctions
LLM sellers in a simulated double auction collude more when they can communicate, and urgency from an authority figure sustains collusion even when an overseer monitors them.
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