Algorithmic Collusion in Construction Bidding: Risks, Dynamics, and Regulatory Implications

The rise of algorithmic decision-making across various industries has introduced both opportunities and challenges, particularly in markets in which competitive pricing is crucial. This study investigated the potential for algorithm-induced collusion in the construction bidding market, a sector that increasingly is adopting artificial intelligence (AI) for bid pricing. Using a reinforcement learning–based simulation framework, we investigated how algorithms interact under various market conditions and bid evaluation methods. Our findings indicate that algorithmic bidders gradually can converge to collusive strategies, consistently leading to supracompetitive prices across different market scenarios. Remarkably, they exhibited collusive coordination even when matched with a new opponent. We also found that different bid evaluation methods influence the speed and stability of this collusive behavior. These results highlight the need for regulatory attention to algorithmic collusion risks in construction bidding markets, particularly in scenarios with limited competition and price-focused evaluations.