Antitrust Regulation in the Age of Algorithmic Collusion: Risks, Challenges, and Mitigation Strategies

With a Comparative Review of the RealPage Pricing Algorithm Matter and Gibson v. Cendyn Grp.

Abstract

Algorithmic collusion, leveraging the technological characteristics of deep learning and autonomous decision-making, transcends the established boundaries of traditional antitrust regulation concerning the identification of collusive agreements, the determination of liability, and the collection of evidence. Drawing upon the RealPage Pricing Algorithm matter and the Gibson v. Cendyn Grp. case, this study examines the operational logic and potential legal risks associated with algorithmic collusion, comparing the fundamental differences between the two instances in terms of algorithm functional design and market intervention effects. Addressing the regulatory challenges inherent in algorithmic collusion-specifically concerning the establishment of concertation ('meeting of minds'), evidence gathering, and liability allocation-this paper proposes a core strategy centered on multi-dimensional technical governance. This strategy aims to curb anomalous pricing behavior through mechanisms such as algorithmic 'speed bumps' and pricing constraints, combined with enhanced trace supervision via log recording, real-time monitoring, and the application of explainability techniques. Furthermore, it suggests utilizing an autonomy scoring model to establish a tiered evaluation framework for liability allocation. The study posits that, predicated on the monitoring and identification of anomalous market behavior, the utilization of indirect evidence and economic analysis can be instrumental in revealing the non-competitive character of algorithmic collusion, thereby bolstering the basis for liability determination. Consequently, the establishment of precise stratification, the integration of technical assistance, and the development of clear liability allocation mechanisms are identified as critical approaches for antitrust regulation in the context of algorithmic collusion, crucial for effectively mitigating the adverse impacts of technological misuse on the competitive market process.