Author
Mr Sun Qilong
Organisation/Institution
The Chinese University of Hong Kong (CUHK)
Country
HONG KONG (SAR OF CHINA)
Panel
Competition Law
Title
Navigating Algorithmic Tacit Collusion: A Panoramic Data Compliance Approach
Abstract
The proliferation of algorithms in the digital economy presents a formidable challenge to antitrust law, particularly in addressing algorithmic tacit collusion. Unlike explicit collusion, this form involves self-learning algorithms that autonomously coordinate market behavior, leading to stable yet covert anticompetitive outcomes that escape traditional antitrust frameworks reliant on proving explicit agreements. This regulatory gap is especially pronounced in Asia's rapidly digitalizing markets, where China's unique market structure—characterized by super-app ecosystems and concentrated platform power—creates fertile ground for such collusion to emerge. Data collection, processing, and utilization function as the operational core of algorithmic collusion, effectively substituting for human communication in facilitating coordination. A robust regulatory response must therefore integrate data compliance throughout this lifecycle, particularly through China's emerging regulatory tools such as the algorithmic transparency requirements in its Platform Economy Antitrust Guidelines. First, the types and sources of data used in algorithmic training and pricing must be systematically categorized. Second, a risk-tiered evaluation framework should be established to determine whether data usage patterns constitute reasonable competition or illicit tacit collusion. Finally, regulatory mechanisms must be designed to balance meaningful data scrutiny with the protection of legitimate trade secrets, enabling collaborative compliance between firms and regulators. By systematically mapping, analyzing, and evaluating data throughout its lifecycle, this study introduces data compliance as an innovative tool tailored to Asian regulatory challenges. It thereby constructs a panoramic regulatory pathway to effectively detect, assess, and govern algorithmic tacit collusion, offering regulators a proactive framework compatible with its evolving digital governance paradigm. Key words: Algorithmic Tacit Collusion, Data Compliance, Digital Platform, Antitrust Regulation
Biography
Sun Qilong is a PhD candidate in competition law at The Chinese University of Hong Kong (CUHK), where his research focuses on the intersection of antitrust law with algorithms, artificial intelligence, and digital platforms. Prior to joining CUHK, he practiced capital markets law at a leading UK law firm, specializing in Hong Kong and U.S. IPOs. He earned his Bachelor of Law and Master of Comparative Law degrees from China University of Political Science and Law (CUPL), where he distinguished himself academically as a recipient of the National Scholarship and the Peng Zhen Scholarship. He was also honored with the university’s top academic accolade, the “Academic Top Ten Star” award, and led a national-level innovative research project for undergraduates. Moreover, he has further enriched his global academic exposure through summer programs at the University of Oxford and the University of Washington. Mr. Sun has been awarded several competitive scholarships, including the CUHK Postgraduate Scholarship (approx. HK$700,000) and the Vice-Chancellor’s Scholarship (HK$80,000).
