2025/09/02 by Natalie Zhang, Zhang, Niuniu
Business, Management and Accounting · Decision Sciences · Economics, Econometrics and Finance · #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Digital Platforms and Economics #FOS: Computer and information sciences #FOS: Economics and business #General Economics (econ.GN) #Merger and Competition Analysis #Multiagent Systems (cs.MA)
paper · pdf · doi:10.48550/arxiv.2509.02800
openalex publication_date 2025/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The rise of autonomous pricing systems has sparked growing concern over algorithmic collusion in markets from retail to housing. This paper examines controlled information quality as an ex ante policy lever: by reducing the fidelity of data that pricing algorithms draw on, regulators can frustrate collusion before supracompetitive prices emerge. We show, first, that information quality is the central driver of competitive outcomes, shaping prices, profits, and consumer welfare. Second, we demonstrate that collusion can be slowed or destabilized by injecting carefully calibrated noise into pooled market data, yielding a feasibility region where intervention disrupts cartels without undermining legitimate pricing. Together, these results highlight information control as a lightweight yet practical lever to blunt digital collusion at its source.