Data Pricing via Competitive Equilibrium

Data powers almost everything we experience on the web today---from the recommendations and ads we see to the AI systems and online marketplaces that shape our digital interactions. The increasing demand for high-quality data has given rise to platforms that facilitate the buying and selling of data. A key practical challenge in such markets is determining how to price data. Competitive equilibrium (CE), a foundational concept in classical market economics, determines prices for rivalrous goods by matching their supply and demand. In this work, we initiate the study of CE in data markets, explicitly incorporating the role of data in improving predictive performance in buyers' utility functions, and the non-rival nature of data by adapting the standard market-clearing condition to allow the simultaneous allocation of data records to multiple buyers. We analyze the existence, structure, and computation of CE in such data markets. We establish that CE always exists, and almost all instances admit a unique and rational equilibrium price vector. In general, however, there could be a non-convex set of prices, which rules out convex-programming approaches for finding a CE. Despite these challenges, we design an FPTAS for computing approximate equilibria using a Walrasian-style price adjustment algorithm. Our framework opens avenues for studying richer buyer utilities under correlated data sellers, and deeper structural and algorithmic aspects of data markets.