Collaborative Research: The Economics of Algorithms, Computing Capacity, and Data

About This Grant

Algorithms, cloud computing, and data define the infrastructure of the modern digital economy. The prices users pay for computation, software services, and information must reflect the true value to society of these goods. This is necessary in a market economy both because such prices help foster innovation and because they lead to efficient use of resources. This research analyzes three linked questions: (i) how to price the tokens that govern access to large language models; (ii) how to design cloud-computing contracts that reward sustained but flexible demand; and (iii) how platforms can share and aggregate data while respecting users’ private information. By developing rigorous economic models and translating the results into actionable pricing rules, the project advances the efficient allocation of digital resources, informs regulatory and antitrust debates, and supports workforce development through graduate training and open educational materials. The investigators build and solve mechanism-design models that capture the multidimensional nature of modern digital services. The first component studies a monopolistic provider of a specific kind of AI service (large language models) that sells finite-input, finite-output, and function tokens. This project uses a Cobb–Douglas production framework to derives cost-based nonlinear pricing plans. The team also characterizes welfare outcomes, and identifies empirically testable pricing ratios. The second component models sequential cloud-compute contracts in which users commit to future demand but retain real-time flexibility. This component shows that two-part and budget contracts implement the revenue-maximizing allocation subject to incentive and participation constraints. The third component analyzes data markets in which platforms, advertisers, and sellers trade information about consumer types. The component it compares distribution-platform and advertising-platform regimes, establishes profit and welfare bounds, and derives algorithms for privacy-preserving data sharing. Extensions examine competition among multiple providers, dynamic ticket pricing for compute resources, and heterogeneous buyer populations. Results are derived analytically and, where closed forms are infeasible, with numerical examples based. Outputs include scholarly articles, policy briefs, and open-source code for tariff computation and welfare analysis, enabling researchers and practitioners to apply the findings across sectors that rely on artificial intelligence, scalable computing, and data-driven decision making. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.