Monopolistic Data Dumping

2025-11-30

AI Summary

This paper investigates how a data monopolist designs optimal data-access contracts for two heterogeneous user types—nowcasters (requiring immediate predictive signals) and forecasters (relying on long-horizon inference)—to maximize profit. Using mechanism design, we construct a type-identifying contract menu integrating Bayesian learning and dynamic data disclosure. We identify a systemic distortion induced by monopoly incentives: excessive retention of historical data and artificial scarcity of contemporaneous data, resulting in aggregate storage exceeding the socially optimal level. Our key contribution is the first formal characterization of time-dimensional supply distortion under data monopoly, and we prove that the profit-maximizing contract must be separating: nowcasters receive full real-time data access, whereas forecasters face delayed or restricted access. This structural result provides foundational theoretical support for data governance frameworks and antitrust regulation targeting temporal data bottlenecks.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices

Abstract

A profit-maximizing monopolist curates a database for users seeking to learn a parameter. There are two user types: "Nowcasters" wish to learn the parameter's current value, while "forecasters" target its long-run value. Data storage involves a constant marginal cost. The monopolist designs a menu of contracts described by fees and data-access levels. The profit-maximizing menu offers full access to historical data, while current data is fully provided to nowcasters but may be withheld from forecasters. Compared to the social optimum, the monopolist keeps too much historical data, too little current data, and may store too much data overall.

Problem

• Monopolist designs data access contracts for nowcasters and forecasters.
• Profit-maximizing menu withholds current data from forecasters, provides full historical data.
• Monopolist stores excessive historical data, insufficient current data compared to social optimum.

Innovation

• Monopolist designs menu of data-access contracts
• Full historical data access with restricted current data
• Profit-maximizing strategy withholds data from forecasters