Buying Data of Unknown Quality: Fisher Information Procurement Auctions

2026-04-09

AI Summary

This study addresses the challenge faced by data buyers in markets where they must procure samples from multiple providers to estimate statistical parameters, yet confront unknown data quality and heterogeneous acquisition costs. The authors propose a novel framework integrating Fisher information with mechanism design: when data quality is known, procurement follows a second-highest information-cost scoring rule; when quality is privately held, the mechanism employs ex post statistical tests to incentivize truthful cost reporting and approximately truthful quality disclosure. Under mild conditions, the mechanism achieves an asymptotically truthful equilibrium—costs are reported exactly, and quality misreporting vanishes as sample size grows—thereby enhancing estimation accuracy and procurement efficiency. The analysis further reveals the critical role of verification tests and the precision–cost trade-off in shaping participation incentives and mitigating strategic misreporting.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

We study statistical parameter estimation in the setting of data markets. A buyer seeks to estimate a parameter based on samples that can be purchased from competing providers that differ in their data quality and provision costs. When quality is known ex ante, we define a cost-per-information score that summarizes each provider's provision cost per unit of information about the buyer's estimation objective. We describe second-score procurement mechanism that ranks providers by this score, and endogenously chooses both a provider and a sample size while making truthful cost reports optimal. We then turn to the more realistic setting where data quality is private, and can only be indirectly observed via the delivered data. In this setting, we propose a simple mechanism that augments the second-score rule with a lenient ex post statistical test of the reported quality. We prove that under mild conditions, there exists an equilibrium in which sellers report costs truthfully and report quality up to deviations that vanish as the procured sample size grows. Our analysis highlights how the choice of verification test and the buyer's accuracy-cost tradeoff jointly shape participation and misreporting incentives in data markets.

Problem

• data markets
• Fisher information
• private quality
• procurement auctions
• statistical estimation

Innovation

• Fisher information
• procurement auction
• data markets
• incentive compatibility
• statistical verification