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Learn Then Decide: A Learning Approach for Designing Data Marketplaces
Yingqi Gao1, Wenlu Xu1, Jin J Zhou2
1Department of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA.
Journal of the American Statistical Association
|July 28, 2026
Summary
We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism for data marketplaces. This novel approach optimizes revenue through adaptive pricing, ensuring fairness and efficiency in digital economies.
Area of Science:
- Economics
- Computer Science
- Data Science
Background:
- Data marketplaces are crucial for the digital economy.
- Efficient pricing mechanisms are needed for revenue optimization and fair pricing.
- Existing mechanisms may not adapt well to varying data values.
Purpose of the Study:
- To introduce a novel pricing mechanism for data marketplaces.
- To optimize revenue and ensure fair, adaptive pricing.
- To theoretically analyze the mechanism's properties and performance.
Main Methods:
- Introduced the Maximum Auction-to-Posted Price (MAPP) mechanism, a two-stage approach.
- Estimated bidder value distribution through auctions.
- Determined optimal posted price based on learned distribution.
- Established theoretical properties: individual rationality and incentive-compatibility.
- Analyzed revenue regret using a statistical valuation density estimation viewpoint.
- Proposed an online MAPP for sequential dataset sales.
- Validated through simulations and FCC AWS-3 spectrum auction data.
Main Results:
- MAPP is individually rational and incentive-compatible, ensuring truthful bidding.
- Revenue regret is controlled by uniform error in valuation density estimation.
- MAPP achieves a regret of O(n^{-1}(log n)^2) with historical bid data.
- Online MAPP achieves no-regret learning for sequential sales.
- Average regret converges at a rate of O(T^{-1/2}(log T)^2).
Conclusions:
- MAPP is an effective mechanism for optimizing revenue in data marketplaces.
- The mechanism balances revenue maximization with fair and adaptive pricing.
- Theoretical analysis provides a strong foundation for its performance guarantees.
- Empirical validation confirms its effectiveness in real-world scenarios.
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