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Online Auction Design Using Distribution-Free Uncertainty Quantification with Applications to E-Commerce.

Jiale Han1, Xiaowu Dai1,2

  • 1Department of Statistics and Data Science, University of California, Los Angeles, CA.

Journal of the American Statistical Association
|January 19, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Conformal Online Auction Design (COAD), a new method for maximizing revenue in online auctions. COAD quantifies bidder value uncertainty without needing known distributions, improving upon traditional approaches.

Keywords:
Conformal predictionOnline auctionRevenue maximizationUncertainty quantification

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Area of Science:

  • E-commerce and computational economics
  • Machine learning and artificial intelligence
  • Mechanism design and game theory

Background:

  • Online auctions are vital to e-commerce, but revenue maximization faces challenges due to unknown bidder values and uncertain participant numbers.
  • Existing mechanisms often rely on unrealistic assumptions of known value distributions and fixed participant/item sets.
  • Real-world online auctions necessitate flexible, robust mechanisms that handle uncertainty and unknown distributions effectively.

Purpose of the Study:

  • To introduce Conformal Online Auction Design (COAD), a novel mechanism for maximizing revenue in online auctions.
  • To address the limitations of traditional auction designs by incorporating distribution-free uncertainty quantification.
  • To develop an incentive-compatible mechanism that leverages bidder and item features for improved revenue generation.

Main Methods:

  • Developed Conformal Online Auction Design (COAD), a novel revenue-maximizing mechanism.
  • Utilized distribution-free uncertainty quantification techniques to estimate bidder values.
  • Integrated machine learning models (random forests, kernel methods, deep neural networks) with bidder and item features.
  • Implemented bidder-specific reserve prices based on lower confidence bounds of valuations.

Main Results:

  • COAD effectively quantifies uncertainty in bidder values without assuming known distributions.
  • The mechanism demonstrated practical effectiveness on real-world eBay auction data.
  • Theoretical results and simulations validated the approach's revenue-maximizing properties and incentive compatibility.
  • Bidder-specific reserve prices improved upon single reserve price strategies.

Conclusions:

  • Conformal Online Auction Design (COAD) offers a robust and effective solution for revenue maximization in online auctions.
  • The approach successfully integrates machine learning and uncertainty quantification for practical e-commerce applications.
  • COAD provides a significant advancement over traditional auction design methods by handling unknown distributions and dynamic environments.