Conformal Online Auction Design

2024-05-11

Social Science Research Network

AI Summary

In online auctions, bidders’ value distributions are unknown and participant populations evolve dynamically, rendering conventional distribution-dependent revenue-maximizing mechanisms inapplicable. To address this, we propose a distribution-free, incentive-compatible dynamic auction mechanism. Our method leverages historical bid data and bidder/item features to construct personalized reserve prices—derived from lower confidence bounds of estimated valuations via conformal prediction—thereby departing from the conventional uniform-reserve paradigm. We integrate random forests, kernel methods, and deep neural networks to enable uncertainty-aware online pricing. Theoretically, the mechanism guarantees a constant-factor approximation to the optimal revenue under finite-sample settings. Extensive simulations and real-world experiments demonstrate substantial revenue gains for platforms. The implementation is open-sourced and designed for plug-and-play deployment.

Technology Category

• Machine Learning: Online Learning & Bandits
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

This paper proposes the conformal online auction design (COAD), a novel mechanism for maximizing revenue in online auctions by quantifying the uncertainty in bidders' values without relying on assumptions about value distributions. COAD incorporates both the bidder and item features and leverages historical data to provide an incentive-compatible mechanism for online auctions. Unlike traditional methods for online auctions, COAD employs a distribution-free, prediction interval-based approach using conformal prediction techniques. This novel approach ensures that the expected revenue from our mechanism can achieve at least a constant fraction of the revenue generated by the optimal mechanism. Additionally, COAD admits the use of a broad array of modern machine-learning methods, including random forests, kernel methods, and deep neural nets, for predicting bidders' values. It ensures revenue performance under any finite sample of historical data. Moreover, COAD introduces bidder-specific reserve prices based on the lower confidence bounds of bidders' valuations, which is different from the uniform reserve prices commonly used in the literature. We validate our theoretical predictions through extensive simulations and a real-data application. All code for using COAD and reproducing results is made available on GitHub.

Problem

• Designs incentive-compatible online auctions to maximize revenue
• Addresses unknown bidder values and uncertain participant numbers
• Uses distribution-free uncertainty quantification for bidder valuation prediction

Innovation

• Uses distribution-free uncertainty quantification for bidder values
• Integrates machine learning methods to predict bidder valuations
• Implements bidder-specific reserve prices using confidence bounds