A Robust Multi-Item Auction Design with Statistical Learning

TL;DR

This paper introduces a statistical learning approach for multi-item auctions that uses credible intervals to improve efficiency, fairness, and incentive compatibility, demonstrated through simulations with the Vickrey-Clarke-Groves mechanism.

Contribution

It presents new strategies leveraging credible intervals for auction implementation, reducing costs while maintaining desirable economic properties.

Findings

Strategies outperform alternatives in revenue and cost metrics

High probability guarantees for fairness and incentive compatibility

Effective in simulation with Vickrey-Clarke-Groves mechanism

Abstract

We propose a novel statistical learning method for multi-item auctions that incorporates credible intervals. Our approach employs nonparametric density estimation to estimate credible intervals for bidder types based on historical data. We introduce two new strategies that leverage these credible intervals to reduce the time cost of implementing auctions. The first strategy screens potential winners' value regions within the credible intervals, while the second strategy simplifies the type distribution when the length of the interval is below a threshold value. These strategies are easy to implement and ensure fairness, dominant-strategy incentive compatibility, and dominant-strategy individual rationality with a high probability, while simultaneously reducing implementation costs. We demonstrate the effectiveness of our strategies using the Vickrey-Clarke-Groves mechanism and evaluate their performance through simulation experiments. Our results show that the proposed strategies consistently outperform alternative methods, achieving both revenue maximization and cost reduction objectives.