Competitive Auctions with Imperfect Predictions

TL;DR

This paper develops learning-augmented auction mechanisms that leverage imperfect predictions to achieve optimal revenue in various auction settings while maintaining worst-case guarantees, bridging the gap between traditional and prediction-based approaches.

Contribution

It introduces the first learning-augmented auction mechanisms with perfect consistency and robustness across multiple auction environments, incorporating error-tolerance for prediction inaccuracies.

Findings

Achieves 1-consistency against the strongest benchmark.

Maintains O(1) robustness in traditional competitive settings.

Provides an error-tolerant mechanism for moderate prediction errors.

Abstract

The competitive auction was first proposed by Goldberg, Hartline, and Wright. In their paper, they introduce the competitive analysis framework of online algorithm designing into the traditional revenue-maximizing auction design problem. While the competitive analysis framework only cares about the worst-case bound, a growing body of work in the online algorithm community studies the learning-augmented framework. In this framework, designers are allowed to leverage imperfect machine-learned predictions of unknown information and pursue better theoretical guarantees when the prediction is accurate(consistency). Meanwhile, designers also need to maintain a nearly-optimal worst-case ratio(robustness). In this work, we revisit the competitive auctions in the learning-augmented setting. We leverage the imperfect predictions of the private value of the bidders and design the learning-augmented mechanisms for several competitive auctions with different constraints, including digital good auctions, limited-supply auctions, and general downward-closed permutation environments. For all these auction environments, our mechanisms enjoy $1$-consistency against the strongest benchmark $OPT$, which is impossible to achieve $O(1)$-competitive without predictions. At the same time, our mechanisms also maintain the $O(1)$-robustness against all benchmarks considered in the traditional competitive analysis. Considering the possible inaccuracy of the predictions, we provide a reduction that transforms our learning-augmented mechanisms into an error-tolerant version, which enables the learning-augmented mechanism to ensure satisfactory revenue in scenarios where the prediction error is moderate.