Problem
Research questions and friction points this paper is trying to address.
auction design
price prediction
revenue maximization
mechanism design
learning theory
Auctions with Price Predictions
🤖 AI Summary
This study addresses the problem of revenue maximization in single-item auctions with unlimited supply by relying solely on a single price prediction. Departing from the conventional assumption that each buyer’s valuation must be predicted, this work integrates mechanism design, game theory, and learning theory to develop an auction mechanism based on a single revenue-maximizing price prediction. Specifically, it proposes a novel framework combining randomized prediction release with a prior-free fallback auction. The main contribution lies in establishing a Pareto frontier that balances consistency and robustness. We prove the Pareto optimality of the proposed mechanism in revenue maximization alongside its resilience to prediction errors, thereby achieving end-to-end robust price prediction applications characterized by graceful degradation.
📝 Abstract
We design auctions for the sale of a single item with unlimited supply given a single prediction of the revenue-maximizing uniform price. This departs from prior work on auctions with predictions which typically assumes predictions of every bidder's value. Our main result is a characterization of the Pareto frontier for consistency and robustness attainable by any universally truthful auction. We show that a mechanism that randomizes between posting the predicted price and conducting an optimal prior-free fallback auction is Pareto-optimal. We then extend our results to achieve graceful degradation of revenue as a function of the prediction accuracy. Finally, we study how such a prediction can be obtained from historical market data through the lens of learning theory. Together, our results give an end-to-end account of how a price prediction can be learned and used robustly to maximize auction revenue.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Auctions with predictions
Pareto frontier
Universal truthfulness
Graceful degradation
Learning theory
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