Robust Clearing Price Mechanisms for Reserve Price Optimization

Zhe Feng; Sébastien Lahaie

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arXiv · 2107.04638

Robust Clearing Price Mechanisms for Reserve Price Optimization

Zhe Feng·Sébastien Lahaie

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Abstract

Setting an effective reserve price for strategic bidders in repeated auctions is a central question in online advertising. In this paper, we investigate how to set an anonymous reserve price in repeated auctions based on historical bids in a way that balances revenue and incentives to misreport. We propose two simple and computationally efficient methods to set reserve prices based on the notion of a clearing price and make them robust to bidder misreports. The first approach adds random noise to the reserve price, drawing on techniques from differential privacy. The second method applies a smoothing technique by adding noise to the training bids used to compute the reserve price. We provide theoretical guarantees on the trade-offs between the revenue performance and bid-shading incentives of these two mechanisms. Finally, we empirically evaluate our mechanisms on synthetic data to validate our theoretical findings.

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Zhe Feng, Sébastien Lahaie. 2021-07-09. Robust Clearing Price Mechanisms for Reserve Price Optimization. https://arxiv.org/abs/2107.04638

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