Learning to Clear the Market

Weiran Shen; Sébastien Lahaie; Renato Paes Leme

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arXiv · 1906.01184

Learning to Clear the Market

Weiran Shen·Sébastien Lahaie·Renato Paes Leme

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Abstract

The problem of market clearing is to set a price for an item such that quantity demanded equals quantity supplied. In this work, we cast the problem of predicting clearing prices into a learning framework and use the resulting models to perform revenue optimization in auctions and markets with contextual information. The economic intuition behind market clearing allows us to obtain fine-grained control over the aggressiveness of the resulting pricing policy, grounded in theory. To evaluate our approach, we fit a model of clearing prices over a massive dataset of bids in display ad auctions from a major ad exchange. The learned prices outperform other modeling techniques in the literature in terms of revenue and efficiency trade-offs. Because of the convex nature of the clearing loss function, the convergence rate of our method is as fast as linear regression.

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Weiran Shen, Sébastien Lahaie, Renato Paes Leme. 2019-06-04. Learning to Clear the Market. https://arxiv.org/abs/1906.01184

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