Learning to Clear the Market

TL;DR

This paper introduces a learning-based approach to predict market clearing prices for auctions, enabling optimized revenue strategies with theoretical grounding and fast convergence, demonstrated on large ad auction datasets.

Contribution

It presents a novel framework that models market clearing as a learning problem, integrating economic theory for improved pricing and revenue optimization.

Findings

Learned prices outperform existing models in revenue and efficiency

The method converges as fast as linear regression due to convex loss

Applied successfully to large-scale ad auction data

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.