Budget Pacing in Repeated Auctions: Regret and Efficiency without Convergence

TL;DR

This paper analyzes the efficiency and regret guarantees of budget pacing algorithms in repeated auctions, demonstrating that they achieve at least half the optimal welfare without requiring convergence, applicable across various auction types.

Contribution

It establishes welfare guarantees and regret bounds for gradient-based pacing algorithms in repeated auctions without needing convergence, applicable to broad auction classes.

Findings

Liquid welfare is at least half the optimal, even without convergence.

Pacing algorithms achieve dynamic regret bounds for individual agents.

Results are robust across different auction formats and valuation correlations.

Abstract

We study the aggregate welfare and individual regret guarantees of dynamic \emph{pacing algorithms} in the context of repeated auctions with budgets. Such algorithms are commonly used as bidding agents in Internet advertising platforms, adaptively learning to shade bids by a tunable linear multiplier in order to match a specified budget. We show that when agents simultaneously apply a natural form of gradient-based pacing, the liquid welfare obtained over the course of the learning dynamics is at least half the optimal expected liquid welfare obtainable by any allocation rule. Crucially, this result holds \emph{without requiring convergence of the dynamics}, allowing us to circumvent known complexity-theoretic obstacles of finding equilibria. This result is also robust to the correlation structure between agent valuations and holds for any \emph{core auction}, a broad class of auctions that includes first-price, second-price, and generalized second-price auctions as special cases. For individual guarantees, we further show such pacing algorithms enjoy \emph{dynamic regret} bounds for individual utility- and value-maximization, with respect to the sequence of budget-pacing bids, for any auction satisfying a monotone bang-for-buck property. To complement our theoretical findings, we provide semi-synthetic numerical simulations based on auction data from the Bing Advertising platform.