Fast Iterative Combinatorial Auctions via Bayesian Learning

TL;DR

This paper introduces a Bayesian iterative combinatorial auction method that uses Monte Carlo Expectation Maximization to achieve faster convergence and better efficiency in general valuation settings.

Contribution

It generalizes previous Bayesian auction designs to unrestricted bidder valuations and demonstrates improved convergence speed through simulation evaluations.

Findings

Outperforms benchmarks in clearing percentage

Achieves faster convergence in simulations

Effectively incorporates prior knowledge for auction efficiency

Abstract

Iterative combinatorial auctions (CAs) are often used in multi-billion dollar domains like spectrum auctions, and speed of convergence is one of the crucial factors behind the choice of a specific design for practical applications. To achieve fast convergence, current CAs require careful tuning of the price update rule to balance convergence speed and allocative efficiency. Brero and Lahaie (2018) recently introduced a Bayesian iterative auction design for settings with single-minded bidders. The Bayesian approach allowed them to incorporate prior knowledge into the price update algorithm, reducing the number of rounds to convergence with minimal parameter tuning. In this paper, we generalize their work to settings with no restrictions on bidder valuations. We introduce a new Bayesian CA design for this general setting which uses Monte Carlo Expectation Maximization to update prices at each round of the auction. We evaluate our approach via simulations on CATS instances. Our results show that our Bayesian CA outperforms even a highly optimized benchmark in terms of clearing percentage and convergence speed.