A Bayesian Clearing Mechanism for Combinatorial Auctions

TL;DR

This paper introduces a Bayesian auction mechanism for combinatorial auctions that efficiently incorporates prior information, reducing the number of rounds to convergence compared to traditional methods.

Contribution

It develops a Bayesian framework with a generative model for valuations and prices, using density filtering and EM to improve auction efficiency.

Findings

Achieves fewer rounds to convergence than baseline auctions.

Effective in diverse valuation domains.

Competitive performance under various conditions.

Abstract

We cast the problem of combinatorial auction design in a Bayesian framework in order to incorporate prior information into the auction process and minimize the number of rounds to convergence. We first develop a generative model of agent valuations and market prices such that clearing prices become maximum a posteriori estimates given observed agent valuations. This generative model then forms the basis of an auction process which alternates between refining estimates of agent valuations and computing candidate clearing prices. We provide an implementation of the auction using assumed density filtering to estimate valuations and expectation maximization to compute prices. An empirical evaluation over a range of valuation domains demonstrates that our Bayesian auction mechanism is highly competitive against the combinatorial clock auction in terms of rounds to convergence, even under the most favorable choices of price increment for this baseline.