A Bayesian Clearing Mechanism for Combinatorial Auctions


  • Gianluca Brero University of Zurich
  • Sébastien Lahaie Google Research




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.




How to Cite

Brero, G., & Lahaie, S. (2018). A Bayesian Clearing Mechanism for Combinatorial Auctions. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11445



AAAI Technical Track: Game Theory and Economic Paradigms