Computing Bayes-Nash Equilibria in Combinatorial Auctions with Verification

Abstract

We present a new algorithm for computing pure-strategy ε-Bayes-Nash equilibria (ε-BNEs) in combinatorial auctions. The main innovation of our algorithm is to separate the algorithm’s search phase (for finding the ε-BNE) from the verification phase (for computing the ε). Using this approach, we obtain an algorithm that is both very fast and provides theoretical guarantees on the ε it finds. Our main contribution is a verification method which, surprisingly, allows us to upper bound the ε across the whole continuous value space without making assumptions about the mechanism. Using our algorithm, we can now compute ε-BNEs in multi-minded domains that are significantly more complex than what was previously possible to solve. We release our code under an open-source license to enable researchers to perform algorithmic analyses of auctions, to enable bidders to analyze different strategies, and many other applications.

Cite

Text

Bosshard et al. "Computing Bayes-Nash Equilibria in Combinatorial Auctions with Verification." Journal of Artificial Intelligence Research, 2020. doi:10.1613/JAIR.1.11525

Markdown

[Bosshard et al. "Computing Bayes-Nash Equilibria in Combinatorial Auctions with Verification." Journal of Artificial Intelligence Research, 2020.](https://mlanthology.org/jair/2020/bosshard2020jair-computing/) doi:10.1613/JAIR.1.11525

BibTeX

@article{bosshard2020jair-computing,
  title     = {{Computing Bayes-Nash Equilibria in Combinatorial Auctions with Verification}},
  author    = {Bosshard, Vitor and Bünz, Benedikt and Lubin, Benjamin and Seuken, Sven},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {2020},
  pages     = {531-570},
  doi       = {10.1613/JAIR.1.11525},
  volume    = {69},
  url       = {https://mlanthology.org/jair/2020/bosshard2020jair-computing/}
}