Computing Bayes-Nash Equilibria in Combinatorial Auctions with Verification
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Abstract (arXiv)
We present a new algorithm for computing pure-strategy varepsilon-Bayes-Nash equilibria (varepsilon-BNEs) in combinatorial auctions with continuous value and action spaces. An essential innovation of our algorithm is to separate the algorithm's search phase (for finding the varepsilon-BNE) from the verification phase (for computing the varepsilon). Using this approach, we obtain an algorithm that is both very fast and provides theoretical guarantees on the varepsilon it finds. Our main technical contribution is a verification method which allows us to upper bound the varepsilon across the whole continuous value space without making assumptions about the mechanism. Using our algorithm, we can now compute varepsilon-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 to facilitate many other applications.
Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). First posted on arXiv 5 Dec 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2.
3 papers.
- 2024Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learningcited by 2
- 2021Equilibrium Learning in Combinatorial Auctions: Computing Approximate Bayesian Nash Equilibria via Pseudogradient Dynamicscited by 1
- 2024Approximating Auction Equilibria with Reinforcement Learningcited by 0
- 2024Approximating Auction Equilibria with Reinforcement Learning17 Oct 2024
- 2024Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning29 Feb 2024
- 2021Equilibrium Learning in Combinatorial Auctions: Computing Approximate Bayesian Nash Equilibria via Pseudogradient Dynamics28 Jan 2021