Fourier Analysis-based Iterative Combinatorial Auctions
METADATA ONLY
Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Recent advances in Fourier analysis have brought new tools to efficiently represent and learn set functions. In this paper, we bring the power of Fourier analysis to the design of combinatorial auctions (CAs). The key idea is to approximate bidders' value functions using Fourier-sparse set functions, which can be computed using a relatively small number of queries. Since this number is still too large for practical CAs, we propose a new hybrid design: we first use neural networks (NNs) to learn bidders’ values and then apply Fourier analysis to the learned representations. On a technical level, we formulate a Fourier transform-based winner determination problem and derive its mixed integer program formulation. Based on this, we devise an iterative CA that asks Fourier-based queries. We experimentally show that our hybrid ICA achieves higher efficiency than prior auction designs, leads to a fairer distribution of social welfare, and significantly reduces runtime. With this paper, we are the first to leverage Fourier analysis in CA design and lay the foundation for future work in this area. Our code is available on GitHub: https://github.com/marketdesignresearch/FA-based-ICAs.
Permanent link
Publication status
published
External links
Editor
Book title
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Journal / series
Volume
Pages / Article No.
549 - 556
Publisher
International Joint Conferences on Artificial Intelligence
Event
31st International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03893 - Püschel, Markus / Püschel, Markus