Efficient Preference Elicitation in Iterative Combinatorial Auctions with Many Participants (2403.19075v2)
Abstract: We study the problem of achieving high efficiency in iterative combinatorial auctions (ICAs). ICAs are a kind of combinatorial auction where the auctioneer interacts with bidders to gather their valuation information using a limited number of queries, aiming for efficient allocation. Preference elicitation, a process that incrementally asks bidders to value bundles while refining the outcome allocation, is a commonly used technique in ICAs. Recently, the integration of ML into ICAs has significantly improved preference elicitation. This approach employs ML models that match the number of bidders, estimating each bidder's valuation functions based on their reported valuations. However, most current studies train a separate model for each bidder, which can be inefficient when there are numerous bidders with similar valuation functions and a limited number of available queries. In this study, we introduce a multi-task learning method to learn valuation functions more efficiently. Specifically, we propose to share model parameters during training to grasp the intrinsic relationships between valuations. We assess the performance of our method using a spectrum auction simulator. The findings demonstrate that our method achieves higher efficiency than existing methods, especially in scenarios with many bidders and items but a limited number of queries.
- A Practical Guide to the Combinatorial Clock Auction. The Economic Journal, 127(605):F334–F350, 10 2017.
- The use of auctions for allocating airport access rights. Transportation Research Part A: Policy and Practice, 114:186–202, 2018.
- Preference elicitation and query learning. Journal of Machine Learning Research, 5(6):649–667, 2004.
- Probably approximately efficient combinatorial auctions via machine learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, 2 2017.
- Combinatorial auctions via machine learning-based preference elicitation. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pages 128–136, 7 2018.
- Machine learning-powered iterative combinatorial auctions, 2021. arXiv preprint arXiv:1911.08042, 2021.
- Edward Clarke. Multipart pricing of public goods. Public Choice, 11(1):17–33, 1971.
- Preference elicitation in combinatorial auctions. In Proceedings of the 3rd ACM Conference on Electronic Commerce, EC ’01, page 256–259, 2001.
- Peter Cramton. Spectrum auction design. Review of industrial organization, 42:161–190, 2013.
- Differentiable economics for randomized affine maximizer auctions. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pages 2633–2641, 8 2023.
- Optimal auctions through deep learning. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 1706–1715, 6 2019.
- Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, volume 2, pages 845–850, 7 2015.
- Optimal auctions through deep learning. Commun. ACM, 64(8):109–116, 7 2021.
- Solids: a combinatorial auction for real estate. Interfaces, 44(4):351–363, 2014.
- Theodore Groves. Incentives in teams. Econometrica, 41(4):617–631, 1973.
- Applying learning algorithms to preference elicitation. In Proceedings of the 5th ACM Conference on Electronic Commerce, EC ’04, pages 180–188, 2004.
- Towards a universal test suite for combinatorial auction algorithms. In Proceedings of the 2nd ACM Conference on Electronic Commerce, EC ’00, pages 66–76, 2000.
- The communication requirements of efficient allocations and supporting prices. Journal of Economic Theory, 129(1):192–224, 2006.
- Chapter 10: Preference elicitation in combinatorial auctions. 12 2005.
- William Vickrey. Counterspeculation, auctions, and competitive sealed tenders. The Journal of Finance, 16(1):8–37, 1961.
- Combinatorial auctions: A survey. INFORMS Journal on Computing, 15:284–309, 8 2003.
- Sats: A universal spectrum auction test suite. AAMAS ’17, pages 51–59, 2017.
- Deep learning—powered iterative combinatorial auctions. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 2284–2293, 4 2020.
- Monotone-value neural networks: Exploiting preference monotonicity in combinatorial assignment. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pages 541–548, 7 2022.
- Bayesian optimization-based combinatorial assignment. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 5858–5866, 6 2023.
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