Improved Truthful Mechanisms for Combinatorial Auctions with Submodular Bidders
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Abstract (arXiv)
A longstanding open problem in Algorithmic Mechanism Design is to design computationally-efficient truthful mechanisms for (approximately) maximizing welfare in combinatorial auctions with submodular bidders. The first such mechanism was obtained by Dobzinski, Nisan, and Schapira [STOC'06] who gave an O(log2m)-approximation where m is the number of items. This problem has been studied extensively since, culminating in an O(√logm)-approximation mechanism by Dobzinski [STOC'16]. We present a computationally-efficient truthful mechanism with approximation ratio that improves upon the state-of-the-art by an exponential factor. In particular, our mechanism achieves an O((loglogm)3)-approximation in expectation, uses only O(n) demand queries, and has universal truthfulness guarantee. This settles an open question of Dobzinski on whether Theta(√logm) is the best approximation ratio in this setting in negative.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Nov 2019.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1.
4 papers.
- 2022Random Rank: The One and Only Strategyproof and Proportionally Fair Randomized Facility Location Mechanismcited by 3
- 2024Online Combinatorial Allocations and Auctions with Few Samplescited by 3
- 2025Single-Sample and Robust Online Resource Allocationcited by 3
- 2024Deep Reinforcement Learning for Sequential Combinatorial Auctionscited by 0
- 2025Single-Sample and Robust Online Resource Allocation5 May 2025
- 2024Online Combinatorial Allocations and Auctions with Few Samples17 Sep 2024
- 2024Deep Reinforcement Learning for Sequential Combinatorial Auctions10 Jul 2024
- 2022Random Rank: The One and Only Strategyproof and Proportionally Fair Randomized Facility Location Mechanism30 May 2022