Approximately Efficient Two-Sided Combinatorial Auctions
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Abstract (arXiv)
Mechanism design for one-sided markets has been investigated for several decades in economics and in computer science. More recently, there has been an increased attention on mechanisms for two-sided markets, in which buyers and sellers act strategically. For two-sided markets, an impossibility result of Myerson and Satterthwaite states that no mechanism can simultaneously satisfy individual rationality (IR), incentive compatibility (IC), strong budget-balance (SBB), and be efficient. On the other hand, important applications to web advertisement, stock exchange, and frequency spectrum allocation, require us to consider two-sided combinatorial auctions in which buyers have preferences on subsets of items, and sellers may offer multiple heterogeneous items. No efficient mechanism was known so far for such two-sided combinatorial markets. This work provides the first IR, IC and SBB mechanisms that provides an O(1)-approximation to the optimal social welfare for two-sided markets. An initial construction yields such a mechanism, but exposes a conceptual problem in the traditional SBB notion. This leads us to define the stronger notion of direct trade strong budget balance (DSBB). We then proceed to design mechanisms that are IR, IC, DSBB, and again provide an O(1)-approximation to the optimal social welfare. Our mechanisms work for any number of buyers with XOS valuations - a class in between submodular and subadditive functions - and any number of sellers. We provide a mechanism that is dominant strategy incentive compatible (DSIC) if the sellers each have one item for sale, and one that is bayesian incentive compatible (BIC) if sellers hold multiple items and have additive valuations over them. Finally, we present a DSIC mechanism for the case that the valuation functions of all buyers and sellers are additive.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Nov 2016.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 1.
6 papers.
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- 2026A New Lower Bound for the Random Offerer Mechanism in Bilateral Trade using AI-Guided Evolutionary Search9 Mar 2026
- 2025A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokerage3 Mar 2025
- 2024A Parametric Contextual Online Learning Theory of Brokerage22 May 2024
- 2024Trading Volume Maximization with Online Learning21 May 2024
- 2024Learning to Maximize Gains From Trade in Small Markets21 Jan 2024
- 2023An Online Learning Theory of Brokerage18 Oct 2023