Bulow-Klemperer-Style Results for Welfare Maximization in Two-Sided Markets
Not on the map. Why?
Abstract (arXiv)
We consider the problem of welfare maximization in two-sided markets using simple mechanisms that are prior-independent. The Myerson-Satterthwaite impossibility theorem shows that even for bilateral trade, there is no feasible (IR, truthful, budget balanced) mechanism that has welfare as high as the optimal-yet-infeasible VCG mechanism, which attains maximal welfare but runs a deficit. On the other hand, the optimal feasible mechanism needs to be carefully tailored to the Bayesian prior, and is extremely complex, eluding a precise description. We present Bulow-Klemperer-style results to circumvent these hurdles in double-auction markets. We suggest using the Buyer Trade Reduction (BTR) mechanism, a variant of McAfee's mechanism, which is feasible and simple (in particular, deterministic, truthful, prior-independent, anonymous). First, in the setting where buyers' and sellers' values are sampled i.i.d. from the same distribution, we show that for any such market of any size, BTR with one additional buyer whose value is sampled from the same distribution has expected welfare at least as high as the optimal in the original market. We then move to a more general setting where buyers' values are sampled from one distribution and sellers' from another, focusing on the case where the buyers' distribution first-order stochastically dominates the sellers'. We present bounds on the number of buyers that, when added, guarantees that BTR in the augmented market have welfare at least as high as the optimal in the original market. Our lower bounds extend to a large class of mechanisms, and all of our results extend to adding sellers instead of buyers. In addition, we present positive results about the usefulness of pricing at a sample for welfare maximization in two-sided markets under the above two settings, which to the best of our knowledge are the first sampling results in this context.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Mar 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 3; 2025: 2; 2026: 1.
7 papers.
- 2023An Online Learning Theory of Brokeragecited by 8
- 2025Nearly Tight Regret Bounds for Profit Maximization in Bilateral Tradecited by 7
- 2024Learning to Maximize Gains From Trade in Small Marketscited by 5
- 2024A Parametric Contextual Online Learning Theory of Brokeragecited by 4
- 2025A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokeragecited by 2
- 2024Trading Volume Maximization with Online Learningcited by 0
- 2026Cogentic: Multi-Agent Orchestration for Automated Proof Discoverycited by 0
- 2026Cogentic: Multi-Agent Orchestration for Automated Proof Discovery30 Sep 2026
- 2025Nearly Tight Regret Bounds for Profit Maximization in Bilateral Trade26 Sep 2025
- 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