mlsanityData to 7 Oct 2026

A Regret Analysis of Bilateral Trade

arXiv 2102.08754 ↗First posted 16 Feb 2021cs.LG econ.TH stat.MLcited by 19

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Abstract (arXiv)

Bilateral trade, a fundamental topic in economics, models the problem of intermediating between two strategic agents, a seller and a buyer, willing to trade a good for which they hold private valuations. Despite the simplicity of this problem, a classical result by Myerson and Satterthwaite (1983) affirms the impossibility of designing a mechanism which is simultaneously efficient, incentive compatible, individually rational, and budget balanced. This impossibility result fostered an intense investigation of meaningful trade-offs between these desired properties. Much work has focused on approximately efficient fixed-price mechanisms, i.e., Blumrosen and Dobzinski (2014; 2016), Colini-Baldeschi et al. (2016), which have been shown to fully characterize strong budget balanced and ex-post individually rational direct revelation mechanisms. All these results, however, either assume some knowledge on the priors of the seller/buyer valuations, or a black box access to some samples of the distributions, as in D"utting et al. (2021). In this paper, we cast for the first time the bilateral trade problem in a regret minimization framework over rounds of seller/buyer interactions, with no prior knowledge on the private seller/buyer valuations. Our main contribution is a complete characterization of the regret regimes for fixed-price mechanisms with different models of feedback and private valuations, using as benchmark the best fixed price in hindsight. More precisely, we prove the following bounds on the regret: bullet widetildeTheta(√T) for full-feedback (i.e., direct revelation mechanisms); bullet widetildeTheta(T2/3) for realistic feedback (i.e., posted-price mechanisms) and independent seller/buyer valuations with bounded densities; bullet Theta(T) for realistic feedback and seller/buyer valuations with bounded densities; bullet Theta(T) for realistic feedback and independent seller/buyer valuations; bullet Theta(T) for the adversarial setting.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 17 of its 49 references that are in the map. First posted on arXiv 16 Feb 2021.

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17 of 49 references.

References · 17

  1. 2019Introduction to Multi-Armed Banditscited by 475
  2. 2013Bandits with Knapsackscited by 163
  3. 2014Bandits with concave rewards and convex knapsackscited by 83
  4. 2015The Sample Complexity of Revenue Maximizationcited by 48
  5. 2013Learning Prices for Repeated Auctions with Strategic Buyerscited by 37
  6. 2016Sample Complexity of Automated Mechanism Designcited by 24
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  8. 2018Dynamic Pricing with Finitely Many Unknown Valuationscited by 23
  9. 2020Efficient Two-Sided Markets with Limited Informationcited by 14
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  11. 2015Learning and Efficiency in Games with Dynamic Populationcited by 9
  12. 2016(Almost) Efficient Mechanisms for Bilateral Tradingcited by 8
5 more
  1. 2017Multi-scale Online Learning and its Applications to Online Auctionscited by 8
  2. 2017Fixed Price Approximability of the Optimal Gain From Tradecited by 8
  3. 2014Reallocation Mechanismscited by 6
  4. 2014Perfect Bayesian Equilibria in Repeated Salescited by 6
  5. 2015The Pseudo-Dimension of Near-Optimal Auctionscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 6; 2024: 4; 2025: 2; 2026: 3.

19 papers.

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