mlsanityData to 13 Oct 2026

An α-regret analysis of Adversarial Bilateral Trade

arXiv 2210.06846 ↗First posted 13 Oct 2022cs.GT cs.DS cs.LGcited by 16

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Abstract (arXiv)

We study sequential bilateral trade where sellers and buyers valuations are completely arbitrary (i.e., determined by an adversary). Sellers and buyers are strategic agents with private valuations for the good and the goal is to design a mechanism that maximizes efficiency (or gain from trade) while being incentive compatible, individually rational and budget balanced. In this paper we consider gain from trade which is harder to approximate than social welfare. We consider a variety of feedback scenarios and distinguish the cases where the mechanism posts one price and when it can post different prices for buyer and seller. We show several surprising results about the separation between the different scenarios. In particular we show that (a) it is impossible to achieve sublinear α-regret for any α<2, (b) but with full feedback sublinear 2-regret is achievable (c) with a single price and partial feedback one cannot get sublinear α regret for any constant α (d) nevertheless, posting two prices even with one-bit feedback achieves sublinear 2-regret, and (e) there is a provable separation in the 2-regret bounds between full and partial feedback.

Key facts. Cited by 16 papers in the map (data through 13 Oct 2026). Builds on 22 of its 55 references that are in the map. First posted on arXiv 13 Oct 2022.

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22 of 55 references.

References · 22

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  8. 2021Bilateral Trade: A Regret Minimization Perspectivecited by 29
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  10. 2021A Regret Analysis of Bilateral Tradecited by 19
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10 more
  1. 2021Beyond Bandit Feedback in Online Multiclass Classificationcited by 14
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  5. 2021Approximately Efficient Bilateral Tradecited by 9
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  7. 2017Fixed Price Approximability of the Optimal Gain From Tradecited by 8
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  9. 2020Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Boundscited by 4
  10. 2020The Complexity of Interactively Learning a Stable Matching by Trial and Errorcited by 3

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Citing papers per year: 2023: 4; 2024: 3; 2025: 2; 2026: 7.

16 papers.

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