An α-regret analysis of adversarial bilateral trade

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.

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Publication Details

Journal
Artificial Intelligence
Published
2024-10-02
DOI
https://doi.org/10.1016/j.artint.2024.104231
Primary Topic
Advanced Bandit Algorithms Research
Type
article
Field-Weighted Citation Impact
0.00

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article

An α-regret analysis of adversarial bilateral trade

Amos Fiat, Federico Fusco, Yossi Azar
Artificial Intelligence
Advanced Bandit Algorithms Research
article

An α-regret analysis of adversarial bilateral trade

Amos Fiat, Federico Fusco, Yossi Azar
article en

Abstract

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.

Artificial IntelligenceVol. 337
Ministero dell’Istruzione, dell’Università e della Ricerca, Israel Science Foundation, H2020 European Research Council
Openalex Percentile: Top 79%
Advanced Bandit Algorithms Research
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An α-regret analysis of adversarial bilateral trade — Amos Fiat, Federico Fusco, et al. · Artificial Intelligence (2024) | TGRS Research Map | TGRS