Bilateral trade and two-sided markets | Vidéo | Carmin.tv

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Bilateral trade and two-sided markets

By Stefano Leonardi

Appears in collection : From matchings to markets. A tale of Mathematics, Economics and Computer Science. / Des matchings aux marchés. Une histoire de mathématiques

We study repeated bilateral trade where an adaptive σ-smooth adversary generates the valuations of sellers and buyers. We provide a complete characterization of the regret regimes for fixed-price mechanisms under different feedback models in the two cases where the learner can post either the same or different prices to buyers and sellers. We begin by showing that the minimax regret after $T$ rounds is of order $\sqrt{T}$ in the full-feedback scenario. Under partial feedback, any algorithm that has to post the same price to buyers and sellers suffers worst-case linear regret. However, when the learner can post two different prices at each round, we design an algorithm enjoying regret of order $T^{3/4}$ ignoring log factors. We prove that this rate is optimal by presenting a surprising $T^{3/4}$ lower bound, which is the main technical contribution of the paper.

Information about the video

Date of recording 12/12/2023
Date of publication 22/12/2023
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers, Graduate Students
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.20119103
Cite this video Leonardi, Stefano (12/12/2023). Bilateral trade and two-sided markets. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.20119103
URL https://dx.doi.org/10.24350/CIRM.V.20119103

Domain(s)

Machine Learning

Bibliography

CESA-BIANCHI, Nicolò, CESARI, Tommaso R., COLOMBONI, Roberto, et al. Repeated bilateral trade against a smoothed adversary. In : The Thirty Sixth Annual Conference on Learning Theory. PMLR, 2023. p. 1095-1130. - https://arxiv.org/abs/2302.10805
DÜTTING, Paul, FUSCO, Federico, LAZOS, Philip, et al. Efficient two-sided markets with limited information. In : Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing. 2021. p. 1452-1465. - http://dx.doi.org/10.1145/3406325.3451076

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