English

Bilateral Trade: A Regret Minimization Perspective

Computer Science and Game Theory 2021-09-28 v1 Machine Learning Theoretical Economics

Abstract

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. In this paper, we cast the bilateral trade problem in a regret minimization framework over TT rounds of seller/buyer interactions, with no prior knowledge on their private valuations. Our main contribution is a complete characterization of the regret regimes for fixed-price mechanisms with different feedback models and private valuations, using as a benchmark the best fixed-price in hindsight. More precisely, we prove the following tight bounds on the regret: - Θ(T)\Theta(\sqrt{T}) for full-feedback (i.e., direct revelation mechanisms). - Θ(T2/3)\Theta(T^{2/3}) for realistic feedback (i.e., posted-price mechanisms) and independent seller/buyer valuations with bounded densities. - Θ(T)\Theta(T) for realistic feedback and seller/buyer valuations with bounded densities. - Θ(T)\Theta(T) for realistic feedback and independent seller/buyer valuations. - Θ(T)\Theta(T) for the adversarial setting.

Keywords

Cite

@article{arxiv.2109.12974,
  title  = {Bilateral Trade: A Regret Minimization Perspective},
  author = {Nicolò Cesa-Bianchi and Tommaso Cesari and Roberto Colomboni and Federico Fusco and Stefano Leonardi},
  journal= {arXiv preprint arXiv:2109.12974},
  year   = {2021}
}

Comments

arXiv admin note: substantial text overlap with arXiv:2102.08754

R2 v1 2026-06-24T06:22:29.135Z