English(EN) Regret Bounds for Profit Maximization in Bilateral Trade: Learning the Best Posted-Price Mechanism

新算法为人工智能驱动的交易机制提供了近乎最优的遗憾界限

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了用于双边贸易场景中利润最大化的新算法，重点关注经纪人的遗憾最小化。该研究引入了一种在对抗性设置下具有 $\tilde O(T^{\frac 23})$ 遗憾率的学习算法，该算法接近最优。对于满足单调风险率特性的独立分布，另一种算法实现了接近紧密的 $\tilde{O}(\sqrt{T})$ 遗憾率。该论文还证明了单调风险率分布和常规分布之间的理论分离，表明后者的最小最大遗憾率为 $\tilde \Theta(T^{\frac 35})$。 AI

影响引入了先进的学习算法，可以优化复杂经济环境中的交易策略。

排序理由学术论文，详细介绍了机器学习和经济学特定问题的 novel 算法和理论界限。 [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Johannes Br\"ustle, Simone Di Gregorio, Federico Fusco, Stefano Leonardi · 2026-11-06 05:00

双边贸易中利润最大化的遗憾界限：学习最佳的定价机制

arXiv:2611.08173v1 Announce Type: cross Abstract: Bilateral trade models the task of intermediating between two strategic agents, a seller and a buyer, willing to trade a good for which they hold private valuations. We study this problem from the perspective of a profit-maximizin…

本文如何被排名

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双边贸易中利润最大化的遗憾界限：学习最佳的定价机制

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