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[2602.12904] Nonparametric Contextual Online Bilateral Trade
Summary
This paper explores nonparametric contextual online bilateral trade, presenting an algorithm that optimizes trade pricing based on contextual information while ensuring budget balance and one-bit feedback.
Why It Matters
The study addresses a significant gap in online trading algorithms by moving beyond linear models to a nonparametric approach. This has implications for improving trade efficiency in various markets, particularly where private valuations are not disclosed. Understanding these dynamics can enhance algorithmic trading strategies and market design.
Key Takeaways
- Introduces a nonparametric approach to contextual online bilateral trade.
- Develops an algorithm that guarantees low regret under stringent conditions.
- Focuses on one-bit feedback and strong budget balance in trading scenarios.
- Demonstrates the tightness of the regret bound with a matching lower bound.
- Contributes to the understanding of trade dynamics in environments with hidden valuations.
Computer Science > Computer Science and Game Theory arXiv:2602.12904 (cs) [Submitted on 13 Feb 2026] Title:Nonparametric Contextual Online Bilateral Trade Authors:Emanuele Coccia, Martino Bernasconi, Andrea Celli View a PDF of the paper titled Nonparametric Contextual Online Bilateral Trade, by Emanuele Coccia and 2 other authors View PDF HTML (experimental) Abstract:We study the problem of contextual online bilateral trade. At each round, the learner faces a seller-buyer pair and must propose a trade price without observing their private valuations for the item being sold. The goal of the learner is to post prices to facilitate trades between the two parties. Before posting a price, the learner observes a $d$-dimensional context vector that influences the agent's valuations. Prior work in the contextual setting has focused on linear models. In this work, we tackle a general nonparametric setting in which the buyer's and seller's valuations behave according to arbitrary Lipschitz functions of the context. We design an algorithm that leverages contextual information through a hierarchical tree construction and guarantees regret $\widetilde{O}(T^{{(d-1)}/d})$. Remarkably, our algorithm operates under two stringent features of the setting: (1) one-bit feedback, where the learner only observes whether a trade occurred or not, and (2) strong budget balance, where the learner cannot subsidize or profit from the market participants. We further provide a matching lower bound in th...
