Profit Maximization in Bilateral Trade against a Smooth Adversary
Bilateral trade models the task of intermediating between two strategic agents, a seller and a buyer, who wish to trade a good. We study this problem from the perspective of a profit-maximizing broker within an online learning framework, where the agents' valuations are generated by a smooth adversary. We devise a learning algorithm that guarantees a $\tilde{O}(\sqrt{T})$ regret bound, which is tight in the time horizon $T$ up to poly-logarithmic factors. This matches the minimax rate for the stochastic i.i.d. case, and is also well separated from the adversarial setting, where sublinear-regret is unattainable. By extending the strong regret guarantees from the i.i.d. case to the smooth a...
| Authors | Simone Di Gregorio, Paul Dütting, Federico Fusco et al. |
|---|---|
| Primary category | cs.GT |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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