# Improved Approximation to First-Best Gains-from-Trade

arXiv: https://arxiv.org/abs/2205.00140 · HTML: https://mlsanity.com/paper/2205.00140/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2022.

## Built on by (most cited first, 4 of 4)

- [No-Regret Learning in Bilateral Trade via Global Budget Balance](https://mlsanity.com/paper/2310.12370/) (2023)
- [Nearly Tight Regret Bounds for Profit Maximization in Bilateral Trade](https://mlsanity.com/paper/2509.22563/) (2025)
- [A New Lower Bound for the Random Offerer Mechanism in Bilateral Trade using AI-Guided Evolutionary Search](https://mlsanity.com/paper/2603.08679/) (2026)
- [Regret Minimization in Bilateral Trade With Perturbed Markets](https://mlsanity.com/paper/2605.10475/) (2026)

## Abstract (arXiv)

We study the two-agent single-item bilateral trade. Ideally, the trade should happen whenever the buyer's value for the item exceeds the seller's cost. However, the classical result of Myerson and Satterthwaite showed that no mechanism can achieve this without violating one of the Bayesian incentive compatibility, individual rationality and weakly balanced budget conditions. This motivates the study of approximating the trade-whenever-socially-beneficial mechanism, in terms of the expected gains-from-trade. Recently, Deng, Mao, Sivan, and Wang showed that the random-offerer mechanism achieves at least a 1/8.23 approximation. We improve this lower bound to 1/3.15 in this paper. We also determine the exact worst-case approximation ratio of the seller-pricing mechanism assuming the distribution of the buyer's value satisfies the monotone hazard rate property.