# Efficient Two-Sided Markets with Limited Information

arXiv: https://arxiv.org/abs/2003.07503 · HTML: https://mlsanity.com/paper/2003.07503/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 14 of 14)

- [Bilateral Trade: A Regret Minimization Perspective](https://mlsanity.com/paper/2109.12974/) (2021)
- [A Regret Analysis of Bilateral Trade](https://mlsanity.com/paper/2102.08754/) (2021)
- [No-Regret Learning in Bilateral Trade via Global Budget Balance](https://mlsanity.com/paper/2310.12370/) (2023)
- [An α-regret analysis of Adversarial Bilateral Trade](https://mlsanity.com/paper/2210.06846/) (2022)
- [Repeated Bilateral Trade Against a Smoothed Adversary](https://mlsanity.com/paper/2302.10805/) (2023)
- [An Online Learning Theory of Brokerage](https://mlsanity.com/paper/2310.12107/) (2023)
- [Nearly Tight Regret Bounds for Profit Maximization in Bilateral Trade](https://mlsanity.com/paper/2509.22563/) (2025)
- [A Parametric Contextual Online Learning Theory of Brokerage](https://mlsanity.com/paper/2407.01566/) (2024)
- [Contextual Online Bilateral Trade](https://mlsanity.com/paper/2602.12903/) (2026)
- [A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokerage](https://mlsanity.com/paper/2503.02646/) (2025)
- [Do Data Valuations Make Good Data Prices?](https://mlsanity.com/paper/2504.05563/) (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)
- [Trading Volume Maximization with Online Learning](https://mlsanity.com/paper/2405.13102/) (2024)
- [Pricing Query Complexity of Multiplicative Revenue Approximation](https://mlsanity.com/paper/2602.10483/) (2026)

## Abstract (arXiv)

A celebrated impossibility result by Myerson and Satterthwaite (1983) shows that any truthful mechanism for two-sided markets that maximizes social welfare must run a deficit, resulting in a necessity to relax welfare efficiency and the use of approximation mechanisms. Such mechanisms in general make extensive use of the Bayesian priors. In this work, we investigate a question of increasing theoretical and practical importance: how much prior information is required to design mechanisms with near-optimal approximations? Our first contribution is a more general impossibility result stating that no meaningful approximation is possible without any prior information, expanding the famous impossibility result of Myerson and Satterthwaite. Our second contribution is that one em single sample (one number per item), arguably a minimum-possible amount of prior information, from each seller distribution is sufficient for a large class of two-sided markets. We prove matching upper and lower bounds on the best approximation that can be obtained with one single sample for subadditive buyers and additive sellers, regardless of computational considerations. Our third contribution is the design of computationally efficient blackbox reductions that turn any one-sided mechanism into a two-sided mechanism with a small loss in the approximation, while using only one single sample from each seller. On the way, our blackbox-type mechanisms deliver several interesting positive results in their own right, often beating even the state of the art that uses full prior information.