# Neural Double Auction Mechanism

arXiv: https://arxiv.org/abs/2412.11465 · HTML: https://mlsanity.com/paper/2412.11465/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Truthful and Trustworthy IoT AI Agents via Immediate-Penalty Enforcement under Approximate VCG Mechanisms](https://mlsanity.com/paper/2512.00513/) (2025)

## Abstract (arXiv)

Mechanism design, a branch of economics, aims to design rules that can autonomously achieve desired outcomes in resource allocation and public decision making. The research on mechanism design using machine learning is called automated mechanism design or mechanism learning. In our research, we constructed a new network based on the existing method for single auctions and aimed to automatically design a mechanism by applying it to double auctions. In particular, we focused on the following four desirable properties for the mechanism: individual rationality, balanced budget, Pareto efficiency, and incentive compatibility. We conducted experiments assuming a small-scale double auction and clarified how deterministic the trade matching of the obtained mechanism is. We also confirmed how much the learnt mechanism satisfies the four properties compared to two representative protocols. As a result, we verified that the mechanism is more budget-balanced than the VCG protocol and more economically efficient than the MD protocol, with the incentive compatibility mostly guaranteed.