# Automated Mechanism Design via Neural Networks

arXiv: https://arxiv.org/abs/1805.03382 · HTML: https://mlsanity.com/paper/1805.03382/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 30 of 32)

- [Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising](https://mlsanity.com/paper/2106.03593/) (2021)
- [Optimal-er Auctions through Attention](https://mlsanity.com/paper/2202.13110/) (2022)
- [PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning](https://mlsanity.com/paper/2106.03215/) (2021)
- [Differentiable Economics for Randomized Affine Maximizer Auctions](https://mlsanity.com/paper/2202.02872/) (2022)
- [A Context-Integrated Transformer-Based Neural Network for Auction Design](https://mlsanity.com/paper/2201.12489/) (2022)
- [Deep Learning for Two-Sided Matching](https://mlsanity.com/paper/2107.03427/) (2021)
- [Learning Revenue-Maximizing Auctions With Differentiable Matching](https://mlsanity.com/paper/2106.07877/) (2021)
- [GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning](https://mlsanity.com/paper/2406.07428/) (2024)
- [Learning to Mitigate AI Collusion on Economic Platforms](https://mlsanity.com/paper/2202.07106/) (2022)
- [AI-driven Prices for Externalities and Sustainability in Production Markets](https://mlsanity.com/paper/2106.06060/) (2021)
- [Mode Connectivity in Auction Design](https://mlsanity.com/paper/2305.11005/) (2023)
- [Automated Mechanism Design for Classification with Partial Verification](https://mlsanity.com/paper/2104.05182/) (2021)
- [Recommender Systems meet Mechanism Design](https://mlsanity.com/paper/2110.12558/) (2021)
- [Automated Deterministic Auction Design with Objective Decomposition](https://mlsanity.com/paper/2402.11904/) (2024)
- [Social Environment Design](https://mlsanity.com/paper/2402.14090/) (2024)
- [An Interpretable Automated Mechanism Design Framework with Large Language Models](https://mlsanity.com/paper/2502.12203/) (2025)
- [Aligned Textual Scoring Rules](https://mlsanity.com/paper/2507.06221/) (2025)
- [A Survey of Online Auction Mechanism Design Using Deep Learning Approaches](https://mlsanity.com/paper/2110.06880/) (2021)
- [Neural Auctions Compromise Bidder Information](https://mlsanity.com/paper/2303.00116/) (2023)
- [Optimal Automated Market Makers: Differentiable Economics and Strong Duality](https://mlsanity.com/paper/2402.09129/) (2024)
- [Deep Reinforcement Learning for Sequential Combinatorial Auctions](https://mlsanity.com/paper/2407.08022/) (2024)
- [Learning Truthful Mechanisms without Discretization](https://mlsanity.com/paper/2506.22911/) (2025)
- [Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Design](https://mlsanity.com/paper/2601.13489/) (2026)
- [Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models](https://mlsanity.com/paper/2604.06263/) (2026)
- [Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications](https://mlsanity.com/paper/2401.05683/) (2024)
- [Statistically Truthful Auctions via Acceptance Rule](https://mlsanity.com/paper/2405.12016/) (2024)
- [Learning Fair and Preferable Allocations through Neural Network](https://mlsanity.com/paper/2410.17500/) (2024)
- [Learning Neural Strategy-Proof Matching Mechanism from Examples](https://mlsanity.com/paper/2410.19384/) (2024)
- [Learning Recommender Mechanisms for Bayesian Stochastic Games](https://mlsanity.com/paper/2505.22979/) (2025)
- [Universal Representation of Generalized Convex Functions and their Gradients](https://mlsanity.com/paper/2509.04477/) (2025)

## Abstract (arXiv)

Using AI approaches to automatically design mechanisms has been a central research mission at the interface of AI and economics [Conitzer and Sandholm, 2002]. Previous approaches that attempt to design revenue optimal auctions for the multi-dimensional settings fall short in at least one of the three aspects: 1) representation -- search in a space that probably does not even contain the optimal mechanism; 2) exactness -- finding a mechanism that is either not truthful or far from optimal; 3) domain dependence -- need a different design for different environment settings. To resolve the three difficulties, in this paper, we put forward -- MenuNet -- a unified neural network based framework that automatically learns to design revenue optimal mechanisms. Our framework consists of a mechanism network that takes an input distribution for training and outputs a mechanism, as well as a buyer network that takes a mechanism as input and output an action. Such a separation in design mitigates the difficulty to impose incentive compatibility constraints on the mechanism, by making it a rational choice of the buyer. As a result, our framework easily overcomes the previously mentioned difficulty in incorporating IC constraints and always returns exactly incentive compatible mechanisms. We then apply our framework to a number of multi-item revenue optimal design settings, for a few of which the theoretically optimal mechanisms are unknown. We then go on to theoretically prove that the mechanisms found by our framework are indeed optimal. To the best of our knowledge, we are the first to apply neural networks to discover optimal auction mechanisms with provable optimality.