# Optimal Auctions through Deep Learning: Advances in Differentiable Economics

arXiv: https://arxiv.org/abs/1706.03459 · HTML: https://mlsanity.com/paper/1706.03459/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2017.

## Built on by (most cited first, 30 of 38)

- [GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning](https://mlsanity.com/paper/2406.07428/) (2024)
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- [Data Market Design through Deep Learning](https://mlsanity.com/paper/2310.20096/) (2023)
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- [Learning Payment-Free Resource Allocation Mechanisms](https://mlsanity.com/paper/2311.10927/) (2023)
- [Mechanism Design Is Not Enough: Prosocial Agents for Cooperative AI](https://mlsanity.com/paper/2605.08426/) (2026)
- [Automated Deterministic Auction Design with Objective Decomposition](https://mlsanity.com/paper/2402.11904/) (2024)
- [Social Environment Design](https://mlsanity.com/paper/2402.14090/) (2024)
- [Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.19420/) (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)
- [Mechanism-Based Intelligence (MBI): Differentiable Incentives for Rational Coordination and Guaranteed Alignment in Multi-Agent Systems](https://mlsanity.com/paper/2512.20688/) (2025)
- [Deep Incentive Design with Differentiable Equilibrium Blocks](https://mlsanity.com/paper/2603.07705/) (2026)
- [Guaranteed Trust Region Optimization via Two-Phase KL Penalization](https://mlsanity.com/paper/2312.05405/) (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)
- [Incentive-Aligned Multi-Source LLM Summaries](https://mlsanity.com/paper/2509.25184/) (2025)
- [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)
- [Large-Scale Contextual Market Equilibrium Computation through Deep Learning](https://mlsanity.com/paper/2406.15459/) (2024)
- [Regularized Proportional Fairness Mechanism for Resource Allocation Without Money](https://mlsanity.com/paper/2501.01111/) (2025)
- [Deep Learning for Double Auction](https://mlsanity.com/paper/2504.05355/) (2025)
- [Optimal Auction Design in the Joint Advertising](https://mlsanity.com/paper/2507.07418/) (2025)
- [Scalable Principal-Agent Contract Design via Gradient-Based Optimization](https://mlsanity.com/paper/2510.21177/) (2025)

## Abstract (arXiv)

Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981, but more than 40 years later a full analytical understanding of the optimal design still remains elusive for settings with two or more items. In this work, we initiate the exploration of the use of tools from deep learning for the automated design of optimal auctions. We model an auction as a multi-layer neural network, frame optimal auction design as a constrained learning problem, and show how it can be solved using standard machine learning pipelines. In addition to providing generalization bounds, we present extensive experimental results, recovering essentially all known solutions that come from the theoretical analysis of optimal auction design problems and obtaining novel mechanisms for settings in which the optimal mechanism is unknown.