# Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications

arXiv: https://arxiv.org/abs/2401.05683 · HTML: https://mlsanity.com/paper/2401.05683/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 21 of 21)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep Models of Interactions Across Sets](https://mlsanity.com/paper/1803.02879/) (2018)
- [Optimal Auctions through Deep Learning: Advances in Differentiable Economics](https://mlsanity.com/paper/1706.03459/) (2017)
- [Complexity of Mechanism Design](https://mlsanity.com/paper/1408.1486/) (2014)
- [Automated Mechanism Design via Neural Networks](https://mlsanity.com/paper/1805.03382/) (2018)
- [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)
- [A Scalable Neural Network for DSIC Affine Maximizer Auction Design](https://mlsanity.com/paper/2305.12162/) (2023)
- [PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning](https://mlsanity.com/paper/2106.03215/) (2021)
- [ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning](https://mlsanity.com/paper/2010.06398/) (2020)
- [Learning Revenue-Maximizing Auctions With Differentiable Matching](https://mlsanity.com/paper/2106.07877/) (2021)
- [Deep Learning-powered Iterative Combinatorial Auctions](https://mlsanity.com/paper/1907.05771/) (2019)
- [Machine Learning-powered Iterative Combinatorial Auctions](https://mlsanity.com/paper/1911.08042/) (2019)
- [Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach](https://mlsanity.com/paper/1711.02844/) (2017)
- [Learning Optimal Redistribution Mechanisms through Neural Networks](https://mlsanity.com/paper/1801.08808/) (2018)
- [Fair Division of Indivisible Goods Among Strategic Agents](https://mlsanity.com/paper/1901.09427/) (2019)
- [A Survey of Online Auction Mechanism Design Using Deep Learning Approaches](https://mlsanity.com/paper/2110.06880/) (2021)
- [EEF1-NN: Efficient and EF1 allocations through Neural Networks](https://mlsanity.com/paper/2112.05436/) (2021)
- [Learning Algorithms for Intelligent Agents and Mechanisms](https://mlsanity.com/paper/2210.02654/) (2022)
- [Designing Fair, Cost-optimal Auctions based on Deep Learning for Procuring Agricultural Inputs through Farmer Collectives](https://mlsanity.com/paper/2304.07341/) (2023)

## Abstract (arXiv)

Mechanism design is essentially reverse engineering of games and involves inducing a game among strategic agents in a way that the induced game satisfies a set of desired properties in an equilibrium of the game. Desirable properties for a mechanism include incentive compatibility, individual rationality, welfare maximisation, revenue maximisation (or cost minimisation), fairness of allocation, etc. It is known from mechanism design theory that only certain strict subsets of these properties can be simultaneously satisfied exactly by any given mechanism. Often, the mechanisms required by real-world applications may need a subset of these properties that are theoretically impossible to be simultaneously satisfied. In such cases, a prominent recent approach is to use a deep learning based approach to learn a mechanism that approximately satisfies the required properties by minimizing a suitably defined loss function. In this paper, we present, from relevant literature, technical details of using a deep learning approach for mechanism design and provide an overview of key results in this topic. We demonstrate the power of this approach for three illustrative case studies: (a) efficient energy management in a vehicular network (b) resource allocation in a mobile network (c) designing a volume discount procurement auction for agricultural inputs. Section 6 concludes the paper.