Optimal auctions through deep learning
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 …
task. The single-item case was resolved in a seminal piece of work by Myerson in 1981 …
A scalable neural network for DSIC affine maximizer auction design
Automated auction design aims to find empirically high-revenue mechanisms through
machine learning. Existing works on multi item auction scenarios can be roughly divided into …
machine learning. Existing works on multi item auction scenarios can be roughly divided into …
Optimal-er auctions through attention
D Ivanov, I Safiulin, I Filippov… - Advances in Neural …, 2022 - proceedings.neurips.cc
RegretNet is a recent breakthrough in the automated design of revenue-maximizing
auctions. It combines the flexibility of deep learning with the regret-based approach to relax …
auctions. It combines the flexibility of deep learning with the regret-based approach to relax …
A context-integrated transformer-based neural network for auction design
One of the central problems in auction design is developing an incentive-compatible
mechanism that maximizes the auctioneer's expected revenue. While theoretical …
mechanism that maximizes the auctioneer's expected revenue. While theoretical …
Breaking the traditional: a survey of algorithmic mechanism design applied to economic and complex environments
Q Chen, X Wang, ZL Jiang, Y Wu, H Li, L Cui… - Neural Computing and …, 2023 - Springer
The mechanism design theory can be applied not only in the economy but also in many
fields, such as politics and military affairs, which has important practical and strategic …
fields, such as politics and military affairs, which has important practical and strategic …
Differentiable economics for randomized affine maximizer auctions
A recent approach to automated mechanism design, differentiable economics, represents
auctions by rich function approximators and optimizes their performance by gradient …
auctions by rich function approximators and optimizes their performance by gradient …
Data market design through deep learning
SS Ravindranath, Y Jiang… - Advances in Neural …, 2024 - proceedings.neurips.cc
The data market design problem is a problem in economic theory to find a set of signaling
schemes (statistical experiments) to maximize expected revenue to the information seller …
schemes (statistical experiments) to maximize expected revenue to the information seller …
Auction learning as a two-player game
J Rahme, S Jelassi, SM Weinberg - arXiv preprint arXiv:2006.05684, 2020 - arxiv.org
Designing an incentive compatible auction that maximizes expected revenue is a central
problem in Auction Design. While theoretical approaches to the problem have hit some …
problem in Auction Design. While theoretical approaches to the problem have hit some …
Benefits of permutation-equivariance in auction mechanisms
Designing an incentive-compatible auction mechanism that maximizes the auctioneer's
revenue while minimizes the bidders' ex-post regret is an important yet intricate problem in …
revenue while minimizes the bidders' ex-post regret is an important yet intricate problem in …
Optimal auctions through deep learning
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 …
task. The single-item case was resolved in a seminal piece of work by Myerson in 1981 …