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 …
Encoding human behavior in information design through deep learning
We initiate the study of $\textit {behavioral information design} $ through deep learning. In
information design, a $\textit {sender} $ aims to persuade a $\textit {receiver} $ to take certain …
information design, a $\textit {sender} $ aims to persuade a $\textit {receiver} $ to take certain …
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 …
Data market design through deep learning
SS Ravindranath, Y Jiang… - Advances in Neural …, 2023 - 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 …
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 …
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 …
Optimal auctions through deep learning: Advances in differentiable economics
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 …
task. The single-item case was resolved in a seminal piece of work by Myerson in 1981, but …
Preferencenet: Encoding human preferences in auction design with deep learning
The design of optimal auctions is a problem of interest in economics, game theory and
computer science. Despite decades of effort, strategyproof, revenue-maximizing auction …
computer science. Despite decades of effort, strategyproof, revenue-maximizing auction …