.png){kind=icon}
Return
Optimal Auctions Through Deep Learning
.png){kind=icon}
OA
AI
DOI：10.1145/3470442.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. Even after 30-40 years of intense research, the problem remains unsolved for settings with two or more items. We overview recent research results that show how tools from deep learning are shaping up to become a powerful tool for the automated design of near-optimal auctions auctions. In this approach, an auction is modeled as a multi-layer neural network, with optimal auction design framed as a constrained learning problem that can be addressed with standard machine learning pipelines. Through this approach, it is possible to recover to a high degree of accuracy essentially all known analytically derived solutions for multi-item settings and obtain novel mechanisms for settings in which the optimal mechanism is unknown.
Keywords:
MECHANISM
DUALITY
DESIGN
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
12.2
Papers:
1.2W
Citations:
3.7W
Organization
Cited Papers
ShareSaveEffect of Scent-Marking in Delaying Territorial Invasion in the Blind Mole-Rat Spalax Ehrenbergi.png)
Behaviour
BehaviourIF0
PREAI
ShareSaveShareSaveWSPRT Methods for Improving Power System Automation Devices in the Conditions of Distributed Generation Sources OperationEnergies
OAAI
EnergiesIF0
OAAIShareSaveShareSaveThe relation between parents' mental state talk and children's social understanding: A meta‐analysisOAAI
OAAIShareSaveAssessment of the Gross, Technical and Economic Potential of Region’s Solar Energy for Photovoltaic EnergeticsEnergiesOAAI
EnergiesIF0
OAAIShareSaveShareSave
View more