Machine Learning for Optimal Economic Design

Dütting, Paul; Feng, Zhe; Golowich, Noah; Narasimhan, Harikrishna; Parkes, David C.; Ravindranath, Sai Srivatsa

doi:10.1007/978-3-030-18050-8_70

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning methods have brought about a turnaround in this matter. For example, Duting [11] designed a simple three-layer MLP network (RochetNet) based on Rochet's idea [12], successfully solving the single-attribute optimal auction problem for multiple items and single bidders. Subsequently, the author proposed a new network structure (RegretNet) to solve the optimal auction problem of multiple items and bidders.…”

Section: Related Workmentioning

confidence: 99%

Optimal Multi-Attribute Auctions Based on Multi-Scale Loss Network

Zhao

Cai

et al. 2023

Mathematics

View full text Add to dashboard Cite

There is a strong demand for multi-attribute auctions in real-world scenarios for non-price attributes that allow participants to express their preferences and the item’s value. However, this also makes it difficult to perform calculations with incomplete information, as a single attribute—price—no longer determines the revenue. At the same time, the mechanism must satisfy individual rationality (IR) and incentive compatibility (IC). This paper proposes an innovative dual network to solve these problems. A shared MLP module is constructed to extract bidder features, and multiple-scale loss is used to determine network status and update. The method was tested on real and extended cases, showing that the approach effectively improves the auctioneer’s revenue without compromising the bidder.

show abstract

Section: Related Workmentioning

confidence: 99%