research-article

Differentiable economics for randomized affine maximizer auctions

AUTHORs:

Tuomas Sandholm,

John DickersonAuthors Info & Claims

IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

Article No.: 293, Pages 2633 - 2641

https://doi.org/10.24963/ijcai.2023/293

Published: 19 August 2023 Publication History

Abstract

A recent approach to automated mechanism design, differentiable economics, represents auctions by rich function approximators and optimizes their performance by gradient descent. The ideal auction architecture for differentiable economics would be perfectly strategyproof, support multiple bidders and items, and be rich enough to represent the optimal (i.e. revenue-maximizing) mechanism. So far, such an architecture does not exist. There are single-bidder approaches (MenuNet, Rochet-Net) which are always strategyproof and can represent optimal mechanisms. RegretNet is multibidder and can approximate any mechanism, but is only approximately strategyproof. We present an architecture that supports multiple bidders and is perfectly strategyproof, but cannot necessarily represent the optimal mechanism. This architecture is the classic affine maximizer auction (AMA), modified to offer lotteries. By using the gradient-based optimization tools of differentiable economics, we can now train lottery AMAs, competing with or outperforming prior approaches in revenue.

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Cited By

Tewolde EConitzer VLarson K(2024)Game transformations that preserve Nash equilibria or best-response setsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/331(2984-2993)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/331
Dütting PFeng ZNarasimhan HParkes DRavindranath S(2024)Optimal Auctions through Deep Learning: Advances in Differentiable EconomicsJournal of the ACM10.1145/363074971:1(1-53)Online publication date: 11-Feb-2024
https://dl.acm.org/doi/10.1145/3630749

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Differentiable economics for randomized affine maximizer auctions

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cover image Guide Proceedings

IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

August 2023

7242 pages

ISBN:978-1-956792-03-4

Editor:
Edith Elkind

Copyright © 2023 International Joint Conferences on Artificial Intelligence.

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Published: 19 August 2023

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Cited By

Tewolde EConitzer VLarson K(2024)Game transformations that preserve Nash equilibria or best-response setsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/331(2984-2993)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/331
Dütting PFeng ZNarasimhan HParkes DRavindranath S(2024)Optimal Auctions through Deep Learning: Advances in Differentiable EconomicsJournal of the ACM10.1145/363074971:1(1-53)Online publication date: 11-Feb-2024
https://dl.acm.org/doi/10.1145/3630749

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