Deep mechanism design: Learning social and economic policies for human benefit

Human society is coordinated by mechanisms that control how prices are agreed, taxes are set, and electoral votes are tallied. The design of robust and effective mechanisms for human benefit is a core problem in the social, economic, and political sciences. Here, we discuss the recent application of modern tools from AI research, including deep neural networks trained with reinforcement learning (RL), to create more desirable mechanisms for people. We review the application of machine learning to design effective auctions, learn optimal tax policies, and discover redistribution policies that win the popular vote among human users. We discuss the challenge of accurately modeling human preferences and the problem of aligning a mechanism to the wishes of a potentially diverse group. We highlight the importance of ensuring that research into "deep mechanism design" is conducted safely and ethically.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2025-06-16
DOI
https://doi.org/10.1073/pnas.2319949121
Citations
3
Primary Topic
Auction Theory and Applications
Type
article
Field-Weighted Citation Impact
6.32
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article

Deep mechanism design: Learning social and economic policies for human benefit

Karl Tuyls, Liu Leqi, Mîruna Pislar, Christopher Summerfield et al.
3 citations
Proceedings of the National Academy of Sciences
Auction Theory and Applications
6.32
article

Deep mechanism design: Learning social and economic policies for human benefit

Karl Tuyls, Liu Leqi, Mîruna Pislar, Christopher Summerfield, Andrea Tacchetti, Raphaël Koster, Matthew Botvinick, David C. Parkes, Jan Balaguer
article en
3 citations

Abstract

Human society is coordinated by mechanisms that control how prices are agreed, taxes are set, and electoral votes are tallied. The design of robust and effective mechanisms for human benefit is a core problem in the social, economic, and political sciences. Here, we discuss the recent application of modern tools from AI research, including deep neural networks trained with reinforcement learning (RL), to create more desirable mechanisms for people. We review the application of machine learning to design effective auctions, learn optimal tax policies, and discover redistribution policies that win the popular vote among human users. We discuss the challenge of accurately modeling human preferences and the problem of aligning a mechanism to the wishes of a potentially diverse group. We highlight the importance of ensuring that research into "deep mechanism design" is conducted safely and ethically.

Proceedings of the National Academy of SciencesVol. 122(25)
Google (United States) (US), Harvard University (US), Princeton University (US), Yale University (US), University of Oxford (GB), Google DeepMind (United Kingdom) (GB), Google (United Kingdom) (GB), Oxford Centre for Computational Neuroscience (GB), Stichting Wetenschappelijk Onderzoek Verkeersveiligheid (NL), University College London (GB)
Openalex Percentile: Top 4%
Auction Theory and Applications
6.32
DOIOpen Access PDFOpenAlex
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Deep mechanism design: Learning social and economic policies for human benefit — Karl Tuyls, Liu Leqi, et al. · Proceedings of the National Academy of Sciences (2025) | TGRS Research Map | TGRS