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

• 成果类型: Article
• 署名作者: Tacchetti, Andrea; Koster, Raphael; Balaguer, Jan; Leqi, Liu; Pislar, Miruna; Botvinick, Matthew M.; Tuyls, Karl; Parkes, David C.; Summerfield, Christopher
• 署名单位: Alphabet Inc.; DeepMind; Google Incorporated; Princeton University; University of London; University College London; Yale University; Harvard University; University of Oxford
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12206
• DOI: 10.1073/pnas.2319949121
• 发表日期: 2025-06-24
• 关键词: public-goods; provision; models; LEVEL; games

摘要

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.