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Social Environment Design

Author

Listed:
  • Edwin Zhang
  • Sadie Zhao
  • Tonghan Wang
  • Safwan Hossain
  • Henry Gasztowtt
  • Stephan Zheng
  • David C. Parkes
  • Milind Tambe
  • Yiling Chen

Abstract

Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making. This paper proposes a new research agenda towards this end by introducing Social Environment Design, a general framework for the use of AI for automated policy-making that connects with the Reinforcement Learning, EconCS, and Computational Social Choice communities. The framework seeks to capture general economic environments, includes voting on policy objectives, and gives a direction for the systematic analysis of government and economic policy through AI simulation. We highlight key open problems for future research in AI-based policy-making. By solving these challenges, we hope to achieve various social welfare objectives, thereby promoting more ethical and responsible decision making.

Suggested Citation

  • Edwin Zhang & Sadie Zhao & Tonghan Wang & Safwan Hossain & Henry Gasztowtt & Stephan Zheng & David C. Parkes & Milind Tambe & Yiling Chen, 2024. "Social Environment Design," Papers 2402.14090, arXiv.org.
    • Handle: RePEc:arx:papers:2402.14090
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    References listed on IDEAS

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    1. Raphael Koster & Jan Balaguer & Andrea Tacchetti & Ari Weinstein & Tina Zhu & Oliver Hauser & Duncan Williams & Lucy Campbell-Gillingham & Phoebe Thacker & Matthew Botvinick & Christopher Summerfield, 2022. "Human-centred mechanism design with Democratic AI," Nature Human Behaviour, Nature, vol. 6(10), pages 1398-1407, October.
      • Raphael Koster & Jan Balaguer & Andrea Tacchetti & Ari Weinstein & Tina Zhu & Oliver Hauser & Duncan Williams & Lucy Campbell-Gillingham & Phoebe Thacker & Matthew Botvinick & Christopher Summerfield, 2022. "Human-centered mechanism design with Democratic AI," Papers 2201.11441, arXiv.org.
    2. Aussel, Didier & Brotcorne, Luce & Lepaul, Sébastien & von Niederhäusern, Léonard, 2020. "A trilevel model for best response in energy demand-side management," European Journal of Operational Research, Elsevier, vol. 281(2), pages 299-315.
    3. Martin Bichler & Nils Kohring & Stefan Heidekrüger, 2023. "Learning Equilibria in Asymmetric Auction Games," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 523-542, May.
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