AIGNMLFeb 21, 2024

Social Environment Design

HarvardTsinghua
arXiv:2402.14090v36 citationsh-index: 14
Originality Synthesis-oriented
AI Analysis

It addresses the problem of enhancing policy-making for governments and economies, but is incremental as it builds on existing fields like Reinforcement Learning and Computational Social Choice.

The paper introduces Social Environment Design, a framework for using AI in automated policy-making to improve government and economic decisions, aiming to achieve social welfare objectives through AI simulation.

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.

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Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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