Stephan Zheng, Tonghan Wang, Milind Tambe, Yiling Chen, David Parkes, Edwin Zhang, Safwan Hossain, Sadie Zhao, Henry Gasztowtt
We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ezhang7423/social-environment-design | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| vec_env_args | Ran | ezhang7423/social-environment-design/sen/vector_constructors.py code served (permissive licence) · get_code("781cb85196d3a3a2") |
| vote | Ran | ezhang7423/social-environment-design/sen/principal/utils.py code served (permissive licence) · get_code("77c8a1d04ac39914") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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 in 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 policymaking. By solving these challenges, we hope to achieve various social welfare objectives, thereby promoting more ethical and responsible decision making.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.14090")
get_code_for_paper("2402.14090")
have("2402.14090")
Connect an agent — have() is free.