Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist

@article{Trott2021BuildingAF,
  title={Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist},
  author={Alexander Trott and Sunil Srinivasa and Douwe van der Wal and Sebastien Haneuse and Stephan Zheng},
  journal={Machine Learning eJournal},
  year={2021}
}
Optimizing economic and public policy is critical to address socioeconomic issues and trade-offs, e.g., improving equality, productivity, or wellness, and poses a complex mechanism design problem. A policy designer needs to consider multiple objectives, policy levers, and behavioral responses from strategic actors who optimize for their individual objectives. Moreover, real-world policies should be explainable and robust to simulation-to-reality gaps, e.g., due to calibration issues. Existing… Expand
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