The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning

  title={The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning},
  author={Stephan Zheng and Alexander Trott and Sunil Srinivasa and David C. Parkes and Richard Socher},
  journal={ERN: Efficiency; Optimal Taxation (Topic)},
  • Stephan Zheng, Alexander Trott, +2 authors R. Socher
  • Published 5 August 2021
  • Computer Science, Economics
  • ERN: Efficiency; Optimal Taxation (Topic)
AI and reinforcement learning (RL) have improved many areas, but are not yet widely adopted in economic policy design, mechanism design, or economics at large. At the same time, current economic methodology is limited by a lack of counterfactual data, simplistic behavioral models, and limited opportunities to experiment with policies and evaluate behavioral responses. Here we show that machine-learning-based economic simulation is a powerful policy and mechanism design framework to overcome… Expand
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Determination and reduction of translocator protein (TSPO) ligand rs6971 discrimination† †The authors declare no competing interests.
The 18 kDa translocator protein (TSPO) is a target for development of diagnostic imaging agents for glioblastoma and neuroinflammation.
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We thank Kathy Baxter for the ethical review
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