44 citations · 89 across the 8 of their papers we have counts for
9 papers
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning
Michael Curry, Alexander Trott, Soham Phade +2
Real economies can be modeled as a sequential imperfect-information game with many heterogeneous agents, such as consumers, firms, and governments. Dynamic general equilibrium (DGE…
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal
Tong Mu, Stephan Zheng, Alexander Trott
Principal-Agent (PA) problems describe a broad class of economic relationships characterized by misaligned incentives and asymmetric information. The Principal's problem is to find…
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist
Alexander Trott, Sunil Srinivasa, Douwe van der Wal +2
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…
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning
Stephan Zheng, Alexander Trott, Sunil Srinivasa +2
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, cu…
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
Stephan Zheng, Alexander Trott, Sunil Srinivasa +4
Tackling real-world socio-economic challenges requires designing and testing economic policies. However, this is hard in practice, due to a lack of appropriate (micro-level) econom…
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Víctor Campos, Alexander Trott, Caiming Xiong +3
Acquiring abilities in the absence of a task-oriented reward function is at the frontier of reinforcement learning research. This problem has been studied through the lens of empow…