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) economic data and limited opportunity to experiment. In Zheng et al., 2020, we propose a two-level deep reinforcement learning approach to learn dynamic tax policies, based on principled economic simulations in which both agents and a social planner (government) learn and adapt. AI social planners can discover tax policies that improve the equality and productivity trade-off by at least 16%, compared to the prominent Saez tax model, US Federal tax, and the free market. The learned tax policies are qualitatively different from the baselines, and certain model instances are effective in human studies as well.

This talk will present three topics: 1) economic policy design in the context of multi-agent RL, 2) our two-level RL approach to economic policy design, and 3) open research problems towards an AI Economist for the real world. These include key methodological challenges in two-level RL and data-driven economic modeling, multi-agent RL, mechanism design, convergence guarantees, robustness, explainability, and others.

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