  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 this work, we train social planners that discover tax policies in dynamic
economies that can effectively trade-off economic equality and productivity. We
propose a two-level deep reinforcement learning approach to learn dynamic tax
policies, based on economic simulations in which both agents and a government
learn and adapt. Our data-driven approach does not make use of economic
modeling assumptions, and learns from observational data alone. We make four
main contributions. First, we present an economic simulation environment that
features competitive pressures and market dynamics. We validate the simulation
by showing that baseline tax systems perform in a way that is consistent with
economic theory, including in regard to learned agent behaviors and
specializations. Second, we show that AI-driven tax policies improve the
trade-off between equality and productivity by 16% over baseline policies,
including the prominent Saez tax framework. Third, we showcase several emergent
features: AI-driven tax policies are qualitatively different from baselines,
setting a higher top tax rate and higher net subsidies for low incomes.
Moreover, AI-driven tax policies perform strongly in the face of emergent
tax-gaming strategies learned by AI agents. Lastly, AI-driven tax policies are
also effective when used in experiments with human participants. In experiments
conducted on MTurk, an AI tax policy provides an equality-productivity
trade-off that is similar to that provided by the Saez framework along with
higher inverse-income weighted social welfare.
