AI经济学家：通过人工智能驱动的税收政策改善平等和生产力
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
摘要：
应对现实世界的社会经济挑战需要设计和测试经济政策。然而，由于缺乏适当的（微观层面）经济数据和有限的实验机会，这在实践中很难做到。在这项工作中，我们训练社会规划人员，发现动态经济中的税收政策，可以有效地权衡经济平等和生产力。我们提出了一种两级深度强化学习方法来学习动态税收政策，该方法基于经济模拟，其中代理人和政府都在学习和适应。我们的数据驱动方法不使用经济建模假设，仅从观察数据中学习。我们做出了四个主要贡献。首先，我们提出了一个具有竞争压力和市场动态的经济模拟环境。我们验证模拟显示，基线税收制度的执行方式是一致的经济理论，包括在学习代理行为和专业化。其次，我们发现，人工智能驱动的税收政策比基线政策（包括著名的Saez税收框架）将平等和生产力之间的权衡提高了16%。第三，我们展示了几个新出现的特征：人工智能驱动的税收政策与基线有质的不同，设定了更高的最高税率和更高的低收入净补贴。此外，人工智能驱动的税收政策在人工智能代理学习的紧急税收游戏策略面前表现强劲。最后，人工智能驱动的税收政策在人类参与者的实验中也是有效的。在MTurk上进行的实验中，人工智能税收政策提供了与Saez框架类似的平等-生产力权衡，以及更高的逆收入加权社会福利。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
