[论文解读] The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies

Stephan Zheng, Alexander Trott+5 more|arXiv (Cornell University)|Apr 28, 2020

Energy, Environment, and Transportation Policies参考文献 83被引用 44

一句话总结

本论文训练一个两层的深度强化学习框架，其中一个人工智能社会规划者在模拟经济中学习动态税收政策，以平衡平等与生产力，在基线税制上取得显著优势。它还展示了出现的税收设计行为和与人类参与者的有效性。

ABSTRACT

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.

研究动机与目标

• 以数据驱动的仿真为基础，激发在动态经济中设计平衡平等与生产力的税收政策。
• 开发一个两层 RL 框架，其中规划者学习税收政策，而智能体自适应其行为。
• 展示基于 AI 的税收政策相较基线政策在平等–生产力权衡方面的改进。
• 验证出现的行为并与人类参与者测试政策，以评估在现实世界中的可行性。

提出的方法

• 创建一个经济仿真（Gather-and-Build 游戏），包含自适应代理和政府规划者。
• 将税收政策表述为对收入区间应用的学习日程，使用基于可观测世界数据的深度神经网络。
• 将代理的效用建模为金币的凹函数减去线性劳动成本，从而使强化学习优化成为可能。
• 在两层 RL 设置中训练代理和规划者，包括课程化学习和基于熵的正则化以获得稳定性。
• 在平等性和生产力指标上评估政策，并与 Saez framework 和自由市场基线进行比较。

实验结果

研究问题

• RQ1 在动态、学习型经济中，基于 AI 的税收政策是否能改善平等与生产力之间的权衡？
• RQ2 来自 AI 的新兴税收设计在本质上是否与基线政策有显著差异，并且对代理的策略性行为是否具有鲁棒性？
• RQ3 在人类参与者评估平等-生产力权衡时，基于 AI 的税收政策是否有效？

主要发现

• 基于 AI 的税收政策在平等-生产力权衡上比包括 Saez framework 的基线政策提升了 16% 。
• AI 政策设定了质的上不同的时间表，包括更高的最高税率和对低收入者的净补贴更高。
• 即使代理学习避税策略，基于 AI 的政策仍然有效。
• 在人类参与者的 MTurk 实验中，学到的政策显示出有利的平等-生产力结果。
• 政策对 AI 代理出现的策略性行为表现出鲁棒性。