What an AI-Driven Tax Policy Would Look Like?
By: Lina Faik
Tags: Reinforcement Learning, Data Science, Taxes, Economics, Editors Pick

What an AI-Driven Tax Policy Would Look Like?

Discover what Reinforcement Learning can bring to Tax Policy Design

Inequality is one of the greatest social and economic concerns of our time. It has dramatically increased over the last decades and has become deeply entrenched in our societies.

Today, the figures are alarming: according to the OECD, the average income of the richest 10% of the population is about 9x that of the poorest 10% across the OECD. This is 7x higher than 25 years ago! The situation is even more worrying in the United State where the richest 1 percent concentrate more on national wealth and income than in any country.

Faced with this situation, what actions should countries be taking to ensure a fair redistribution of wealth?

As Amitabh Behar, the CEO of Oxfam India, puts it:

“The gap between rich and poor can’t be resolved without deliberate inequality-busting policies”.

Therefore, despite the critics, tax policy is an indispensable tool for governments. It enables them to redistribute wealth through the services and the benefits they provide and thus reduce inequality.

And yet, finding the optimal tax policy can be very challenging. Why?

The main reason is that it has found a comprise of two opposing objectives: equality and productivity. In order words, tax policies must improve equality without undermining people's willingness to work, which would lead to lower productivity.

Even with these two objectives in mind, it is still hard to determine the most effective tax policy, as it is difficult to carry out real-life experiments. Therefore, it is common to rely on economic theory. But the latter often makes simplifying assumptions that are hard to validate.

So, how can Artificial Intelligence be used for tax policy design?

AI and more precisely the field of Reinforcement Learning provides tools to simulate an artificial economy and observe and explore quantitatively the consequences of different policy scenarios.

It consists of modeling multiple agents. These agents are immersed in a certain environment, which is here a simulated economy with a specific tax policy. For every action they choose to take, they receive rewards or penalties. After many iterations, they eventually learn the optimal decision-making strategy that will allow them to maximize the rewards accumulated over time.

By observing the behaviors of these agents, it is possible to quantitative measure the efficiency of the tax policy both in terms of agents’ productivity and overall equality.

How can it be implemented in practice?

This is exactly what you will discover in my article.

To go beyond the conceptual framework, I have simulated a simple world in which economic actors interact with each other according to the fiscal constraints set by a social planner. Both the economic actors and the government learn and adapt progressively. They do not use any economic modeling and rely only on observational data.

In this article, I will describe the methodology I used, and I will share with you my key findings.

You will learn:

How to build an economic environment where multiple agents interact under market competitive pressures and dynamics?

where multiple agents interact under market competitive pressures and dynamics? How to measure the effectiveness of a tax policy ?

? What an AI-driven tax policy looks like and what improvement does it bring over common tax systems?

This work is inspired by the research paper The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies written by S. Zheng & al. in April 2020[1].

Environment Description

From the Agents’ Perspective

The environment consists of a 2-dimensional grid word where agents are free to move, collect resources (stone or wood), and build houses at each step of an episode. They can also trade the resources they gather for a pre-defined price.