An AI can simulate an economy millions of times to create fairer tax policy

Income inequality is one of the overarching problems of economics. One of the most effective tools policymakers have to address it is taxation: governments collect money from people according to what they earn and redistribute it either directly, via welfare schemes, or indirectly, by using it to pay for public projects. But though more taxation can lead to greater equality, taxing people too much can discourage them from working or motivate them to find ways to avoid paying—which reduces the overall pot.

Getting the balance right is not easy. Economists typically rely on assumptions that are hard to validate. People’s economic behavior is complex, and gathering data about it is hard. Decades of economic research has wrestled with designing the best tax policy, but it remains an open problem. 

Scientists at the US business technology company Salesforce think AI can help. Led by Richard Socher, the team has developed a system called the AI Economist that uses reinforcement learning—the same sort of technique behind DeepMind’s AlphaGo and AlpahZero—to identify optimal tax policies for a simulated economy. The tool is still relatively simple (there’s no way it could include all the complexities of the real world or human behavior), but it is a promising first step toward evaluating policies in an entirely new way. “It would be amazing to make tax policy less political and more data driven,” says team member Alex Trott.

In one early result, the AI found a policy that—in terms of maximizing both productivity and income equality—was 16% fairer than a state-of-the-art progressive tax framework studied by academic economists. The improvement over current US policy was even greater. “I think it’s a totally interesting idea,” says Blake LeBaron at Brandeis University in Massachusetts, who has used neural networks to model financial markets. 

In the simulation, four AI workers are each controlled by their own reinforcement-learning models. They interact with a two-dimensional world, gathering wood and stone and either trading these resources with others or using them to build houses, which earns them money. The workers have different levels of skill, which leads them to specialize. Lower-skilled workers learn they do better if they gather resources, and higher-skilled ones learn they do better if they buy resources to build houses. At the end of each simulated year, all workers are taxed at a rate devised by an AI-controlled policymaker, which is running its own reinforcement-learning algorithm. The policymaker’s goal is to boost both the productivity and the income of all workers. The AIs converge on optimal behavior by repeating the simulation millions of times.

Both reinforcement-learning models start from scratch, with no prior knowledge of economic theory, and learn how to act through trial and error—in much the same way that DeepMind’s AIs learn, with no human input, to play Go and StarCraft at superhuman levels. 

Can you learn much from only four AI workers? In theory, yes, because simple interactions between a handful of agents soon lead to very complex behaviors. (For all its complexity, Go still involves only two players, for example.) Even so, everyone involved in the project agrees that increasing the number of workers in the simulation

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