Tax policy is surely a complex beast, and depending on your political leanings, you probably have some strong feelings about how it should be implemented. Salesforce AI researchers are trying to build a model to bring artificial intelligence to bear on what will undoubtedly always be a highly political process.

Richard Socher, who heads up AI research at Salesforce, says the company is researching all kinds of solutions related to AI and business, and how it could improve the Salesforce product family; however, he also looks at how his team could use AI to solve a set of broader social issues beyond what it can do for the product line.

Socher says when you look at the biggest issues of our time, one of the largest is economic inequality, and how we could use policy to solve that. To that end, the company created a model it calls an AI economist that could look at various economic variables, a broad set of economic models and using the power of AI begin to demonstrate how various policies affect economic inequality versus productivity.

“We are using reinforcement learning to try to identify what’s optimal taxation,” Socher said. That involves building a model, one that’s fairly simple at first with some basic economic inputs like buying and selling resources and building houses to see how different scenarios affect inequality.

In a Q&A on the company website, Stephan Zheng, a member of the research team explained how this could work:

The AI Economist uses a collection of AI agents designed to simulate how real people might react to different taxes. In the simulation, each AI agent earns money by collecting and trading resources and building houses. The agents learn to maximize their utility (or happiness) by adjusting their movement, trading, and building behavior. One way to do this is to maximize income while minimizing effort, for example, making as high of an hourly wage as possible.

The modeling is designed to play to the strength of AI, by looking across a huge body of economic research and feeding all of that data into the AI economist to help build optimal models. This level of data would be impossible for even the most gifted economists to understand, but this is what AI is really good at, looking across a corpus of complex data and using all of that information to help humans make better decisions.

Ultimately, the company hopes the model can help economists and policy makers set a more equitable tax system, however an individual government might define that.

“The objective that we chose here was productivity x equality, and we hope that that will help move the pure shareholder capitalism to a more equal stakeholder capitalism — and we hope that we find a more optimal point on the quality versus productivity spectrum,” Socher explained.

While Socher admits this is an early attempt, and they hope to layer on more complex inputs over time, he likens it to early genome research. It didn’t produce concrete results right away, but over time we have seen tools like CRISPR develop, and he hopes that this approach could have a similar impact on tax policy as they build on their initial research.

“We think we found a point, at least in our first simulated environments that is even more optimal than the most commonly used baselines for taxation,” he said.