Salesforce's AI Economist Research Wants To Explore The Equilibrium Between Equality And Productivity

Republished By Plato

Followers: 0

The AI Economist is an economic simulation in which AI agents collect and trade resources, build houses, earn income, and pay taxes to a government.

Salesforce

Zheng noted that the research considered many different spatial layouts and distributions of resources, as well as agents with different skill sets or skill levels. He also mentioned that the current work is a proof of concept, focusing on the AI part of the problem.

“The key conceptual issue that we’re addressing is the government trying to optimize this policy, but we can also use AI to model how the economy is going to respond in turn. This is something we call a two-level RL problem.

From that point of view, having ten agents in the economy and the government is already quite challenging to solve. We really have to put a lot of work in to find the algorithm, to find the right mix of learning strategies to actually make the system find these really good tax policy solutions”, Zheng said.

Looking at how people use RL to train systems to play some types of video games or chess, these are already really hard search and optimization problems, even though they utilize just two or ten agents, Zheng added. He claimed that the AI Economist is more efficient than those systems.

The AI Economist team are confident that now that they have a good grasp on the learning part, they are in a great position to think about the future and extend this work also along other dimensions, according to Zheng.

In an earlier version of the AI Economist, the team experimented with having human players participate in the simulation, too. This resulted in more noise, as people behaved in inconsistent ways; according to Zheng, however, the AI Economist still achieved higher quality and productivity levels.

Economics and economists

Some obvious questions as far as this research goes are what do economists think of it and whether their insights were modeled in the system as well. No member of the AI Economist team is actually an economist. However, some economists were consulted, according to Zheng.

“When we first started out, we didn’t have an economist on board, so we partnered with David Parkes, who sits both in computer science and economics. Over the course of the work, we did talk to economists and got their opinions their feedback. We also had an exchange with [economist and best-selling author] Thomas Piketty. He’s a very busy man, so I think he found the work interesting.

He also raised questions about, to some degree, how the policies could be implemented. And you can think of this from many dimensions, but overall he was interested in the work. I think that reflects the broader response from the economic community. There’s both interest and questions on whether this is implementable. What do we need to do this? It’s food for thought for the economics community”, Zheng said.

As for the way forward, Zheng believes it’s “to make this broadly useful and have some positive social impact”. Zheng added that one of the directions the team is headed towards is how to get closer to the real world.

[embedded content]

On the one hand, that means building bigger and better simulations, so they’re more accurate and more realistic. Zheng believes that will be a key component of frameworks for economic modeling and policy design. A big part of that for AI researchers is to prove that you can trust these methods.

“You want to show things like robustness and explainability. We want to tell everyone here are the reasons why the AI recommended this or that policy. Also, I strongly believe in this as an interdisciplinary problem. I think really the opportunity here is for AI researchers to work together with economists, to work together with policy experts in understanding not just the technical dimensions of their problem, but also to understand how that technology can be useful for society”, Zheng said.

Two aspects that Zheng emphasized about this research were goal-setting and transparency. Goal-setting, i.e. what outcomes to optimize for, is done externally. This means that whether the system should optimize for maximum equality, maximum productivity, their equilibrium, or potentially in the future, incorporate other parameters such as sustainability as well is a design choice up to the user.

Zheng described “full transparency” as the cornerstone of the project. If in the future iterations of these types of systems are going to be used for social good, then everyone should be able to inspect, question and critique them, according to Zheng. To serve this goal, the AI Economist team has open-sourced all the code and experimental data based on the research.

Another part of the way forward for the AI Economist team is more outreach to the economist community. “I think there’s a fair bit of education here, where today economists are not trained as computer scientists. They typically are not taught programming in Python, for instance. And things like RL might also not be something that is part of their standard curriculum or their way of thinking. I think that there’s a really big opportunity here for interdisciplinary research,” Zheng said.

The AI Economist team is constantly conversing with economists and presenting this work to the scientific community. Zheng said the team is working on a number of projects, which they will be able to share more about in the near future. He concluded that a bit of education to make people familiar with this approach and more user-friendly UI/UX may go a long way.