The moonshot goal of this project is to build a reinforcement learning framework that will recommend economic policies that drive social outcomes in the real world, such as improving sustainability, productivity, and equality. To achieve this, we’ll need to advance AI, challenge conventional economic thinking, and create AI that can ground and guide policy making. While none of these tasks are easy, together, they make for a true moonshot.
This moonshot is both ambitious and necessary, and more timely than ever given economic challenges around the world. Importantly, the AI Economist is a powerful optimization framework that can objectively automate policy design and evaluation. This will allow economists and policy experts to focus on the end goal of improving social welfare.
Given the social and ethical implications that economic policies can have, we believe it is essential to have transparency in the process. By open sourcing the AI Economist, not only do we empower collaboration from all over the world but we also enable unfettered review of policy simulations.
The key ingredients are:
A high-fidelity simulation that should be grounded in data, and aligned with economic theory as well as with social and ethical values. Simulations should not be prohibitively expensive to run, and should be maintainable and modular.
AI policy models should be effective in a wide range of scenarios, explainable, and robust to economic shocks.
The simulation and policy models should be calibrated against real-world data and, as much as possible, validated in human-subject studies.
I suppose I am skeptical, but fortunately progress does not depend on pleasing me. There is much more information at the original link.
For the pointer I thank Mike Doherty.