Can AI design fairer rules for sharing limited resources?

When people share resources in networks—think community fisheries or water systems—simple rules like equal splits fail because they kill incentives, while proportional ones entrench inequality. Researchers used reinforcement learning to train an AI planner that allocates resources based on local conditions and network position, achieving higher cooperation and lower inequality than standard mechanisms. The learned strategy distills into two practical rules adapted to network structure.