Researchers have proposed a new approach to computing market prices and allocations that takes into account negative externalities such as the cost of over-appropriating a common-pool resource. They use a deep reinforcement learning policymaker agent that operates in an environment with other learning agents, allowing for tuning of prices based on diverse objectives like sustainability and fairness. The study found that this approach is more successful than traditional market equilibrium outcomes in maintaining resource sustainability, especially in scarce environments. --- Why it matters: This work matters to AI researchers because it demonstrates a practical application of deep reinforcement learning to real-world economic problems, showing how AI can be used to improve sustainability and fairness in production markets. The proposed policymaker agent is a significant advancement in this area, with potential applications beyond the specific context studied here. Source: https://arxiv.org/abs/2106.06060