Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-pool resource (which diminishes future stock, and thus harvest, for everyone). Quantifying appropriate interventions to market prices has proven to be quite challenging. We propose a practical approach to computing market prices and allocations via a deep reinforcement learning policymaker agent, operating in an environment of other learning agents. Our policymaker allows us to tune the prices with regard to diverse objectives such as sustainability and resource wastefulness, fairness, buyers' and sellers' welfare, etc. As a highlight of our findings, our policymaker is significantly more successful in maintaining resource sustainability, compared to the market equilibrium outcome, in scarce resource environments.
翻译:传统竞争市场不考虑消极的外部效应;一些参与者对其他人造成的间接成本,例如过度挪用共同资源(减少未来股票,进而对每个人收获)的成本。 事实证明,对市场价格进行适当干预是相当具有挑战性的。 我们提议了一种切实可行的方法,通过一个深厚的学习决策者代理人,在其他学习代理人的环境中运作,计算市场价格和分配。 我们的决策者允许我们调整价格,以实现多种目标,如可持续性和资源浪费、公平、买主和卖主的福利等。 作为我们调查结果的突出,我们的决策者在稀缺的资源环境中维持资源可持续性比市场平衡结果要成功得多。