AI-driven Prices for Externalities and Sustainability in Production Markets
This addresses sustainability and fairness issues in production markets for policymakers and economists, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of negative externalities in competitive markets by proposing a deep reinforcement learning policymaker agent to compute market prices and allocations, achieving significantly better resource sustainability compared to market equilibrium outcomes in scarce environments.
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.