AI-driven Prices for Externalities and Sustainability in Production Markets

TL;DR

This paper introduces a deep reinforcement learning approach for setting prices in markets with externalities, aiming to improve sustainability and fairness over traditional equilibrium methods.

Contribution

It presents a novel RL-based policymaker that dynamically adjusts prices considering externalities, outperforming traditional market outcomes in resource sustainability.

Findings

Enhanced resource sustainability with RL policymaker

Better alignment of prices with externalities

Improved fairness and welfare balance

Abstract

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.