# AI-driven Prices for Externalities and Sustainability in Production Markets

arXiv: https://arxiv.org/abs/2106.06060 · HTML: https://mlsanity.com/paper/2106.06060/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 93 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Adversarial Policies: Attacking Deep Reinforcement Learning](https://mlsanity.com/paper/1905.10615/) (2019)
- [A Survey and Critique of Multiagent Deep Reinforcement Learning](https://mlsanity.com/paper/1810.05587/) (2018)
- [Learning to Incentivize Other Learning Agents](https://mlsanity.com/paper/2006.06051/) (2020)
- [RLlib: Abstractions for Distributed Reinforcement Learning](https://mlsanity.com/paper/1712.09381/) (2017)
- [Automated Mechanism Design via Neural Networks](https://mlsanity.com/paper/1805.03382/) (2018)
- [A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms](https://mlsanity.com/paper/1904.06979/) (2019)
- [Econometrics for Learning Agents](https://mlsanity.com/paper/1505.00720/) (2015)
- [Distortion in Social Choice Problems: The First 15 Years and Beyond](https://mlsanity.com/paper/2103.00911/) (2021)
- [Improved Cooperation by Exploiting a Common Signal](https://mlsanity.com/paper/2102.02304/) (2021)
- [Walrasian Dynamics in Multi-unit Markets](https://mlsanity.com/paper/1712.08910/) (2017)
- [Computing Equilibria in Markets with Budget-Additive Utilities](https://mlsanity.com/paper/1603.07210/) (2016)

## Built on by (most cited first, 3 of 3)

- [EconGym: A Scalable AI Testbed with Diverse Economic Tasks](https://mlsanity.com/paper/2506.12110/) (2025)
- [Fair Contracts in Principal-Agent Games with Heterogeneous Types](https://mlsanity.com/paper/2506.15887/) (2025)
- [Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games](https://mlsanity.com/paper/2403.12093/) (2024)

## Abstract (arXiv)

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.