# AI4GCC -- Track 3: Consumption and the Challenges of Multi-Agent RL

arXiv: https://arxiv.org/abs/2308.05260 · HTML: https://mlsanity.com/paper/2308.05260/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [Learning with Opponent-Learning Awareness](https://mlsanity.com/paper/1709.04326/) (2017)
- [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://mlsanity.com/paper/2004.13332/) (2020)
- [Maintaining cooperation in complex social dilemmas using deep reinforcement learning](https://mlsanity.com/paper/1707.01068/) (2017)
- [AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N](https://mlsanity.com/paper/2208.07004/) (2022)

## Abstract (arXiv)

The AI4GCC competition presents a bold step forward in the direction of integrating machine learning with traditional economic policy analysis. Below, we highlight two potential areas for improvement that could enhance the competition's ability to identify and evaluate proposed negotiation protocols. Firstly, we suggest the inclusion of an additional index that accounts for consumption/utility as part of the evaluation criteria. Secondly, we recommend further investigation into the learning dynamics of agents in the simulator and the game theoretic properties of outcomes from proposed negotiation protocols. We hope that these suggestions can be of use for future iterations of the competition/simulation.