  Comprehensive global cooperation is essential to limit global temperature
increases while continuing economic development, e.g., reducing severe
inequality or achieving long-term economic growth. Achieving long-term
cooperation on climate change mitigation with n strategic agents poses a
complex game-theoretic problem. For example, agents may negotiate and reach
climate agreements, but there is no central authority to enforce adherence to
those agreements. Hence, it is critical to design negotiation and agreement
frameworks that foster cooperation, allow all agents to meet their individual
policy objectives, and incentivize long-term adherence. This is an
interdisciplinary challenge that calls for collaboration between researchers in
machine learning, economics, climate science, law, policy, ethics, and other
fields. In particular, we argue that machine learning is a critical tool to
address the complexity of this domain. To facilitate this research, here we
introduce RICE-N, a multi-region integrated assessment model that simulates the
global climate and economy, and which can be used to design and evaluate the
strategic outcomes for different negotiation and agreement frameworks. We also
describe how to use multi-agent reinforcement learning to train rational agents
using RICE-N. This framework underpinsAI for Global Climate Cooperation, a
working group collaboration and competition on climate negotiation and
agreement design. Here, we invite the scientific community to design and
evaluate their solutions using RICE-N, machine learning, economic intuition,
and other domain knowledge. More information can be found on
www.ai4climatecoop.org.
