# AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N arXiv: https://arxiv.org/abs/2208.07004 ยท HTML: https://mlsanity.com/paper/2208.07004/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Builds on 12 of its 57 references that are in the map. - First posted on arXiv 15 Aug 2022. ## Builds on (most cited first, 12 of 12) - [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017) - [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019) - [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021) - [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019) - [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022) - [Emergent Communication through Negotiation](https://mlsanity.com/paper/1804.03980/) (2018) - [Maintaining cooperation in complex social dilemmas using deep reinforcement learning](https://mlsanity.com/paper/1707.01068/) (2017) - [No Press Diplomacy: Modeling Multi-Agent Gameplay](https://mlsanity.com/paper/1909.02128/) (2019) - [Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings](https://mlsanity.com/paper/2006.14156/) (2020) - [Blockchain Technology as a Regulatory Technology: From Code is Law to Law is Code](https://mlsanity.com/paper/1801.02507/) (2018) - [Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective](https://mlsanity.com/paper/2106.05402/) (2021) - [Feedback, Dynamics, and Optimal Control in Climate Economics](https://mlsanity.com/paper/1812.08066/) (2018) ## Built on by (most cited first, 22 of 22) - [Advantage Alignment Algorithms](https://mlsanity.com/paper/2406.14662/) (2024) - [Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery](https://mlsanity.com/paper/2505.16477/) (2025) - [Crafting Desirable Climate Trajectories with RL Explored Socio-Environmental Simulations](https://mlsanity.com/paper/2410.07287/) (2024) - [InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma](https://mlsanity.com/paper/2411.09856/) (2024) - [Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis](https://mlsanity.com/paper/2504.12777/) (2025) - [Incentives to Build Houses, Trade Houses, or Trade House Building Skills in Simulated Worlds under Various Governing Systems or Institutions: Comparing Multi-agent Reinforcement Learning to Generative Agent-based Model](https://mlsanity.com/paper/2411.17724/) (2024) - [Exploring Equity of Climate Policies using Multi-Agent Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2505.01115/) (2025) - [A Benchmark for Multi-Party Negotiation Games from Real Negotiation Data](https://mlsanity.com/paper/2603.14066/) (2026) - [Improving International Climate Policy via Mutually Conditional Binding Commitments](https://mlsanity.com/paper/2307.14267/) (2023) - [EconoJax: A Fast & Scalable Economic Simulation in Jax](https://mlsanity.com/paper/2410.22165/) (2024) - [Altruism and Fair Objective in Mixed-Motive Markov games](https://mlsanity.com/paper/2602.08389/) (2026) - [Dynamic Grouping for Climate Change Negotiation: Facilitating Cooperation and Balancing Interests through Effective Strategies](https://mlsanity.com/paper/2307.13886/) (2023) - [AI4GCC-Team -- Below Sea Level: Score and Real World Relevance](https://mlsanity.com/paper/2307.13892/) (2023) - [Dynamic Grouping for Climate Change Negotiation: Facilitating Cooperation and Balancing Interests through Effective Strategies](https://mlsanity.com/paper/2307.13893/) (2023) - [AI4GCC - Team: Below Sea Level: Critiques and Improvements](https://mlsanity.com/paper/2307.13894/) (2023) - [Explore the possibility of advancing climate negotiations on the basis of regional trade organizations: A study based on RICE-N](https://mlsanity.com/paper/2307.14226/) (2023) - [AI4GCC -- Track 3: Consumption and the Challenges of Multi-Agent RL](https://mlsanity.com/paper/2308.05260/) (2023) - [Artificial Utopia: Simulation and Intelligent Agents for a Democratised Future](https://mlsanity.com/paper/2503.07364/) (2025) - [Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm](https://mlsanity.com/paper/2503.18816/) (2025) - [AI-Driven Climate Policy Scenario Generation for Sub-Saharan Africa](https://mlsanity.com/paper/2505.18694/) (2025) - [Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM](https://mlsanity.com/paper/2510.07971/) (2025) - [Eating for a Sustainable Planet: Personalized Sustainable Diet Recommendation via Constraint-Aware Decision-Making Modeling](https://mlsanity.com/paper/2608.24274/) (2026) ## Abstract (arXiv) 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.