AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N

21 Pages Posted: 19 Aug 2022

See all articles by Tianyu Zhang

Tianyu Zhang

Mila - Quebec AI Institute; University of Montreal - Department of Computer Science and Operations Research

Andrew Williams

University of Montreal - Department of Computer Science and Operations Research; Mila - Quebec AI institute

Soham Phade

Salesforce Research

Sunil Srinivasa

Salesforce

Yang Zhang

Mila - Quebec AI Institute

Prateek Gupta

University of Oxford - Department of Engineering Science

Yoshua Bengio

University of Montreal - Department of Computer Science and Operations Research

Stephan Zheng

Salesforce

Date Written: August 14, 2022

Abstract

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 underpins AI 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.

Keywords: Climate change; Climate agreements; Negotiation; Simulation; Machine learning; Game theory; Reinforcement learning; Agent-based model

JEL Classification: Q54, Q01, Q50, C00, C30

Suggested Citation

Zhang, Tianyu and Williams, Andrew and Phade, Soham and Srinivasa, Sunil and Zhang, Yang and Gupta, Prateek and Bengio, Yoshua and Zheng, Stephan, AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N (August 14, 2022). Available at SSRN: https://ssrn.com/abstract=4189735 or http://dx.doi.org/10.2139/ssrn.4189735

Tianyu Zhang

Mila - Quebec AI Institute ( email )

6666 St-Urbain
#200
Montreal, Quebec H2S 3H1
Canada

HOME PAGE: http://mila.quebec/en/person/tianyu-zhang/

University of Montreal - Department of Computer Science and Operations Research ( email )

Pavillon André-Aisenstadt
2920, chemin de la Tour
Montreal, Quebec H3T 1J4
Canada

Andrew Williams

University of Montreal - Department of Computer Science and Operations Research ( email )

C.P. 6128 succursale Centre-ville
Montreal, Quebec H3C 3J7
Canada

Mila - Quebec AI institute

Montreal
Canada

Soham Phade

Salesforce Research ( email )

United States

Sunil Srinivasa

Salesforce ( email )

United States

Yang Zhang

Mila - Quebec AI Institute ( email )

Quebec
Canada

Prateek Gupta

University of Oxford - Department of Engineering Science ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

Yoshua Bengio

University of Montreal - Department of Computer Science and Operations Research ( email )

CP 6128, Succ. Centre-Ville
2920 Chemin de la tour
Montreal H3C 3J7, Quebec
Canada
514-343-6804 (Phone)
514-343-5834 (Fax)

Stephan Zheng (Contact Author)

Salesforce ( email )

United States

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