Exploring Equity of Climate Policies Using Multi-Agent Multi-Objective Reinforcement Learning

Biswas, Palok; Osika, Zuzanna; Tamassia, Isidoro; Whorra, Adit; Zatarain Salazar, Jazmin; Kwakkel, Jan; Oliehoek, Frans A.; Murukannaiah, Pradeep K.

doi:10.24963/ijcai.2025/1064

Exploring Equity of Climate Policies Using Multi-Agent Multi-Objective Reinforcement Learning

Palok Biswas, Zuzanna Osika, Isidoro Tamassia, Adit Whorra, Jazmin Zatarain-Salazar, Jan Kwakkel, Frans A. Oliehoek, Pradeep K. Murukannaiah

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. AI and Social Good. Pages 9573-9581.

https://doi.org/10.24963/ijcai.2025/1064

Keywords

Agent-based and Multi-agent Systems: General
Multidisciplinary Topics and Applications: General

Abstract

Addressing climate change requires coordinated policy efforts of nations worldwide. These efforts are informed by scientific reports, which rely in part on Integrated Assessment Models (IAMs), prominent tools used to assess the economic impacts of climate policies. However, traditional IAMs optimize policies based on a single objective, limiting their ability to capture the trade-offs among economic growth, temperature goals, and climate justice. As a result, policy recommendations have been criticized for perpetuating inequalities, fueling disagreements during policy negotiations. We introduce JUSTICE, the first framework integrating IAM with Multi-Objective Multi-Agent Reinforcement Learning (MOMARL). By incorporating multiple objectives, JUSTICE generates policy recommendations that shed light on equity while balancing climate and economic goals. Further, using multiple agents can provide a realistic representation of the interactions among the diverse policy actors. We identify equitable Pareto-optimal policies using our framework, which facilitates deliberative decision-making by presenting policymakers with the inherent trade-offs in climate and economic policy.

Palok Biswas, Zuzanna Osika, Isidoro Tamassia, Adit Whorra, Jazmin Zatarain Salazar, Jan Kwakkel, Frans A. Oliehoek, and Pradeep K. Murukannaiah. 2025. Exploring Equity of Climate Policies Using Multi-Agent Multi-Objective Reinforcement Learning. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 9573–9581. https://doi.org/10.24963/ijcai.2025/1064

@inproceedings{ijcai2025p1064,
  title     = {Exploring Equity of Climate Policies Using Multi-Agent Multi-Objective Reinforcement Learning},
  author    = {Biswas, Palok and Osika, Zuzanna and Tamassia, Isidoro and Whorra, Adit and Zatarain Salazar, Jazmin and Kwakkel, Jan and Oliehoek, Frans A. and Murukannaiah, Pradeep K.},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {9573--9581},
  year      = {2025},
  month     = {8},
  note      = {AI and Social Good},
  doi       = {10.24963/ijcai.2025/1064},
  url       = {https://doi.org/10.24963/ijcai.2025/1064},
}

TY  - CPAPER
TI  - Exploring Equity of Climate Policies Using Multi-Agent Multi-Objective Reinforcement Learning
AU  - Biswas, Palok
AU  - Osika, Zuzanna
AU  - Tamassia, Isidoro
AU  - Whorra, Adit
AU  - Zatarain Salazar, Jazmin
AU  - Kwakkel, Jan
AU  - Oliehoek, Frans A.
AU  - Murukannaiah, Pradeep K.
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 9573
EP  - 9581
DO  - 10.24963/ijcai.2025/1064
UR  - https://doi.org/10.24963/ijcai.2025/1064
ER  -