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

Palok Biswas, Zuzanna Osika, Isidoro Tamassia, Adit Whorra, Jazmin Zatarain Salazar, Jan H. Kwakkel, Frans A. Oliehoek, and Pradeep K. Murukannaiah. 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), pp. 9573–9581, International Joint Conferences on Artificial Intelligence Organization, 8 2025. AI and Social Good

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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.

BibTeX Entry

@inproceedings{Biswas25IJCAI,
    title =     {Exploring Equity of Climate Policies using Multi-Agent Multi-Objective Reinforcement Learning},
    author =    {Palok Biswas and Zuzanna Osika and Isidoro Tamassia and
                 Adit Whorra and Jazmin Zatarain Salazar and Jan H. Kwakkel 
                 and Frans A. Oliehoek and Pradeep K. Murukannaiah},
    booktitle = IJCAI25,
    year =      2025,
    publisher = {International Joint Conferences on Artificial Intelligence Organization},
    OPTeditor    = {James Kwok},
    pages     = {9573--9581},
    month     = {8},
    note      = {AI and Social Good},
    doi       = {10.24963/ijcai.2025/1064},
    url       = {https://doi.org/10.24963/ijcai.2025/1064},    
    keywords =  {refereed},
    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.}
}

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