用多智能体强化学习让气候政策更公平，兼顾减排与经济平衡
Exploring Equity of Climate Policies using Multi-Agent Multi-Objective Reinforcement Learning
- 引入多目标多智能体强化学习框架，模拟各国政策互动
- 找到兼顾气候目标与经济公平的帕累托最优政策组合
- 适合关注气候正义与国际政策协调的研究者
应对气候变化需要全球各国协同制定政策。现有政策评估依赖集成评估模型（IAMs），但传统IAMs仅优化单一目标，难以反映经济增长、温控目标与气候公平之间的权衡，导致政策建议被批评为加剧不平等，引发谈判分歧。本文提出首个融合IAM与多目标多智能体强化学习（MOMARL）的框架——Justice。该框架通过多目标优化，生成兼顾气候与经济公平的政策建议；多智能体设计更真实地模拟不同政策主体间的互动。我们基于此框架识别出若干公平的帕累托最优政策，帮助决策者理解气候与经济政策间的内在权衡，支持更具协商性的决策过程。
原文摘要 · Abstract (English)
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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