Improving International Climate Policy via Mutually Conditional Binding Commitments

2023-07-26 · Jobst Heitzig, Jörg Oechssler, Christoph Pröschel, Niranjana Ragavan, Richie YatLong Lo

This paper proposes enhancements to the RICE-N simulation and multi-agent reinforcement learning framework to improve the realism of international climate policy negotiations. Acknowledging the framework's value, we highlight the necessity of significant enhancements to address the diverse array of factors in modeling climate negotiations. Building upon our previous work on the "Conditional Commitments Mechanism" (CCF mechanism) we discuss ways to bridge the gap between simulation and reality. We suggest the inclusion of a recommender or planner agent to enhance coordination, address the Real2Sim gap by incorporating social factors and non-party stakeholder sub-agents, and propose enhancements to the underlying Reinforcement Learning solution algorithm. These proposed improvements aim to advance the evaluation and formulation of negotiation protocols for more effective international climate policy decision-making in Rice-N. However, further experimentation and testing are required to determine the implications and effectiveness of these suggestions.

📄 PDF Abstract BibTeX arXiv:2307.14266

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Decision Making Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning