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This paper proposes enhancements to the RICE-N simulation and multi-agentreinforcement learning framework to improve the realism of internationalclimate policy negotiations. Acknowledging the framework's value, we highlightthe necessity of significant enhancements to address the diverse array offactors in modeling climate negotiations. Building upon our previous work onthe "Conditional Commitments Mechanism" (CCF mechanism) we discuss ways tobridge the gap between simulation and reality. We suggest the inclusion of arecommender or planner agent to enhance coordination, address the Real2Sim gapby incorporating social factors and non-party stakeholder sub-agents, andpropose enhancements to the underlying Reinforcement Learning solutionalgorithm. These proposed improvements aim to advance the evaluation andformulation of negotiation protocols for more effective international climatepolicy decision-making in Rice-N. However, further experimentation and testingare required to determine the implications and effectiveness of thesesuggestions.

Improving International Climate Policy via Mutually Conditional Binding Commitments