The AI4GCC competition presents a bold step forward in the direction of
integrating machine learning with traditional economic policy analysis. Below,
we highlight two potential areas for improvement that could enhance the
competition's ability to identify and evaluate proposed negotiation protocols.
Firstly, we suggest the inclusion of an additional index that accounts for
consumption/utility as part of the evaluation criteria. Secondly, we recommend
further investigation into the learning dynamics of agents in the simulator and
the game theoretic properties of outcomes from proposed negotiation protocols.
We hope that these suggestions can be of use for future iterations of the
competition/simulation.

AI4GCC 竞赛在将机器学习与传统经济政策分析相结合方面迈出了大胆的一步。我们提出了两个改进的潜在方向，可以提高竞赛识别和评估提议的谈判协议的能力。首先，我们建议包括一个额外的指标，用于评估消费 / 效用。其次，我们建议进一步调查模拟器中代理人的学习动态以及提议的谈判协议结果的博弈论属性。我们希望这些建议对未来的竞赛 / 模拟有所帮助。