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。

AI4GCC - 第三轨道：消费和多智能体强化学习的挑战

AI4GCC -- Track 3: Consumption and the Challenges of Multi-Agent RL

We examine properties of a model of resource allocation in which several
agents exchange resources in order to optimise their individual holdings. The
schemes discussed relate to well-known negotiation protocols proposed in
earlier work and we consider a number of alternative notions of rationality
covering both quantitative measures, e.g. cooperative and individual
rationality and more qualitative forms, e.g. Pigou-Dalton transfers. While it
is known that imposing particular rationality and structural restrictions may
result in some reallocations of the resource set becoming unrealisable, in this
paper we address the issue of the number of restricted rational deals that may
be required to implement a particular reallocation when it is possible to do
so. We construct examples showing that this number may be exponential (in the
number of resources m), even when all of the agent utility functions are
monotonic. We further show that k agents may achieve in a single deal a
reallocation requiring exponentially many rational deals if at most k-1 agents
can participate, this same reallocation being unrealisable by any sequences of
rational deals in which at most k-2 agents are involved.

考虑到资源分配的实现，本文探讨了多个代理通过资源交换来优化其个体持有的模型，并利用合作和个体的理性以及 Pigou-Dalton 转移等多种形式对合理性概念作出了不同的定义，其中我们发现，对理性和结构方面的某些限制，可能会难以实现某些资源的重新分配，并构建了一些例子说明了这种策略的数量可能呈指数级增长。