Exploring Global Climate Cooperation through AI: An Assessment of the AI4GCC Framework by simulations

Xavier Marjou, Arnaud Braud, Gaël Fromentoux
3/31/2023
cs.MA

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Abstract

In scenarios where a single player cannot control other players, cooperative AI is a recent technology that takes advantage of deep learning to assess whether cooperation might occur. One main difficulty of this approach is that it requires a certain level of consensus on the protocol (actions and rules), at least from a majority of players. In our work, we study the simulations performed on the cooperative AI tool proposed in the context of AI for Global Climate Cooperation (AI4GCC) competition. We experimented simulations with and without the AI4GCC default negotiation, including with regions configured slightly differently in terms of labor and/or technology growth. These first results showed that the AI4GCC framework offers a promising cooperative framework to experiment with global warming mitigation. We also propose future work to strengthen this framework.

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Cite this paper

@article{marjou2023exploring,
  title  = {Exploring Global Climate Cooperation through AI: An Assessment of the AI4GCC Framework by simulations},
  author = {Xavier Marjou and Arnaud Braud and Gaël Fromentoux},
  year   = {2023},
  eprint = {2303.17990},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.17990v1}
}

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