AI4GCC -- Track 3: Consumption and the Challenges of Multi-Agent RL
This provides incremental suggestions for a competition focused on integrating machine learning with economic policy analysis.
The paper identifies two areas for improving the AI4GCC competition's evaluation of negotiation protocols: adding a consumption/utility index and investigating agent learning dynamics and game-theoretic outcomes.
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.