When building LLM systems for real-world decisions involving multiple stakeholders with conflicting interests, use iterative game-theoretic reasoning rather than one-shot reasoning—agents improve by learning from and countering each other's strategies.
This paper introduces Multi-Agent Fictitious Play (MAFP), a framework where LLM-based agents represent different stakeholders and iteratively improve decisions by responding to each other's past choices.