Utilizing Multi-agent Geopolitical Forecasting in Language Modeling

• In Event: Poster Session: Posters in Political Methodology

Abstract

This paper involves the simulation of a dialogic interplay among distinct AI agents, akin to states engaged in discussions, debates, and negotiations. These agents are equipped with different perspectives, biases, and information, mirroring the diversity of states in the real world. By fostering collaboration and debate among these agents within a defined number of rounds, we aim to enhance the reasoning abilities and predictive accuracy of Language Models (LLMs). The objective is to encourage these AI agents to iterate through a series of discussions, allowing for a convergence towards a collective, compromise-based prediction. Each agent represents a particular viewpoint or analytical approach, thereby contributing to a comprehensive exploration of potential geopolitical scenarios. This approach fosters a more informed decision-making process by amalgamating varied insights and reducing the influence of individual biases.

Through this innovative strategy, I introduce a framework that not only elevates the performance of LLMs but also promotes transparency and accountability in predictive modeling. Moreover, it facilitates a grounded, data-driven approach to political forecasting by assimilating multiple perspectives and promoting a collective intelligence that mimics the dynamics of real-world geopolitical interactions. This paper uses the Russia-Ukraine war as a case study.

Author

• Kirill Kalinin, Stanford University