Multi-Agent Strategic Games with LLMs
For researchers in international relations and AI, this provides a scalable, transparent method to probe strategic mechanisms, though the contribution is primarily methodological.
This paper uses LLMs as experimental subjects in a repeated security dilemma to test whether they reproduce canonical mechanisms from international relations theory. Results show that multipolarity increases conflict, finite horizons cause unraveling, and communication reduces conflict.
This paper asks whether large language models (LLMs) can be used to study the strategic foundations of conflict and cooperation. I introduce LLMs as experimental subjects in a repeated security dilemma and evaluate whether they reproduce canonical mechanisms from international relations theory. The baseline game is extended along three theoretically central dimensions: multipolarity, finite time horizons, and the availability of communication. Across multiple models, the results exhibit systematic and consistent patterns: multipolarity increases the likelihood of conflict, finite horizons induce universal unraveling consistent with backward-induction logic, and communication reduces conflict by enabling signaling and reciprocity. Beyond observed behavior, the design provides access to agents' private reasoning and public messages, allowing choices to be linked to underlying strategic logics such as preemption, cooperation under uncertainty, and trust-building. The contribution is primarily methodological. LLM-based experiments offer a scalable, transparent, and replicable approach to probing theoretical mechanisms.