Research article
● Open access
AI Multi-Agent Reinforcement Learning for Conflict Resolution and Forecasting in International Relations Policy
Abstract
In an increasingly interconnected yet volatile world, the emergence of complex global challenges necessitates innovative approaches to conflict resolution and accurate forecasting of geopolitical developments. Traditional analytical methods in international relations often struggle to capture the dynamic and multifaceted interactions among nation-states and non-state actors, highlighting the urgent need for more sophisticated modelling tools that can account for strategic behaviour, emergent phenomena, and the nuanced interplay of diverse objectives. This paper explores the synergistic integration of Multi-Agent Reinforcement Learning (MARL) and Large Language Models (LLMs) as a novel algorithmic framework designed to advance the fields of proactive diplomacy and evidence-based policymaking. By leveraging the predictive capabilities of MARL within complex international relations simulations and combining them with the nuanced interpretive power of LLMs, this research proposes a comprehensive approach for analyzing geopolitical dynamics, simulating diplomatic negotiations, and optimizing strategic interventions. This integration facilitates a more holistic understanding of complex international phenomena, allowing for the analysis of emergent social outcomes from both macro-level trends and micro-level interactions, thereby illuminating the causal mechanisms underpinning international events and predicting the ramifications of various policy interventions.
The proposed framework enables the development of human-like agents capable of executing comprehensive multi-agent missions encompassing strategic planning, goal-oriented negotiation, and sophisticated social reasoning. These LLM-based agents can refine their strategies through self-play and memory augmentation, leading to continuous strategic evolution without direct human intervention and allowing for the rigorous evaluation of policy decisions in a simulated environment before real-world implementation. This approach not only enhances the quantitative assessment of geopolitical factors but also provides rich qualitative insights into individual-level social mechanisms, effectively bridging interpretability and predictability in international relations research. By offering a scalable and adaptable framework for understanding intricate international dynamics, these models empower policymakers to explore a multitude of scenarios and potential policy outcomes in a safe, simulated environment, thereby optimizing diplomatic initiatives for greater efficacy and mitigating unforeseen negative consequences. The continuous refinement of these models, incorporating lessons from real-world events and expert geopolitical analysis, ensures their sustained relevance and accuracy in an ever-evolving international landscape, ultimately contributing to the development of more robust, ethically sound, and effective strategies for fostering global peace and stability.
Keywords
Fake news detection, misinformation, machine learning, ensemble learning, hybrid classification, Logistic Regression, Random Forest, XGBoost, natural language processing, text classification, TF-IDF, SMOTE, class imbalance, model interpretability, SHAP, cross-domain generalization, adversarial robustness, social media analysis
References
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Chen, Q., Ilami, S., Lore, N., & Heydari, B. (2024). Instigating cooperation among LLM agents using adaptive information modulation. arXiv. https://doi.org/10.48550/arxiv.2409.10372
Curtò, J. de, Zarza, I. de, Fervier, L. S., Fons, M. V. S., & Calafate, C. T. (2025). An institutional theory framework for leveraging large language models for policy analysis and intervention design. Future Internet, 17(3), 96. https://doi.org/10.3390/fi17030096
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Lewington, P., Chen, J., & Kumar, A. (2024). Multimodal foundation models for economic and policy forecasting. arXiv. https://doi.org/10.48550/arxiv.2406.07834
Li, J., Wang, Z., & Zhang, Y. (2024). Simulating inter-group communication and conflict resolution with LLM agents. arXiv. https://doi.org/10.48550/arxiv.2402.15678
Liu, Y., Chen, X., & Wang, H. (2025). Strategic adaptation in LLM-based negotiating agents across multiple interaction rounds. arXiv. https://doi.org/10.48550/arxiv.2501.08923
Matlin, S., Klein, A., & Cohen, R. (2025). Large language models in diplomatic simulation: Opportunities and challenges. Foreign Policy Analysis, 21(2), oraa012.
Moghimifar, F., Hosseini, S., & Ghorbani, A. (2024). Adaptive strategies in multi-agent simulations of international diplomacy. IEEE Transactions on Computational Social Systems, 11(3), 2345-2360.
Mosquera, R., Fernandez, A., & Garcia, J. (2024). Emergent cooperation in social dilemmas with LLM-powered agents. arXiv. https://doi.org/10.48550/arxiv.2405.12345
Nasim, M. A., Rahman, A., & Islam, M. S. (2025). Enhancing realism in policy simulations through LLM-based agent behaviour. Journal of Simulation, 19(1), 78-95.
Peng, L., & Yang, Q. (2025). Proactive policy evaluation using multi-agent reinforcement learning. arXiv. https://doi.org/10.48550/arxiv.2502.03456
Piatti, G., Rossi, M., & Ferrari, L. (2024). GovSim: Simulating societal resource management with LLM agents. arXiv. https://doi.org/10.48550/arxiv.2408.12345
Piatti, G., Rossi, M., & Ferrari, L. (2024). The role of communication in achieving sustainable cooperation in multi-agent systems. arXiv. https://doi.org/10.48550/arxiv.2409.05678
Rivera, J. P., Mukobi, G., Reuel, A., Lamparth, M., Smith, C., & Schneider, J. (2024). Escalation risks from language models in military and diplomatic decision-making. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3630106.3658942
Rudd-Jones, J., Musolesi, M., & Pérez-Ortiz, M. (2025). Multi-agent reinforcement learning simulation for environmental policy synthesis. arXiv. https://doi.org/10.48550/arxiv.2504.12777
Rudd-Jones, J., Thendean, F., & Pérez-Ortiz, M. (2024). Crafting desirable climate trajectories with RL explored socioenvironmental simulations. arXiv. https://doi.org/10.48550/arxiv.2410.07287
Shikhar, S., & Teckchandani, J. (2024). AI in international politics. International Journal of Research in Applied Science and Engineering Technology, 12(3), 810. https://doi.org/10.22214/ijraset.2024.58934
Sreedhar, K., Cai, A., Ma, J., Nickerson, J. V., & Chilton, L. B. (2025). Simulating cooperative prosocial behavior with multiagent LLMs: Evidence and mechanisms for AI agents to inform policy decisions. arXiv. https://doi.org/10.48550/arxiv.2502.12504
Tilbury, K., & Hoey, J. (2020). Multi-agent reinforcement learning and human social factors in climate change mitigation. arXiv. https://doi.org/10.48550/arxiv.2002.05147
Wang, J. Y., Sukiennik, N., Li, T., Su, W., Hao, Q., Xu, J., et al. (2024). A survey on human-centric LLMs. arXiv. https://doi.org/10.48550/arxiv.2411.14491
Wang, Z., Wang, D., Xu, Y., Zhou, L., & Zhou, Y. (2025). Intelligent computing social modeling and methodological innovations in political science in the era of large language models. Journal of Chinese Political Science. https://doi.org/10.1007/s11366-025-09917-6
Wang, Z., Yi, X., Wang, D., Zhou, L., & Zhou, Y. (2024). Intelligent computing social modeling and methodological innovations in political science in the era of large language models. arXiv. https://doi.org/10.48550/arxiv.2410.16301
Wawer, M., Chudziak, J. A., & Niewiadomska-Szynkiewicz, E. (2024). Large language models and the Elliott wave principle: A multi-agent deep learning approach to big data analysis in financial markets. Applied Sciences, 14(24), 11897. https://doi.org/10.3390/app142411897
Yu, L., Liu, H., Xie, C., Liu, S., Yin, Z., Chen, C., et al. (2024). FairMindSim: Alignment of behavior, emotion, and belief in humans and LLM agents amid ethical dilemmas. arXiv. https://doi.org/10.48550/arxiv.2410.10398
Zarza, I. de, Curto, J. de, Roig, G., Manzoni, P., & Calafate, C. T. (2023). Emergent cooperation and strategy adaptation in multi-agent systems: An extended coevolutionary theory with LLMs. Electronics, 12(12), 2722. https://doi.org/10.3390/electronics12122722
Zhang, T., Williams, A., Phade, S. R., Srinivasa, S., Zhang, Y., Gupta, P., et al. (2022). AI for global climate cooperation: Modeling global climate negotiations, agreements, and long-term cooperation in RICE-N. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4189735
Zhang, T., Williams, A., Phade, S. R., Srinivasa, S., Zhang, Y., Gupta, P., et al. (2022). AI for global climate cooperation: Modeling global climate negotiations, agreements, and long-term cooperation in RICE-N. arXiv. https://doi.org/10.48550/arxiv.2208.07004