Research on Data Development and Application for Manual Wargaming in Joint Operations

Yanan Lei; Fang Zhang; Zhezhe Xu

doi:10.6911/WSRJ.202609_12(9).0003

Authors

Yanan Lei Rocket Force University of Engineering, Xi’an 710025, China
Fang Zhang Rocket Force University of Engineering, Xi’an 710025, China
Zhezhe Xu Rocket Force University of Engineering, Xi’an 710025, China

DOI:

https://doi.org/10.6911/WSRJ.202609_12(9).0003

Keywords:

Joint operations; manual wargaming; data development; combat-oriented training.

Abstract

Manual wargaming is an important method for joint operational training, , which is used to support operational simulation, improve command ability, and test tactics and operational methods. The quality of data development directly affects the realism of wargaming and the training effect. However, at present, the data system for manual wargaming in joint operations is still at an early stage of development, and there are some problems, such as weak connection between training and actual operations, limited quantitative description of human factors, and incomplete data accumulation and management mechanisms. These problems reduce the ability of grassroots units to meet routine training and combat-oriented training requirements, and , and also affect the training of operational personnel and the reuse of training results. Based on the practical needs of grassroots wargaming training, this study focuses on the gap between data supply and application needs. It proposes an application framework that includes standardized training, verification of tactics and operational methods, data updating, and training feedback. The framework is developed from three aspects: improving data granularity at different levels, establishing quantitative indicators for human factors, and improving data management standards.

Downloads

Download data is not yet available.

References

[1] Hu, X. F., & Luo, P. (2017). Wargaming: Innovation and practice in complex systems management. National Defense University Press.

[2] Qin, Y. L., Zhang, X. L., & Tao, H. J. (2019). Research on a Bayesian-theory-based data analysis method for wargaming exercises. Journal of Rocket Force University of Engineering (Natural Science Edition), (3), 34–38.

[3] Yang, G., & Hu, X. S. (2016). Research on a naval wargaming exercise system. Command Control & Simulation, 38(4), 96–101.

[4] Wu, K. Y., Huang, K. H., & Wang, L. (2026). Design and validation of a human–machine hybrid decisionmaking framework for wargaming. Journal of Zhengzhou University (Engineering Science), (1).

[5] Ma, J. T., & Zhang, Z. G. (2026). Exploration of innovation pathways for wargaming from the perspective of intelligence. National Defense Science & Technology, (1), 36–42.

[6] Zhang, D. Y., Yang, J. Y., Ma, J., & others. (2024). Construction of a machine-learning dataset for after-action review and analysis of wargaming. Journal of System Simulation, 36(3), 608–624.

[7] Lee, C. E., Baek, J., Son, J., & others. (2023). Deep AI military staff: Cooperative battlefield situation awareness for commander’s decision making. The Journal of Supercomputing, 79(6), 6040–6056. https://doi.org/10.1007/s11227-022-04832-4.