Aiming at the dynamic risk evolution problem caused by the strategic interaction of multiple stakeholders in complex systems, this paper proposes a hybrid risk assessment model integrating Markov chains and game theory. Traditional static models have limitations in depicting the nonlinear conduction mechanism of strategy interaction and the long-term risk trend. In this study, by embedding the game equilibrium strategy into the state transition process of the Markov chain, a dynamic closed-loop framework of "strategy selection - state evolution - benefit feedback" is constructed. The model incorporates the environmental risk level and the combination of participants' strategies into the state space together, and modifies the transition probability matrix in real time based on Nash equilibrium to achieve the dynamic mapping of micro-strategy interaction and macro-risk evolution. Numerical experiments in the supply chain scenario show that in a low/medium risk state, suppliers and purchasers can significantly increase long-term returns through the "expansion - additional purchase" strategy combination (the maximum increase in supplier returns is 46.4%). Under the high-risk state, the strategy synergy shifts to the "contraction - purchase reduction" combination, and the returns of both sides are optimized by 45.6% and 13.6% respectively, verifying the risk adaptive characteristics of the Nash equilibrium. The sensitivity analysis further revealed that the discount factor has a significant impact on the long-term value under the low-risk state (the increase of the value function reaches 28.7% when γ=0.95), providing a theoretical basis for the design of the dynamic discount mechanism. This model provides supply chain managers with a decision support tool that combines theoretical rigor and practical operability by coordinating strategic conflicts and optimizing risk exposure.

Wei Guo, Shuijin Rong, Hao Liu. Dynamic Supply Chain Risk Assessment and Strategy Collaborative Optimization Model Based on Markov Chain and Game Theory. Academic Journal of Engineering and Technology Science (2025), Vol. 8, Issue 3: 51-58. https://doi.org/10.25236/AJETS.2025.080308.

[1] Siu, N. (1994). Risk assessment for dynamic systems: an overview. Reliability Engineering & System Safety, 43(1), 43-73.

[2] Li, P., & Bathelt, H. (2018). Location strategy in cluster networks. Journal of International Business Studies, 49(8), 967-989.

[3] Morgan, M. G. (2014). Use (and abuse) of expert elicitation in support of decision making for public policy. Proceedings of the National academy of Sciences, 111(20), 7176-7184.

[4] Do, C. T., Tran, N. H., Hong, C., Kamhoua, C. A., Kwiat, K. A., Blasch, E., ... & Iyengar, S. S. (2017). Game theory for cyber security and privacy. ACM Computing Surveys (CSUR), 50(2), 1-37.

[5] Bloembergen, D., Tuyls, K., Hennes, D., & Kaisers, M. (2015). Evolutionary dynamics of multi-agent learning: A survey. Journal of Artificial Intelligence Research, 53, 659-697.

[6] Sandholm, W. H. (2020). Evolutionary game theory. Complex social and behavioral systems: game theory and agent-based models, 573-608.

[7] Sahinoglu, M., Cueva‐Parra, L., & Ang, D. (2012). Game‐theoretic computing in risk analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 4(3), 227-248.

[8] Pang, J. S., Sen, S., & Shanbhag, U. V. (2017). Two-stage non-cooperative games with risk-averse players. Mathematical Programming, 165, 235-290.

[9] Davison, A. C. (2003). Statistical models (Vol. 11). Cambridge university press.

[10] Colman, A. M. (2016). Game theory and experimental games: The study of strategic interaction. Elsevier.

[11] Wang, X., & Teo, K. L. (2022). Generalized Nash equilibrium problem over a fuzzy strategy set. Fuzzy Sets and Systems, 434, 172-184.

[12] Boholm, M. (2019). Risk and quantification: A linguistic study. Risk Analysis, 39(6), 1243-1261.

[13] Wu, D., & Li, X. (2025). Sensitivity analysis for quantiles of hidden biases in matched observational studies. Journal of the American Statistical Association, 1-12.