{"ID":2844775,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.04940","arxiv_id":"2511.04940","title":"Strategic Decision-Making Under Uncertainty through Bi-Level Game Theory and Distributionally Robust Optimization","abstract":"In strategic scenarios where decision-makers operate at different hierarchical levels, traditional optimization methods are often inadequate for handling uncertainties from incomplete information or unpredictable external factors. To fill this gap, we introduce a mathematical framework that integrates bi-level game theory with distributionally robust optimization (DRO), particularly suited for complex network systems. Our approach leverages the hierarchical structure of bi-level games to model leader-follower interactions while incorporating distributional robustness to guard against worst-case probability distributions. To ensure computational tractability, the Karush-Kuhn-Tucker (KKT) conditions are used to transform the bi-level challenge into a more manageable single-level model, and the infinite-dimensional DRO problem is reformulated into a finite equivalent. We propose a generalized algorithm to solve this integrated model. Simulation results validate our framework's efficacy, demonstrating that under high uncertainty, the proposed model achieves up to a 22\\% cost reduction compared to traditional stochastic methods while maintaining a service level of over 90\\%. This highlights its potential to significantly improve decision quality and robustness in networked systems such as transportation and communication networks.","short_abstract":"In strategic scenarios where decision-makers operate at different hierarchical levels, traditional optimization methods are often inadequate for handling uncertainties from incomplete information or unpredictable external factors. To fill this gap, we introduce a mathematical framework that integrates bi-level game the...","url_abs":"https://arxiv.org/abs/2511.04940","url_pdf":"https://arxiv.org/pdf/2511.04940v1","authors":"[\"Jiachen Shen\",\"Jian Shi\",\"Lei Fan\",\"Chenye Wu\",\"Dan Wang\",\"Choong Seon Hong\",\"Zhu Han\"]","published":"2025-11-07T02:59:39Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"eess.SP\"]","methods":"[]","has_code":false}