提出新方法计算动态博弈中非归一化均衡解，拓展解空间并提升应用灵活性。
Generalized Nash Equilibrium Solutions in Dynamic Games With Shared Constraints
- 基于混合互补问题（MCP）构建非归一化广义纳什均衡求解框架。
- 通过预设准则系统选择最优均衡解，支持多目标决策需求。
- 数值实验验证方法有效性，为传统归一化解提供替代方案。
在具有共享约束的动态博弈中，广义纳什均衡（GNE）通常通过归一化解法计算，该方法假设所有玩家对共享约束使用相同的拉格朗日乘子。尽管广泛应用，此方法会排除其他潜在有价值的GNE。本文提出一种基于混合互补问题（MCP）的新方法，用于计算非归一化的GNE，从而扩展解空间。同时，我们设计了一种基于预定义准则的系统性方法，以选择最优的GNE，增强实际应用中的灵活性。数值例子验证了该方法的有效性，提供了对传统归一化解的替代方案。
原文摘要 · Abstract (English)
In dynamic games with shared constraints, Generalized Nash Equilibria (GNE) are often computed using the normalized solution concept, which assumes identical Lagrange multipliers for shared constraints across all players. While widely used, this approach excludes other potentially valuable GNE. This paper presents a novel method based on the Mixed Complementarity Problem (MCP) formulation to compute non-normalized GNE, expanding the solution space. We also propose a systematic approach for selecting the optimal GNE based on predefined criteria, enhancing practical flexibility. Numerical examples illustrate the methods effectiveness, offering an alternative to traditional normalized solutions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。