可微函数
路径(计算)
纳什均衡
数学优化
数理经济学
随机博弈
计算机科学
一般均衡理论
数学
应用数学
经济
数学分析
微观经济学
程序设计语言
作者
Yiyin Cao,Yin Chen,Chuangyin Dang
出处
期刊:Informs Journal on Computing
日期:2023-10-11
卷期号:36 (2): 377-396
标识
DOI:10.1287/ijoc.2022.0148
摘要
The concept of proper equilibrium was established as a strict refinement of perfect equilibrium. This establishment has significantly advanced the development of game theory and its applications. Nonetheless, it remains a challenging problem to compute such an equilibrium. This paper develops a differentiable path-following method with a compact formulation to compute a proper equilibrium. The method incorporates square-root-barrier terms into payoff functions with an extra variable and constitutes a square-root-barrier game. As a result of this barrier game, we acquire a smooth path to a proper equilibrium. To further reduce the computational burden, we present a compact formulation of an ε-proper equilibrium with a polynomial number of variables and equations. Numerical results show that the differentiable path-following method is numerically stable and efficient. Moreover, by relaxing the requirements of proper equilibrium and imposing Selten’s perfection, we come up with the notion of perfect d-proper equilibrium, which approximates a proper equilibrium and is less costly to compute. Numerical examples demonstrate that even when d is rather large, a perfect d-proper equilibrium remains to be a proper equilibrium. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms-Continuous. Funding: This work was partially supported by General Research Fund (GRF) CityU 11306821 of Hong Kong SAR Government. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0148 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0148 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
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