两阶段分布鲁棒非合作对策:纳什均衡的存在性及其在Cournot-Nash竞争中的应用

IF 1.2 4区 工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY
Atsushi Hori, Nobuo Yamashita
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引用次数: 1

摘要

研究了具有连续决策变量的两阶段分布鲁棒随机非合作对策。在这种博弈中,每个参与者根据其他参与者的决策解决一个两阶段的分布鲁棒优化问题。该领域的现有研究受到严格假设的限制,例如线性决策规则,并假设每个参与者解决具有特定结构模糊集的两阶段线性分布鲁棒优化。这种限制促使我们在非线性情况下归纳和分析游戏。本研究的贡献在于(ⅰ)证明了在凸性和紧性假设下两阶段纳什均衡存在的条件,(ⅱ)考虑了两阶段分布鲁棒Cournot-Nash竞争的应用,并从经济意义上考察了市场均衡存在的条件。我们还报告了一些数值实验的结果,以说明分布鲁棒性如何影响库尔诺-纳什竞争中每个参与者的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-stage distributionally robust noncooperative games: Existence of Nash equilibrium and its application to Cournot–Nash competition
Two-stage distributionally robust stochastic noncooperative games with continuous decision variables are studied. In such games, each player solves a two-stage distributionally robust optimization problem depending on the decisions of the other players. Existing studies in this area have been limited with strict assumptions, such as linear decision rules, and supposed that each player solves a two-stage linear distributionally robust optimization with a specifically structured ambiguity set. This limitation motivated us to generalize and analyze the game in a nonlinear case. The contributions of this study are (ⅰ) demonstrating the conditions for the existence of two-stage Nash equilibria under convexity and compactness assumptions, and (ⅱ) consideration of a two-stage distributionally robust Cournot–Nash competition as an application, as well as an investigation into the conditions for the existence of market equilibria in an economic sense. We also report some results of numerical experiments to illustrate how distributional robustness affects the decision of each player in the Cournot–Nash competition.
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来源期刊
CiteScore
2.50
自引率
15.40%
发文量
207
审稿时长
18 months
期刊介绍: JIMO is an international journal devoted to publishing peer-reviewed, high quality, original papers on the non-trivial interplay between numerical optimization methods and practically significant problems in industry or management so as to achieve superior design, planning and/or operation. Its objective is to promote collaboration between optimization specialists, industrial practitioners and management scientists so that important practical industrial and management problems can be addressed by the use of appropriate, recent advanced optimization techniques.
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