Stochastic Nash Equilibrium Problems: Models, Analysis, and Algorithms
IEEE Control Systems Magazine(2022)
摘要
Decision making under uncertainty has been studied extensively over the last 70 years, if not earlier. In the field of optimization, models for two-stage, stochastic, linear programming, presented by Dantzig
[1]
and Beale
[2]
, are often viewed as the basis for the subsequent development of the field of stochastic optimization. This subfield of optimization now encompasses a breadth of models that can accommodate both convexity and nonconvexity, probabilistic constraints, risk-aversion, discreteness, and multistage decision-making (compare
[3]
,
[4]
). Similarly, stochastic control
[5]
has proven to be an enormously impactful subarea of control theory. When one extends the decision-making paradigm to multiple self-interested decision makers, then the resulting problem can be viewed as a noncooperative game that is rooted in the groundbreaking text by Von Neumann and Morgenstern
[6]
.
更多查看译文
关键词
multistage decision-making paradigm,self-interested decision makers,control theory,stochastic control,risk-aversion,probabilistic constraints,nonconvexity,stochastic optimization,linear programming,stochastic programming,stochastic Nash equilibrium problems
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要