具有退化扩散的Stopper vs. Singular Controller Games

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
Andrea Bovo, Tiziano De Angelis, Jan Palczewski
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引用次数: 0

摘要

研究了当受控扩散过程的(状态相关)扩散矩阵退化时,奇异控制器与阻塞器之间的零和随机对策。特别地,我们展示了游戏的存在值,并确定了一个最优的策略。动力学的退化性阻碍了基于Sobolev空间中合适变分问题解的解析方法的使用。因此,我们采用一种基于由参数\(\gamma >0\)调制的底层扩散扰动的概率方法。对于每个\(\gamma >0\),近似对策是非退化的,并承认一个值\(u^\gamma \)和一个最优策略\(\tau ^\gamma _*\)。让\(\gamma \rightarrow 0\)我们证明\(u^\gamma \)收敛于一个函数v,它识别了原始游戏的值。我们还明确构造了\(u^\gamma \)的最优停止时间\(\theta ^\gamma _*\),与\(\tau ^\gamma _*\)相关但不等于,对于具有退化动力学的博弈,它几乎肯定收敛于最优停止时间\(\theta _*\)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stopper vs. Singular Controller Games With Degenerate Diffusions

We study zero-sum stochastic games between a singular controller and a stopper when the (state-dependent) diffusion matrix of the underlying controlled diffusion process is degenerate. In particular, we show the existence of a value for the game and determine an optimal strategy for the stopper. The degeneracy of the dynamics prevents the use of analytical methods based on solution in Sobolev spaces of suitable variational problems. Therefore we adopt a probabilistic approach based on a perturbation of the underlying diffusion modulated by a parameter \(\gamma >0\). For each \(\gamma >0\) the approximating game is non-degenerate and admits a value \(u^\gamma \) and an optimal strategy \(\tau ^\gamma _*\) for the stopper. Letting \(\gamma \rightarrow 0\) we prove convergence of \(u^\gamma \) to a function v, which identifies the value of the original game. We also construct explicitly optimal stopping times \(\theta ^\gamma _*\) for \(u^\gamma \), related but not equal to \(\tau ^\gamma _*\), which converge almost surely to an optimal stopping time \(\theta _*\) for the game with degenerate dynamics.

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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