Sequential Decomposition of Graphon Mean Field Games
Sequential Decomposition of Graphon Mean Field Games
复制标题
图平均场博弈的顺序分解
DOI：
10.2139/ssrn.3520348
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
S. Vishwanath
中科院分区：
文献类型：
--
作者：
Deepanshu Vasal;Rajesh K. Mishra;S. Vishwanath
本文提出了一种计算动态图子平均场博弈图子平均场均衡的序列分解算法。我们考虑了大量的球员顺序地作出战略决策，每个球员的行动影响他们的邻居，这是在一个图中捕获，由一个已知的graphon生成。每个局中人都观察到一个私有状态和一个公共信息，作为一个图子平均场种群状态，它代表了其他局中人类型的经验网络分布。我们考虑非平稳的人口状态动力学，并提出了一种新的向后递归算法来计算GMFE，这取决于两个，一个球员的私人类型，和当前（动态）人口状态确定通过graphon。该算法的每一步都由求解一个不动点方程组成。我们提供的条件，存在这样的GMFE的模型参数。使用该算法，我们获得了GMFE的网络物理系统中的特定安全设置，用于捕获系统中节点之间的交互的不同的图子。
In this paper, we present a sequential decomposition algorithm to compute graphon mean-field equillibrium (GMFE) of dynamic graphon mean-field games (GMFGs). We consider a large population of players sequentially making strategic decisions where the actions of each player affect their neighbors which is captured in a graph, generated by a known graphon. Each player observes a private state and also a common information as a graphon mean-field population state which represents the empirical networked distribution of other players' types. We consider non-stationary population state dynamics and present a novel backward recursive algorithm to compute GMFE that depend on both, a player's private type, and the current (dynamic) population state determined through the graphon. Each step in this algorithm consists of solving a fixed-point equation. We provide conditions on model parameters for which there exists such a GMFE. Using this algorithm, we obtain the GMFE for a specific security setup in cyber physical systems for different graphons that capture the interactions between the nodes in the system.
影响因子：
1.4
作者：
F. Delarue;D. Lacker;K. Ramanan
通讯作者：
F. Delarue;D. Lacker;K. Ramanan