Artificial Viscosity Joint Spacetime Multigrid Method for Hamilton–Jacobi–Bellman and Kolmogorov–Fokker–Planck System Arising from Mean Field Games

2021 ◽  
Vol 88 (1) ◽  
Author(s):  
Yangang Chen ◽  
Justin W. L. Wan
Author(s):  
Виктория Сергеевна Корниенко ◽  
Владимир Викторович Шайдуров ◽  
Евгения Дмитриевна Карепова

Представлен конечно-разностный аналог дифференциальной задачи, сформулированной в терминах теории “игр среднего поля” (mean field games). Задачи оптимизации такого типа формулируются как связанные системы параболических дифференциальных уравнений в частных производных типа Фоккера - Планка и Гамильтона - Якоби - Беллмана. Предложенный конечно-разностный аналог обладает основными свойствами оптимизационной дифференциальной задачи непосредственно на дискретном уровне. В итоге он может служить как приближение, сходящееся к исходной дифференциальной задаче при стремлении шагов дискретизации к нулю, так и как самостоятельная оптимизационная задача с конечным числом участников. Для предложенного аналога построен алгоритм монотонной минимизации функционала стоимости, проиллюстрированный на модельной экономической задаче In most forecasting problems, overstating or understating forecast leads to various losses. Traditionally, in the theory of “mean field games”, the functional responsible for the costs of implementing the interaction of the continuum of agents between each other is supposed to be dependent on the squared function of control of the system. Since additional external factors can influence the player’s strategy, the control function of a dynamic system is more complex. Therefore, the purpose of this article is to develop a computational algorithm applicable for more general set of control functions. As a research method, a computational experiment and proof of the stability of the constructed computational scheme are used in this study. As a result, the numerical algorithm was applied on the problem of economic interaction in the presence of alternative resources. We consider the model, in which a continuum of consumer agents consists of households deciding on heating, having a choice between the cost of installing and maintaining the thermal insulation or the additional cost of electricity. In the framework of the problem, the convergence of the method is numerically demonstrated. Conclusions. The article considers a model of the strategic interaction of continuum of agents, the interaction of which is determined by a coupled differential equations, namely, the Fokker - Planck and the Hamilton - Jacobi - Bellman one. To approximate the differential problem, difference schemes with a semi-Lagrangian approximation are used, which give a direct rule for minimizing the cost functional


Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 14
Author(s):  
Luca Di Persio ◽  
Matteo Garbelli

We provide a rigorous mathematical formulation of Deep Learning (DL) methodologies through an in-depth analysis of the learning procedures characterizing Neural Network (NN) models within the theoretical frameworks of Stochastic Optimal Control (SOC) and Mean-Field Games (MFGs). In particular, we show how the supervised learning approach can be translated in terms of a (stochastic) mean-field optimal control problem by applying the Hamilton–Jacobi–Bellman (HJB) approach and the mean-field Pontryagin maximum principle. Our contribution sheds new light on a possible theoretical connection between mean-field problems and DL, melting heterogeneous approaches and reporting the state-of-the-art within such fields to show how the latter different perspectives can be indeed fruitfully unified.


Author(s):  
Piyush Grover

This work is concerned with stability analysis of stationary and time-varying equilibria in a class of mean-field games that relate to multi-agent control problems of flocking and swarming. The mean-field game framework is a non-cooperative model of distributed optimal control in large populations, and characterizes the optimal control for a representative agent in Nash-equilibrium with the population. A mean-field game model is described by a coupled PDE system of forward-in-time Fokker-Planck (FP) equation for density of agents, and a backward-in-time Hamilton-Jacobi-Bellman (HJB) equation for control. The linear stability analysis of fixed points of these equations typically proceeds via numerical computation of spectrum of the linearized MFG operator. We explore the Evans function approach that provides a geometric alternative to solving the characteristic equation.


2021 ◽  
Vol 62 (8) ◽  
pp. 083302
Author(s):  
Thibault Bonnemain ◽  
Thierry Gobron ◽  
Denis Ullmo

Games ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 7
Author(s):  
Vassili N. Kolokoltsov

Quantum games and mean-field games (MFG) represent two important new branches of game theory. In a recent paper the author developed quantum MFGs merging these two branches. These quantum MFGs were based on the theory of continuous quantum observations and filtering of diffusive type. In the present paper we develop the analogous quantum MFG theory based on continuous quantum observations and filtering of counting type. However, proving existence and uniqueness of the solutions for resulting limiting forward-backward system based on jump-type processes on manifolds seems to be more complicated than for diffusions. In this paper we only prove that if a solution exists, then it gives an ϵ-Nash equilibrium for the corresponding N-player quantum game. The existence of solutions is suggested as an interesting open problem.


Sign in / Sign up

Export Citation Format

Share Document