826 research outputs found
Partial Information Differential Games for Mean-Field SDEs
This paper is concerned with non-zero sum differential games of mean-field
stochastic differential equations with partial information and convex control
domain. First, applying the classical convex variations, we obtain stochastic
maximum principle for Nash equilibrium points. Subsequently, under additional
assumptions, verification theorem for Nash equilibrium points is also derived.
Finally, as an application, a linear quadratic example is discussed. The unique
Nash equilibrium point is represented in a feedback form of not only the
optimal filtering but also expected value of the system state, throughout the
solutions of the Riccati equations.Comment: 7 page
Necessary Condition for Near Optimal Control of Linear Forward-backward Stochastic Differential Equations
This paper investigates the near optimal control for a kind of linear
stochastic control systems governed by the forward backward stochastic
differential equations, where both the drift and diffusion terms are allowed to
depend on controls and the control domain is not assumed to be convex. In the
previous work (Theorem 3.1) of the second and third authors [\textit{%
Automatica} \textbf{46} (2010) 397-404], some problem of near optimal control
with the control dependent diffusion is addressed and our current paper can be
viewed as some direct response to it. The necessary condition of the
near-optimality is established within the framework of optimality variational
principle developed by Yong [\textit{SIAM J. Control Optim.} \textbf{48} (2010)
4119--4156] and obtained by the convergence technique to treat the optimal
control of FBSDEs in unbounded control domains by Wu [% \textit{Automatica}
\textbf{49} (2013) 1473--1480]. Some new estimates are given here to handle the
near optimality. In addition, an illustrating example is discussed as well.Comment: To appear in International Journal of Contro
A Probabilistic Approach to Mean Field Games with Major and Minor Players
We propose a new approach to mean field games with major and minor players.
Our formulation involves a two player game where the optimization of the
representative minor player is standard while the major player faces an
optimization over conditional McKean-Vlasov stochastic differential equations.
The definition of this limiting game is justified by proving that its solution
provides approximate Nash equilibriums for large finite player games. This
proof depends upon the generalization of standard results on the propagation of
chaos to conditional dynamics. Because it is on independent interest, we prove
this generalization in full detail. Using a conditional form of the Pontryagin
stochastic maximum principle (proven in the appendix), we reduce the solution
of the mean field game to a forward-backward system of stochastic differential
equations of the conditional McKean-Vlasov type, which we solve in the Linear
Quadratic setting. We use this class of models to show that Nash equilibriums
in our formulation can be different from those of the formulations contemplated
so far in the literature
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