In this paper we study the {\it pathwise stochastic Taylor expansion}, in the
sense of our previous work \cite{Buckdahn_Ma_02}, for a class of It\^o-type
random fields in which the diffusion part is allowed to contain both the random
field itself and its spatial derivatives. Random fields of such an
"self-exciting" type particularly contains the fully nonlinear stochastic PDEs
of curvature driven diffusion, as well as certain stochastic
Hamilton-Jacobi-Bellman equations.
In this paper we will discuss the optimal risk transfer problems when risk
measures are generated by G-expectations, and we present the relationship
between inf-convolution of G-expectations and the inf-convolution of drivers G.
Mathematical mean-field approaches play an important role in different fields
of Physics and Chemistry, but have found in recent works also their application
in Economics, Finance and Game Theory.