Special Session 29: Mean field stochastic control problems and related topics
Organizer(s): Juan Li , Rainer Buckdahn
Parallel Session 9 :: Wednesday, 12/18, 8:00-10:00
Capital Suite 10
8:00-8:30
Rainer Buckdahn
(Universite de Bretagne Occidentale, France)
Optimal control problems with generalized mean-field dynamics and viscosity solution to Master Bellman equation
8:30-9:00
Laurent Denis
(Le Mans University, France)
Stochastic PDEs driven by $G-$Brownian motion and the associated Backward Doubly Stochastic Differential Equations
9:00-9:30
Juan Li
(Shandong University, Peoples Rep of China)
Mean field stochastic control problems under sublinear expectation
9:30-10:00
Brahim BM Mezerdi
(King Fahd University of Petroleum and Minerals, Saudi Arabia)
On Some Generic Properties of Mean-Field Stochastic Differential Equations
Parallel Session 10 :: Wednesday, 12/18, 12:30-14:30 Capital Suite 10
12:30-13:00
Zhiyong Yu
(Shandong University, Peoples Rep of China)
Exact Controllability for Linear Stochastic Game-Based Control Systems
13:00-13:30
Qi Zhang
(Fudan University, Peoples Rep of China)
Some New Results on Entropy Regularized Backward Stochastic Control Systems
13:30-14:00
Qingmeng Wei
(Northeast Normal Univeristy, Peoples Rep of China)
Linear-Quadratic Optimal Control Problem for Mean-Field Stochastic Differential Equations with a Type of Random Coefficients
14:00-14:30
Jing Zhang
(Fudan University, Peoples Rep of China)
Backward Stochastic Partial Differential Equations with Conormal Boundary Conditions
Parallel Session 11 :: Wednesday, 12/18, 14:45-16:45 Capital Suite 10
14:45-15:15
Yunzhang Li
(Fudan University, Peoples Rep of China)
Fractional BSPDEs with Applications to Optimal Control of Partially Observed Systems with Jumps
15:45-16:15
Wenqiang Li
(Shandong University, Peoples Rep of China)
Mean Field Games of Major-Minor Agents with Recursive Functionals
16:15-16:45
Chuanzhi Xing
(Shandong University, Peoples Rep of China)
Path-dependent controlled mean-field coupled forward-backward SDEs. The associated stochastic maximum principle