TY - JOUR
T1 - Social Optima of Backward Linear-Quadratic-Gaussian Mean-Field Teams
AU - Feng, Xinwei
AU - Huang, Jianhui
AU - Wang, Shujun
N1 - Funding Information:
The authors would like to thank the anonymous referees and the Associate Editor for careful reading and constructive comments. This work was supported by the National Natural Science Foundation of China (No. 12001317, 12001320), Shandong Provincial Natural Science Foundation (No. ZR2020QA019), RGC 153005/14P, 153275/16P, P0030808, P0008686, P0031044, QILU Young Scholars Program of Shandong University and the Fundamental Research Funds of Shandong University (11030072064071). The authors also acknowledge the financial support from PolyU-SDU joint research center.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2021/12
Y1 - 2021/12
N2 - This paper studies a class of stochastic linear-quadratic-Gaussian (LQG) dynamic optimization problems involving a large number of weakly-coupled heterogeneous agents. By “heterogeneous,” we mean agents are endowed with different types of parameters thus they are not statistically identical. Specifically, discrete-type heterogeneous agents are considered here which are more practical than homogeneous-type agents, and at the same time, more tractable than continuum-type heterogeneous agents. Unlike well-studied mean-field-game, these agents formalize a team with cooperation to minimize some social cost functional. Moreover, unlike standard social optima literature, the state here evolves by some backward stochastic differential equation (BSDE) in which the terminal instead initial condition is specified. Accordingly, the related social cost is represented by some recursive functional for which the initial state is considered. Applying a backward version of person-by-person optimality, we construct an auxiliary control problem for each agent based on decentralized information. The decentralized social strategy is derived by a class of new consistency condition (CC) systems, which are mean-field-type forward-backward stochastic differential equations (FBSDEs). The well-posedness of such consistency condition system is obtained via Riccati decoupling method. The related asymptotic social optimality is also verified.
AB - This paper studies a class of stochastic linear-quadratic-Gaussian (LQG) dynamic optimization problems involving a large number of weakly-coupled heterogeneous agents. By “heterogeneous,” we mean agents are endowed with different types of parameters thus they are not statistically identical. Specifically, discrete-type heterogeneous agents are considered here which are more practical than homogeneous-type agents, and at the same time, more tractable than continuum-type heterogeneous agents. Unlike well-studied mean-field-game, these agents formalize a team with cooperation to minimize some social cost functional. Moreover, unlike standard social optima literature, the state here evolves by some backward stochastic differential equation (BSDE) in which the terminal instead initial condition is specified. Accordingly, the related social cost is represented by some recursive functional for which the initial state is considered. Applying a backward version of person-by-person optimality, we construct an auxiliary control problem for each agent based on decentralized information. The decentralized social strategy is derived by a class of new consistency condition (CC) systems, which are mean-field-type forward-backward stochastic differential equations (FBSDEs). The well-posedness of such consistency condition system is obtained via Riccati decoupling method. The related asymptotic social optimality is also verified.
KW - Asymptotic social optima
KW - Backward person-by-person optimality
KW - Discrete-type heterogeneous system
KW - Initially mixed-coupled FBSDE
KW - LQG recursive control
KW - Mean-field team
UR - http://www.scopus.com/inward/record.url?scp=85105308678&partnerID=8YFLogxK
U2 - 10.1007/s00245-021-09782-8
DO - 10.1007/s00245-021-09782-8
M3 - Journal article
AN - SCOPUS:85105308678
SN - 0095-4616
VL - 84
SP - 651
EP - 694
JO - Applied Mathematics and Optimization
JF - Applied Mathematics and Optimization
IS - 1
ER -