Backward Anticipated Social Optima: Input Constraints and Partial Information

A class of stochastic linear-quadratic (LQ) dynamic optimization problems involving a large population is investigated in this work. Here, the agents cooperate with each other to minimize certain social costs. Furthermore, differently from the classic social optima literature, the dynamics in this f...

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Main Author: Shujun Wang
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Mathematics
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Online Access:https://www.mdpi.com/2227-7390/13/2/306
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author Shujun Wang
author_facet Shujun Wang
author_sort Shujun Wang
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description A class of stochastic linear-quadratic (LQ) dynamic optimization problems involving a large population is investigated in this work. Here, the agents cooperate with each other to minimize certain social costs. Furthermore, differently from the classic social optima literature, the dynamics in this framework are driven by <i>anticipated</i> backward stochastic differential equations (ABSDE) in which the <i>terminal</i> instead of the <i>initial</i> condition is specified and the anticipated terms are involved. The individual admissible controls are constrained in <i>closed convex subsets</i>, and the <i>common noise</i> is considered. As a result, the related social cost is represented by a recursive functional in which the <i>initial</i> state is involved. By virtue of the so-called <i>anticipated person-by-person optimality principle</i>, a decentralized strategy can be derived. This is based on a class of new consistency condition systems, which are mean-field-type anticipated forward-backward stochastic differential delay equations (AFBSDDEs). The well-posedness of such a consistency condition system is obtained through a discounting decoupling method. Finally, the corresponding asymptotic social optimality is proved.
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spelling doaj-art-fe9c18f324b249858512dbc2bb9a9de32025-01-24T13:40:07ZengMDPI AGMathematics2227-73902025-01-0113230610.3390/math13020306Backward Anticipated Social Optima: Input Constraints and Partial InformationShujun Wang0School of Management, Shandong University, Jinan 250100, ChinaA class of stochastic linear-quadratic (LQ) dynamic optimization problems involving a large population is investigated in this work. Here, the agents cooperate with each other to minimize certain social costs. Furthermore, differently from the classic social optima literature, the dynamics in this framework are driven by <i>anticipated</i> backward stochastic differential equations (ABSDE) in which the <i>terminal</i> instead of the <i>initial</i> condition is specified and the anticipated terms are involved. The individual admissible controls are constrained in <i>closed convex subsets</i>, and the <i>common noise</i> is considered. As a result, the related social cost is represented by a recursive functional in which the <i>initial</i> state is involved. By virtue of the so-called <i>anticipated person-by-person optimality principle</i>, a decentralized strategy can be derived. This is based on a class of new consistency condition systems, which are mean-field-type anticipated forward-backward stochastic differential delay equations (AFBSDDEs). The well-posedness of such a consistency condition system is obtained through a discounting decoupling method. Finally, the corresponding asymptotic social optimality is proved.https://www.mdpi.com/2227-7390/13/2/306asymptotic social optimaanticipated person-by-person optimalityinitially mixed-coupled AFBSDDELQ recursive controlmean-field team
spellingShingle Shujun Wang
Backward Anticipated Social Optima: Input Constraints and Partial Information
Mathematics
asymptotic social optima
anticipated person-by-person optimality
initially mixed-coupled AFBSDDE
LQ recursive control
mean-field team
title Backward Anticipated Social Optima: Input Constraints and Partial Information
title_full Backward Anticipated Social Optima: Input Constraints and Partial Information
title_fullStr Backward Anticipated Social Optima: Input Constraints and Partial Information
title_full_unstemmed Backward Anticipated Social Optima: Input Constraints and Partial Information
title_short Backward Anticipated Social Optima: Input Constraints and Partial Information
title_sort backward anticipated social optima input constraints and partial information
topic asymptotic social optima
anticipated person-by-person optimality
initially mixed-coupled AFBSDDE
LQ recursive control
mean-field team
url https://www.mdpi.com/2227-7390/13/2/306
work_keys_str_mv AT shujunwang backwardanticipatedsocialoptimainputconstraintsandpartialinformation