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Bibliographic Details
Main Authors: Gu, Zihao, Zhang, Jianfeng
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2602.07318
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author Gu, Zihao
Zhang, Jianfeng
author_facet Gu, Zihao
Zhang, Jianfeng
contents In this paper we study an optimization problem in which the control is information, more precisely, the control is a $σ$-algebra or a filtration. In a dynamic setting, we establish the dynamic programming principle and the law invariance of the value function. The latter requires a condition slightly stronger than the (H)-hypothesis for the admissible filtration, and enables us to define the value function on $\mathcal P_2(\mathcal P_2(\mathbb R^d))$, the space of laws of random probability measures. By using a new Itô's formula for smooth functions on $\mathcal P_2(\mathcal P_2(\mathbb R^d))$, we characterize the value function of the information control problem by an Hamilton-Jacobi-Bellman equation on this space.
format Preprint
id arxiv_https___arxiv_org_abs_2602_07318
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle On Information Controls
Gu, Zihao
Zhang, Jianfeng
Optimization and Control
35R15, 49N30, 60H30, 91A27
In this paper we study an optimization problem in which the control is information, more precisely, the control is a $σ$-algebra or a filtration. In a dynamic setting, we establish the dynamic programming principle and the law invariance of the value function. The latter requires a condition slightly stronger than the (H)-hypothesis for the admissible filtration, and enables us to define the value function on $\mathcal P_2(\mathcal P_2(\mathbb R^d))$, the space of laws of random probability measures. By using a new Itô's formula for smooth functions on $\mathcal P_2(\mathcal P_2(\mathbb R^d))$, we characterize the value function of the information control problem by an Hamilton-Jacobi-Bellman equation on this space.
title On Information Controls
topic Optimization and Control
35R15, 49N30, 60H30, 91A27
url https://arxiv.org/abs/2602.07318