# On Information Controls

arXiv: https://arxiv.org/abs/2602.07318 · HTML: https://mlsanity.com/paper/2602.07318/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [Optimal Coarse Correlated Equilibria in Mean Field Games: Linear Programming and No-Regret Learning](https://mlsanity.com/paper/2606.20062/) (2026)

## Abstract (arXiv)

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 P2(mathcal P2(mathbb Rd)), the space of laws of random probability measures. By using a new Ito's formula for smooth functions on mathcal P2(mathcal P2(mathbb Rd)), we characterize the value function of the information control problem by an Hamilton-Jacobi-Bellman equation on this space.