# Low rattling: A predictive principle for self-organization in active collectives

arXiv: https://arxiv.org/abs/2101.00683 · HTML: https://mlsanity.com/paper/2101.00683/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2021.

## Built on by (most cited first, 2 of 2)

- [Maximum diffusion reinforcement learning](https://mlsanity.com/paper/2309.15293/) (2023)
- [Goal Discovery with Causal Capacity for Efficient Reinforcement Learning](https://mlsanity.com/paper/2508.09624/) (2025)

## Abstract (arXiv)

Self-organization is frequently observed in active collectives, from ant rafts to molecular motor assemblies. General principles describing self-organization away from equilibrium have been challenging to identify. We offer a unifying framework that models the behavior of complex systems as largely random, while capturing their configuration-dependent response to external forcing. This allows derivation of a Boltzmann-like principle for understanding and manipulating driven self-organization. We validate our predictions experimentally in shape-changing robotic active matter, and outline a methodology for controlling collective behavior. Our findings highlight how emergent order depends sensitively on the matching between external patterns of forcing and internal dynamical response properties, pointing towards future approaches for design and control of active particle mixtures and metamaterials.