  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.
