  The correlated motion of flocks is an instance of global order emerging from
local interactions. An essential difference with analogous ferromagnetic
systems is that flocks are active: animals move relative to each other,
dynamically rearranging their interaction network. The effect of this
off-equilibrium element is well studied theoretically, but its impact on actual
biological groups deserves more experimental attention. Here, we introduce a
novel dynamical inference technique, based on the principle of maximum entropy,
which accodomates network rearrangements and overcomes the problem of slow
experimental sampling rates. We use this method to infer the strength and range
of alignment forces from data of starling flocks. We find that local bird
alignment happens on a much faster timescale than neighbour rearrangement.
Accordingly, equilibrium inference, which assumes a fixed interaction network,
gives results consistent with dynamical inference. We conclude that bird
orientations are in a state of local quasi-equilibrium over the interaction
length scale, providing firm ground for the applicability of statistical
physics in certain active systems.
