I will present a simple adaptive network model describing recent insect swarming experiments. By exploiting an analogy with human decision-making models and considering network-like interactions, this model captures the experimental dynamics using a low dimensional system of equations that permits analytical investigation. It reproduces several characteristic features of swarms, including: spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, intermittency, and metastable configurations displaying memory effects. By considering only minimal components, and introducing few elements of the spatial dynamics, it highlights the essential elements required to reproduce the observed behavior.