  The collective decision-making exhibited by animal groups provides enormous
inspiration for multi-agent control system design as it embodies several
features that are desirable in engineered networks, including robustness and
adaptability, low computational effort, and an intrinsically decentralized
architecture. However, many of the mechanistic models for collective
decision-making are described at the population-level abstraction and are
challenging to implement in an engineered system. We develop simple and
easy-to-implement models of opinion dynamics that realize the empirically
observed collective decision-making behavior as well as the behavior predicted
by existing models of animal groups. Using methods from Lyapunov analysis,
singularity theory, and monotone dynamical systems, we rigorously investigate
the steady-state decision-making behavior of our models.
