Bio-inspired decision-making and control: From honeybees and neurons to network design

We present nonlinear deterministic models and linear stochastic models of decision-making between alternatives that connect biological groups as diverse as honeybees and neurons. Using these models we explain how biological groups, with decentralized control and limited sensing and communication, select the highest quality alternative, flip a coin for nearly equal alternatives, optimally balance speed and accuracy, maintain robustness in the face of uncertainty, and leverage heterogeneity. Motivated by these remarkable behaviors, we present a generalizable agent-based model for the design and control of network dynamics with the advantageous features observed in the biological groups.