We investigate decentralised decision-making, in which a robot swarm is tasked with selecting the best-quality option among a set of alternatives. Individual robots are simplistic as they only perform diffusive search, make local noisy estimates of the options' quality, and exchange information with near neighbours. We propose a decentralised algorithm, inspired by house-hunting honeybees, to efficiently aggregate noisy estimations. Individual robots, by varying over time a single decentralised parameter that modulates the interaction strength, balance exploration and agreement. In this way, the swarm first identifies the options under consideration, then rapidly converges on the best available option, even when outnumbered by lower quality options. We present stochastic analyses and swarm robotics simulations to compare the novel strategy with previous methods and to quantify the performance improvement. The proposed strategy limits the spreading of errors within the population and allows swarms of simple noisy units with minimal communication capabilities to make highly accurate collective decisions in predictable time.

Decision making is a complex task, and its underlying mechanisms that regulate behavior, such as the implementation of the coupling between physiological states and neural networks, are hard to decipher. To gain more insight into neural computations underlying ongoing binary decision-making tasks, we consider a neural circuit that guides the feeding behavior of a hypothetical animal making dietary choices. We adopt an inhibition motif from neural network theory and propose a dynamical system characterized by nonlinear feedback, which links mechanism (the implementation of the neural circuit and its coupling to the animal's nutritional state) and function (improving behavioral performance). A central inhibitory unit influences evidence-integrating excitatory units, which in our terms correspond to motivations competing for selection. We determine the parameter regime where the animal exhibits improved decision-making behavior and explain different behavioral outcomes by making the link between accessible states of the nonlinear neural circuit model and decision-making performance. We find that for given deficits in nutritional items, the variation of inhibition strength and ratio of excitation and inhibition strengths in the decision circuit allows the animal to enter an oscillatory phase that describes its internal motivational state. Our findings indicate that this oscillatory phase may improve the overall performance of the animal in an ongoing foraging task and underpin the importance of an integrated functional and mechanistic study of animal activity selection.