Adaptive decision-making over complex networks

It is common for biological networks to encounter situations where agents need to decide between multiple options, such as deciding between moving towards one food source or another or between moving towards a new hive or another. In previous works, we developed several powerful diffusion strategies that allow agents to estimate a model of interest in an adaptive and distributed manner through a process of in-network collaboration and learning. In this work, we consider the situation in which the data observed by the agents may arise from two different distributions or models. We develop and study a procedure by which the entire network can be made to follow one objective or the other through a distributed and collaborative decision process.