Several iterative knowledge aggregation methods are discussed. Such methods are used to choose one of a finite set of labels about each of a set of objects. First, a stimulus is analyzed locally at each object, yielding an initial state which assigns a weight of the evidence from that analysis to each of the labels. The methods continue as a sequence of trials where new evidence is gathered and the current state and new evidence are combined, resulting in a new state. This method iterates until sufficient confidence in a single label at each object is achieved. Several such methods are compared.