A major challenge in the field of Multi-Agent Systems is to enable autonomous agents to allocate tasks efficiently. This paper extends previous work on an approach to the collective iterative task allocation problem where a group of agents endeavours to make the best allocations possible over multiple iterations of proposing, selection and learning. We offer an algorithm capturing the main aspects of this approach, and then show analytically and empirically that the agents? estimations of the performance of a task and the type of group decision policy play an important role in the performance of the algorithm.