two significant differences between the Vancouver algorithm and the PeerRank rule. First, the Vancouver algorithm measures variance in grades whilst PeerRank (like the NSF rule) measures the absolute deviation. A rule based on variance will tend to penalise inexperienced agents more greatly. Second, the reward term in the Vancouver algorithm is added after a fixed point is reached, whilst in the PeerRank rule, it is part of the fixed point calculation. We conjecture that it is more robust to include it in the fixed point calculation when there is significant variation in the accuracy of grades assigned by a single agent. There are a number of closely related problems to ours in the social choice literature. For example, Holzman and Moulin have studied a related problem in which a set of agents wish to select one amongst them to receive a prize [@hmeconometrica2013]. A fundamental assumption of this work is that nominations are impartial: your message never influences whether you win the prize. In our setting, such an assumption is less appropriate. We want your evaluation of the work of another agent to influence your evaluation. There are several reasons behind this change. First, your ability to evaluate the work 