Friend-Based Ranking
研究如何通过社会网络中的局部信息，设计激励相容且高效的机制来提取个体排名，并刻画了能实现完全排名的最稀疏网络结构。
We analyze the design of a mechanism to extract a ranking of individuals according to a unidimensional characteristic, such as ability or need. Individuals, connected on a social network, only have local information about the ranking. We show that a planner can construct an ex post incentive compatible and efficient mechanism if and only if every pair of friends has a friend in common. We characterize the windmill network as the sparsest social network for which the planner can always construct a complete ranking.