Crowdsourcing and Optimal Market Design
提出一种众包机制，在代理人无法完全观察对象特征时近似实现最优分配，通过惩罚偏离“群体智慧”的报告来激励诚实，并证明该机制在市场规模增大时能以指数速度逼近完全信息下的最优分配。
Abstract Mechanisms used to derive optimal allocations are typically designed assuming agents fully know their own preferences. It is often impossible to duplicate optimal allocations when agents imperfectly observe object characteristics. I present a crowdsourcing mechanism to approximate optimal allocations under imperfect observations. To ensure truthtelling, agents are punished when their reports differ from the “wisdom of the crowd.” Under mild conditions, this crowdsourcing-with-punishment mechanism replicates the full-information optimal allocation with probability exponentially converging to one in the market size, with small waste. No alternative mechanism meaningfully does better. The proposed mechanism can be applied in many settings, including matching markets.