Abstract
The theory of optimal mechanism design often relies on the assumption that agents fully know their preferences. More realistically, preferences may be based on characteristics of goods which are observed via noisy, informative signals. Can markets aggregate this information while still delivering a full-information “optimal” outcome? I show that it is possible to achieve these goals by first aggregating the information of all agents and then running an optimal full-information mechanism. To ensure proper incentives, agents are punished when their reports do not match up with the “wisdom of the crowd.” The punishment scheme is independent of the desired allocation, and can be enacted with or without monetary transfers. Even when the number of objects being assessed is much larger than the number of assessors, the proposed mechanism asymptotically correctly identifies every object's quality, while imposing a worst-case total punishment that converges exponentially to zero. Therefore, I am able to generate nearly optimal allocations in two-sided matching markets with interdependent preferences, a setting for which impossibility results exist. I give necessary and sufficient conditions for recovering desirable properties when information acquisition is endogenous and costly.
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