Virtual Bayesian Implementation
刻画了在不完全信息下，社会选择函数能被机制均衡结果近似实施的条件：激励相容是虚拟贝叶斯可实施的必要且几乎充分的条件，结合贝叶斯激励一致性则充分。
Allowing for incomplete information, this paper characterizes the social choice functions that can be approximated by the equilibrium outcomes of a mechanism: incentive compatibility is necessary and almost sufficient for virtual Bayesian implementability. In conjunction with a second condition, Bayesian incentive consistency, incentive compatibility is also sufficient. This new condition is weak-under standard topological and informational assumptions, it is satisfied by every social choice function. The type sets of the agents are taken to be arbitrary (possibly infinite) measurable spaces. An example shows that there are virtually (in fact, exactly) Bayesian implementable social choice functions that are not virtually implementable in iteratively undominated strategies.