基于信息单调性的无验证信息获取的均衡选择
Equilibrium Selection in Information Elicitation without Verification via Information Monotonicity
摘要：
同伴预测机制是一种从自利主体那里获得私有的、不可变的信息的机制-形式上讲，告知真值是该机制的严格贝叶斯纳什均衡。原始的同伴预测机制有两个主要的局限性：（1）该机制必须知道代理人信号的“共同先验”;（2）存在额外的不受欢迎和不真实的均衡，这些均衡通常比说实话的均衡具有更大的预期收益。一系列结果成功地削弱了已知的共同先验假设。然而，平衡多重性问题仍然是一个挑战。在本文中，我们解决上述两个问题。在一个共同的先验存在，但不知道的机制设置，我们表明（1）一般的负面结果适用于一个大类的机制，表明说实话永远不能支付严格更多的期望比一组特定的均衡，代理人串通“重新标记”的信号，并告诉真相后，重新标记的信号;（2）提供一种机制，该机制没有关于共同先验的信息，但在该机制中，讲真话的预期支付与任何重新标记均衡一样多，并且支付严格高于任何其他对称均衡;（3）此外，在我们的机制中，如果代理人的数量足够大，那么说实话的报酬与任何接近“重新标记”均衡的均衡类似，并且严格地比任何不接近重新标记均衡的均衡都要高。
英文摘要：
Peer-prediction is a mechanism which elicits privately-held, non-variable information from self-interested agents---formally, truth-telling is a strict Bayes Nash equilibrium of the mechanism. The original Peer-prediction mechanism suffers from two main limitations: (1) the mechanism must know the "common prior" of agents' signals; (2) additional undesirable and non-truthful equilibria exist which often have a greater expected payoff than the truth-telling equilibrium. A series of results has successfully weakened the known common prior assumption. However, the equilibrium multiplicity issue remains a challenge. In this paper, we address the above two problems. In the setting where a common prior exists but is not known to the mechanism we show (1) a general negative result applying to a large class of mechanisms showing truth-telling can never pay strictly more in expectation than a particular set of equilibria where agents collude to "relabel" the signals and tell the truth after relabeling signals; (2) provide a mechanism that has no information about the common prior but where truth-telling pays as much in expectation as any relabeling equilibrium and pays strictly more than any other symmetric equilibrium; (3) moreover in our mechanism, if the number of agents is sufficiently large, truth-telling pays similarly to any equilibrium close to a "relabeling" equilibrium and pays strictly more than any equilibrium that is not close to a relabeling equilibrium.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
