论文标题
实施不确定的证据
Implementation with Uncertain Evidence
论文作者
论文摘要
我们研究了设计师未知的州的完全实施问题，但代理商已知，在该州中，代理商的证据不确定，私下证据从国家依赖性分布中获取。随机证据使``完美的欺骗''，代理商报告可以模仿错误状态的证据分布，从而使任何机制都无法区分。这产生了我们的主要结果：在（混合策略）贝叶斯nash平衡中实施的必要和充分的条件，没有完美的欺骗（NPD）。该解决方案需要通过竞争评分规则来启发信念的新技术，以及使用证据结构的内源性``测试分配''。对于信息小型代理（McLean and Postlewaite（2002）），广义状况（GNPD）就足够了。我们的机制适用于两个或多个代理，避免进行整数/模特游戏，并使用有限的责任转移，以消失在平衡中。
We study a full implementation problem with a state unknown to the designer but known to agents, where agents have uncertain evidence privately drawn from state-dependent distributions. Stochastic evidence enables ``perfect deceptions,'' where agents' reports can mimic the evidence distribution of a false state, making differentiation impossible for any mechanism. This yields our main result: a necessary and sufficient condition, No Perfect Deceptions (NPD), for implementation in (mixed-strategy) Bayesian Nash equilibria. The solution requires novel techniques like belief elicitation via competing scoring rules, and an endogenous ``test allocation'' using the evidence structure. For informationally small agents (McLean and Postlewaite (2002)), a generalized condition (GNPD) is sufficient. Our mechanisms work for two or more agents, avoid integer/modulo games, and use limited liability transfers that vanish in equilibrium.
