AI 中文总结
本文在不透明部分承诺下的贝叶斯说服框架中，刻画ρ-可实现性并证明直接遵循实现定理，将鲁棒价值前沿等嵌入该框架，得到高概率均衡且区分统计失败与均衡近似。
AI 中文摘要
作为现有带有不充分信息机制的贝叶斯说服框架的扩展，本文研究当发送者仅以概率ρ受已设定的信息策略约束时的直接推荐，其中约束的实现是隐藏的，且接收者无法观察到持久的结构性环境。接收者首先会看到一个收益中性、不可操纵的校准样本，随后面临全新的、未经过验证的部署交互。通常情况下，校准法则仅能识别面向接收者的简化形式，而非潜在的约束与自由裁量内核。本文刻画了按类型划分的ρ-可实现性，构建了接收者关于完整部署节点的后验分布，并证明了静态直接遵循实现定理。在每一次通过后验预测服从性检验的校准历史之后，部署评估即为一个精确的完美贝叶斯均衡：贝叶斯一致性、接收者序贯理性、发送者序贯理性以及非路径完成均得到验证。在有限类型分离、共同推荐支持以及正的服从裕度条件下，该检验以高概率激活此类均衡。本文的结果将统计失败概率与均衡近似区分开来。最后，我们将原始的鲁棒价值前沿、支持感知线性规划算法以及二元动作分数背包特例嵌入该实现框架。
英文摘要
As an extension of existing Bayesian persuasion framework with inadequate message mechanism, we study direct recommendation when a sender is bound by an installed information policy only with probability $ρ$, the realization of binding is hidden, and the receiver does not observe the persistent structural environment. The receiver first sees a payoff-neutral, nonmanipulable calibration sample and then faces a fresh, non-certified deployment interaction. In common, the calibration law identifies only the receiver-facing reduced form, not the latent binding and discretionary kernels. We characterize type-wise $ρ$-implementability, construct the receiver's posterior over the full deployment node, and prove a static direct-following implementation theorem. After every calibration history that passes a posterior-predictive obedience test, the deployment assessment is an exact perfect Bayesian equilibrium: Bayes consistency, receiver sequential rationality, sender sequential rationality, and off-path completion are all verified. Under finite-type separation, common recommendation support, and a positive obedience margin, the test activates such an equilibrium with high probability. Our results keep statistical failure probability distinct from equilibrium approximation. Finally, we embed the original robust value frontier, support-wise linear-programming algorithm, and binary-action fractional-knapsack specialization into this implementation framework
