国家安全危机的可能性：珍珠港再访
Probabilistic Warnings in National Security Crises: Pearl Harbor Revisited
摘要：
想象一下，一群对手正在准备对美国或美国利益发动攻击。一位情报分析师观察到了一些信号，但情况正在迅速变化。分析师面临着这样一个决定：是向主要决策者发出攻击即将发生的警报，还是等到对情况有更多了解时再行动。这种预警决策是基于分析师对信号（独立或相关）的观察和评估，以及她对可能情景及其结果的先验概率的更新。预警决定还取决于分析师对危机动态的评估和对主要决策者偏好的看法，以及作出适当反应所需的准备时间。本文提出了一个模型，以支持该分析师的动态预警决策。与大多数涉及预警的问题一样，关键是要在假阳性和假阴性之间进行权衡，因为这两种类型的情报失败都有可能发生。该模型是通过重温1941年12月袭击珍珠港的情况下说明。它表明，日本舰队的无线电静默携带了相当多的信息（阿瑟·柯南·道尔爵士的“夜间狗”问题），这在当时被误解了。尽管不同攻击的概率相对较低，但其后果是如此，这里描述的贝叶斯动态推理可能为关键决策者提供了有价值的信息。
英文摘要：
Imagine a situation where a group of adversaries is preparing an attack on the United States or U.S. interests. An intelligence analyst has observed some signals, but the situation is rapidly changing. The analyst faces the decision to alert a principal decision maker that an attack is imminent, or to wait until more is known about the situation. This warning decision is based on the analyst's observation and evaluation of signals, independent or correlated, and on her updating of the prior probabilities of possible scenarios and their outcomes. The warning decision also depends on the analyst's assessment of the crisis' dynamics and perception of the preferences of the principal decision maker, as well as the lead time needed for an appropriate response. This article presents a model to support this analyst's dynamic warning decision. As with most problems involving warning, the key is to manage the tradeoffs between false positives and false negatives given the probabilities and the consequences of intelligence failures of both types. The model is illustrated by revisiting the case of the attack on Pearl Harbor in December 1941. It shows that the radio silence of the Japanese fleet carried considerable information (Sir Arthur Conan Doyle's "dog in the night" problem), which was misinterpreted at the time. Even though the probabilities of different attacks were relatively low, their consequences were such that the Bayesian dynamic reasoning described here may have provided valuable information to key decision makers.
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。
