Probabilistic Warnings in National Security Crises: Pearl Harbor Revisited

TL;DR

This paper develops a Bayesian dynamic model to support intelligence analysts in making warning decisions during crises, balancing false alarms and missed detections, illustrated by the Pearl Harbor case study.

Contribution

It introduces a probabilistic, dynamic decision model for warning analysts, incorporating signals, probabilities, and crisis dynamics, with a case study revisiting Pearl Harbor.

Findings

Radio silence contained significant information misinterpreted historically

Bayesian reasoning could have improved decision-making at Pearl Harbor

Model supports balancing false positives and negatives in warning decisions

Abstract

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.