  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.
