This book reports our research on detection of change processes that
underlie psychophysical, learning, medical diagnosis, military, and pro-
duction control situations, and share three major features. First, the
states of the process are not directly observable but become gradually
known with the sequential acquisition of fallible information over time.
Second, the mechanism that generates the fallible information is not
stationary; rather, it is subjected to a sudden and irrevocable change.
Thirdly, in- complete, probabilistic information about the time of
change is available when the process commences. The purpose of the book
is to characterize this class of detection of change processes, to
derive the optimal policy that minimizes total expected loss, and, most
importantly, to develop testable response models, based on simple
decision rules, for describing detection of change behavior. The book is
theoretical in the sense that it offers mathematical models of
multi-stage decision behavior and solutions to optimization problems.
However, it is not anti-empirical, as it aims to stimulate new
experimental research and to generate applications. Throughout the book,
questions of experimental verification are briefly considered, and
existing data from two studies are brought to bear on the validity of
the models. The work is not complete; it only provides a starting point
for investigating how people detect a change in an uncertain
environment, balancing between the cost of delay in detecting the change
and the cost of making an incor- rect terminal decision.