Human decision making in manned space flight including topics on memory models, signal detection, and pilot performance.

Simulations are widely used in the military for training personnel, analyzing proposed equipment, and rehearsing missions, and these simulations need realistic models of human behavior. This book draws together a wide variety of theoretical and applied research in human behavior modeling that can be considered for use in those simulations. It covers behavior at the individual, unit, and command level. At the individual soldier level, the topics covered include attention, learning, memory, decisionmaking, perception, situation awareness, and planning. At the unit level, the focus is on command and control. The book provides short-, medium-, and long-term goals for research and development of more realistic models of human behavior.

If you aren't using the term naturalistic decision making, or NDM, you soon will be. Even as a very young field, NDM has already had far-reaching applications in areas as diverse as management, aviation, health care, nuclear power, military command and control, corporate teamwork, and manufacturing. Put simply, NDM is the way people use their experience to make decisions in the context of a job or task. Of particular interest to NDM researchers are the effects of high-stake consequences, shifting goals, incomplete information, time pressure, uncertainty, and other conditions that are present in most of today's work places and that add to the complexity of decision making. Applications of NDM research findings target decision aids and training that help people in their decision-making processes. This book reports the findings of top NDM researchers, as well as many of their current applications. In addition, the book offers a historical perspective on the emergence of this new paradigm, describes recent theoretical and methodological advancements, and points to future developments. It was written for people interested in decision making research and applications relative to a diverse array of work settings and products such as human-computer interfaces, decision support systems, individual and team training, product designs, and organizational development and planning.

Human decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.