This research note summarizes three years of a four year contract to study ways of improving human performance in highly integrated systems in such areas as communications, transportation, manufacturing, etc. Rule-based computer models of human performance (CAIN) are discussed, as are methods form measuring the complexity of the task of monitoring these large-scale systems. Finally, the development of a computer model (MABEL) which requires subjects to monitor a large-scale communications network is described. Keywords: Man machine systems; CAIN(Contextually Augmented Integrated Network); MABEL(Monitoring Accessing Browsing and Evaluating Limits).

This research note seeks an empirical understanding of the relationship between the physical characteristics of a large-scale system and human performance, and formalizing these empirical results into several measures of large scale complexity. Behavioral computer models of the human operator in a large-scale environment were then constructed, and the models were found consistent with human behavior. This approach showed subtle performance improvement for aided subjects. Keywords: Man-machine, Computer models, Human performance, Communication networks.

This research note seeks an empirical understanding of the relationship between the physical characteristics of a large-scale system and human performance, and formalizing these empirical results into several measures of large scale complexity. Behavioral computer models of the human operator in a large-scale environment were then constructed, and the models were found consistent with human behavior. This approach showed subtle performance improvement for aided subjects. Keywords: Man-machine, Computer models, Human performance, Communication networks.

This volume presents a state-of-the-science review of the most promising current European research -- and its historic roots of research -- on complex problem solving (CPS) in Europe. It is an attempt to close the knowledge gap among American scholars regarding the European approach to understanding CPS. Although most of the American researchers are well aware of the fact that CPS has been a very active research area in Europe for quite some time, they do not know any specifics about even the most important research. Part of the reason for this lack of knowledge is undoubtedly the fact that European researchers -- for the most part -- have been rather reluctant to publish their work in English-language journals. The book concentrates on European research because the basic approach European scholars have taken to studying CPS is very different from one taken by North American researchers. Traditionally, American scholars have been studying CPS in "natural" domains -- physics, reading, writing, and chess playing -- concentrating primarily on exploring novice-expert differences and the acquisition of a complex skill. European scholars, in contrast, have been primarily concerned with problem solving behavior in artificially generated, mostly computerized, complex systems. While the American approach has the advantage of high external validity, the European approach has the advantage of system variables that can be systematically manipulated to reveal the effects of system parameters on CPS behavior. The two approaches are thus best viewed as complementing each other. This volume contains contributions from four European countries -- Sweden, Switzerland, Great Britain, and Germany. As such, it accurately represents the bulk of empirical research on CPS which has been conducted in Europe. An international cooperation started two years ago with the goal of bringing the European research on complex problem solving to the awareness of American scholars. A direct result of that effort, the contributions to this book are both informative and comprehensive.

Controlling Uncertainty: Decision Making and Learning in Complex Worlds reviews and discusses the most current research relating to the ways we can control the uncertain world around us. Features reviews and discussions of the most current research in a number of fields relevant to controlling uncertainty, such as psychology, neuroscience, computer science and engineering Presents a new framework that is designed to integrate a variety of disparate fields of research Represents the first book of its kind to provide a general overview of work related to understanding control