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This book comprises a set of stories about being an engineer for many decades and the lessons the author learned from research and practice. These lessons focus on people and organizations, often enabled by technology.

Public-private collaborations are key to the functioning of most essential ecosystems such as security, healthcare, education, and energy. William B. Rouse addresses the challenges of transforming these ecosystems and provides an integrated perspective for understanding and enabling change.

Computer-based simulation has long been used to project the behavior of systems too complex for analytical calculation. Simulation has also been used for many decades to enable human visualization and learning about complex tasks such as aircraft piloting and process plant control, and the versatility and cost-effectiveness of these training simulators are widely recognized

AbstractThe complexity of health cannot be adequately explained with a representation that only captures one level of abstraction of the ecosystem. Multiple levels of abstraction are needed to comprehend the full range of forces that affect the health of a population. These levels are outlined and run the gamut from cells to society, ranging from aberrations of immune system signalling to society's values and norms. Computational modelling of this multilevel system can enable both understanding and managing the complexity of health. This is illustrated in contexts that include Washington, DC, and New York City.

AbstractThis article considers the national challenge of controlling healthcare costs. It is argued that this system‐level challenge is better addressed with an engineering approach rather than from the perspective of medical science that, quite rightly, is tailored to address clinical practice. The engineering approach is outlined and three models are proposed for controlling the costs of healthcare so that the growth of these costs tracks the growth of gross domestic product. These models provide insights into the magnitude of efficiency gains needed to accomplish this goal. Possible ways to achieve these gains are discussed. Copyright © 2009 John Wiley & Sons, Ltd.

Adaptive aiding is a human-machine system design concept that involves using aiding/automation only at those points in time when human performance in a system needs support to meet operational requirements---in the absence of such needs, human performance remains unaided/manual, and thereby humans remain very much "in the loop." This paper describes the evolution and results of an ongoing program of experimental and theoretical research in adaptive aiding. The development and proof of concept are first discussed, followed by consideration of human performance models, on-line assessment methods, and the psychology of human-aid interaction. The implications of these ideas and results are discussed relative to design of intelligent support systems in general and expert systems in particular. A framework for design is presented that includes a structured set of design questions that may be addressed in terms of principles of adaptation and principles of interaction.

Forty-eight first semester trainees in an FAA certificate program participated in an experimental study of trouble-shooting of two different types of graphically displayed networks. The effects of network size, redundancy, feedback, computer aiding, and training were considered. It was found that performance degraded as network size increased, degraded as the level of feedback was reduced, improved with the use of computer aiding, and that skills developed with computer aiding in one task were transferred to the other task.