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The Department of Defense (DoD) relies heavily on mathematical models and computer simulations to analyze and acquire new weapon systems. Models and simulations help decision makers understand the differences between systems and provide insights into the implications of weapon system tradeoffs. Given this key role, the credibility of simulations is paramount. For combat models, this is gained through the verification, validation, and accreditation process required of DoD analytical models prior to their use in weapon systems acquisition and other studies. The nature of nondeterministic human behavior makes validation of models of human behavior representation contingent on the judgments of subject matter experts that are routinely acquired using a face validation methodology. In an attempt to better understand the strengths and weaknesses of assessing human behavior representation using experts and the face validation methodology, the authors conducted experiments to identify issues critical to utilizing human experts for the purpose of ascertaining ways to enrich the validation process for models relying on human behavior representation. The research was limited to the behaviors of individuals engaged in close combat in an urban environment. This paper presents the study methodology, data analysis, and recommendations for mitigating attendant problems with validation of human behavior representation models.

Prior to 1999, the Introduction to Systems Engineering Design course was taught late in the sequence of courses that the typical student took in the Department of Systems Engineering at the United States Military Academy. The normal progression of courses front‐loaded students with various modeling and analysis “toolkit” courses. Much later in the program students were taught the engineering design big picture. In 1999, the academic program was overhauled. The introductory engineering design course became the first course in the academic program. In addition, the program's courses were separated into “methods” courses that fill the student's toolkit and “formulation” courses that require students to synthesize various tools in formulating and solving small‐scale engineering problems. Finally, summer academic internship experiences were linked with a yearlong capstone design experience during the student's senior year. This paper discusses these changes and highlights advantages of this improved approach to teaching systems engineering.

His research interests include fire behavior of structures and non-structural materials, nondestructive evaluation and K-12 math and science education.