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Army and Joint Transformation initiatives in U.S. national defense (Shinseki, 2000) underscore the need to plan and meet mission requirements for individual soldier and small unit deployment in “close fight” scenarios (e.g. close combat, direct fire, complex terrain). This has focused interest and attention on the need for improved individual human performance research data, models, and high-fidelity simulations that can accurately represent human behavior in individual and small unit settings. New strategies are now needed to bridge the gap between performance outcome assessment and prediction (see also Pew & Mavor, 1998). The purpose of this chapter is to address epistemological and methodological issues that are fundamentally relevant to this goal.

This volume was initiated by the U.S. Army Medical Research and Materiel Command (USAMRMC) Military Operational Medicine Research Directorate in response to a specific need to organize research on behavioral modeling in the Department of Defense. In particular, it was necessary to address relevant issues concerning the metrics, methods, and presuppositions of scientific inquiry in all aspects of human performance research and modeling, with specific focus on individual and small unit performance. The objective of this volume is to bridge the gap between outcome assessment and prediction in military performance literature, and thus to advance the utility and development of individual human performance research, modeling, and simulation.

The history of simulated warfare is nearly as old as warfare itself, dating back at least 5000 years to the Chinese war game known as Wei-Hai. Also the game we now know as chess evolved from a war game originally played in India as early as 500AD (see also Smith, 1998). Throughout military history, the art of warfare has been trained and practiced through the use of artificial tabletop landscapes, miniaturized soldiers, and tactical and strategic gaming rules designed to challenge the minds of military leaders.

Baron et al. briefly summarized the history of human performance modeling¹ (HPM) in their 1990 review. The application of control theory to aircraft simulations and the development of task network models stimulated the development of methods to represent the human contribution to system dynamics. These groundbreaking efforts first identified the manifold difficulties associated with the simulation of human performance in military settings, and many of these difficulties remain matters of contemporary concern. The technical challenges associated with the representation of human performance have endured, and military applications continue to be a major driver of interest in HBR. The expense and various difficulties associated with laboratory research, field studies, and operational tests have pushed modeling and simulation to center stage as an affordable alternative to empirical studies. Simulation is now an essential component of military force development, operational planning, engineering development and acquisition, and training.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

The fields of virtual reality and microworld simulation have advanced significantly in the past decade. Today, computer generated personas or agents that populate these worlds and interact with human operators are now used in many endeavors and avenues of investigation. A few of many example application areas are Hollywood animations for movies, cartoons, and advertising (von-Neuman & Morganstern, 1947); immersive industrial and safety training simulations (Fudenberg & Tirole, 2000; Silverman et al., 2001); distributed, interactive military war games and mission rehearsals (Johns & Silverman, 2001); and personal assistant agents to reduce technologic complexity for the general public, among others (Weaver, Silverman, Shin & Dubois, 2001).

This chapter provides an overview of the Department of Defense (DoD) laboratory structure to help equipment designers, modelers, and manufacturers determine where research, testing programs, or relevant findings can be found. The chapter includes a discussion of the performance measures and metrics typically used in DoD laboratories and concludes by considering the current state-of-the-art as well as the state-of-the-possible for human performance measurement.

A strong, useful theoretical foundation for performance assessment and prediction relies on four components: preliminary observation of a system, identification of key or dominating variables in the system, synthetic and vertical thinking, and successive refinement.