Publications

The work reported in this document involves a development effort to provide combat commanders and systems engineers with a capability to explore and optimize system concepts that include operational concepts as part of the design effort. An infrastructure and analytic framework has been designed and partially developed that meets a gap in systems engineering design for combat related complex systems. The system consists of three major components: The first component consists of a design environment that permits the combat commander to perform 'what-if' types of analyses in which parts of a course of action (COA) can be automated by generic system constructs. The second component consists of suites of optimization tools designed to integrate into the analytical architecture to explore the massive design space of an integrated design and operational space. These optimization tools have been selected for their utility in requirements development and operational concept development. The third component involves the design of a modeling paradigm for the complex system that takes advantage of functional definitions and the coupled state space representations, generic measures of effectiveness and performance, and a number of modeling constructs to maximize the efficiency of computer simulations. The system architecture has been developed to allow for a future extension in which the operational concept development aspects can be performed in a co-evolutionary process to ensure the most robust designs may be gleaned from the design space(s).

In exploring the question of how humans reason in ambiguous situations or in the absence of complete information, we stumbled onto a body of knowledge that addresses issues beyond the original scope of our effort. We have begun to understand the importance that philosophy, in particular the work of C. S. Peirce, plays in developing models of human cognition and of information theory in general. We have a foundation that can serve as a basis for further studies in cognition and decision making. Peircean philosophy provides a foundation for understanding human reasoning and capturing behavioral characteristics of decision makers due to cultural, physiological, and psychological effects. The present paper describes this philosophical approach to understanding the underpinnings of human reasoning. We present the work of C. S. Peirce, and define sets of fundamental reasoning behavior that would be captured in the mathematical constructs of these newer technologies and would be able to interact in an agent type framework. Further, we propose the adoption of a hybrid reasoning model based on his work for future computational representations or emulations of human cognition.

This report documents an exploratory FY 00 LDRD project that sought to demonstrate the first steps toward a realistic computational representation of the variability encountered in individual human behavior. Realism, as conceptualized in this project, required that the human representation address the underlying psychological, cultural, physiological, and environmental stressors. The present report outlines the researchers' approach to representing cognitive, cultural, and physiological variability of an individual in an ambiguous situation while faced with a high-consequence decision that would greatly impact subsequent events. The present project was framed around a sensor-shooter scenario as a soldier interacts with an unexpected target (two young Iraqi girls). A software model of the ''Sensor Shooter'' scenario from Desert Storm was developed in which the framework consisted of a computational instantiation of Recognition Primed Decision Making in the context of a Naturalistic Decision Making model [1]. Recognition Primed Decision Making was augmented with an underlying foundation based on our current understanding of human neurophysiology and its relationship to human cognitive processes. While the Gulf War scenario that constitutes the framework for the Sensor Shooter prototype is highly specific, the human decision architecture and the subsequent simulation are applicable to other problems similar in concept, intensity, and degree of uncertainty. The goal was to provide initial steps toward a computational representation of human variability in cultural, cognitive, and physiological state in order to attain a better understanding of the full depth of human decision-making processes in the context of ambiguity, novelty, and heightened arousal.

Abstract not provided.