Design and Performance Metrics for Autonomous Human-Machine Teams and Systems (A-HMT-S)

Metrics for the design and operations of a team or organization are likely to use a rational approach organized around Shannon's theory of information. When these metrics are applied, uncertainty is commonly located in methods, algorithms, models or incomplete data. But reality is noisy, incomplete and uncertain. To function in reality, metrics for the design and performance of a team or an organization as presently constituted have to be transformed to assist autonomous systems with making decisions in uncertain environments, especially when the uncertainty is posed by opposing autonomous systems (e.g., conflict, deception, competition). We address this more difficult problem in our research with our theory of the interdependence of complementarity. Consequently, we discuss metrics derived from our prior findings that support separating team structure and performance, and metrics based on our new discovery that vulnerability is a target in an opponent's team or a key concern in one's own team or organization.